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Rahman, Mahmudur. "Data Verifications for Online Social Networks." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2299.
Повний текст джерелаАнотація:
Social networks are popular platforms that simplify user interaction and encourage collaboration. They collect large amounts of media from their users, often reported from mobile devices. The value and impact of social media makes it however an attractive attack target. In this thesis, we focus on the following social media vulnerabilities. First, review centered social networks such as Yelp and Google Play have been shown to be the targets of significant search rank and malware proliferation attacks. Detecting fraudulent behaviors is thus paramount to prevent not only public opinion bias, but also to curb the distribution of malware. Second, the increasing use of mobile visual data in news networks, authentication and banking applications, raises questions of its integrity and credibility. Third, through proof-of- concept implementations, we show that data reported from wearable personal trackers is vulnerable to a wide range of security and privacy attacks, while off-the-shelves security solutions do not port gracefully to the constraints introduced by trackers. In this thesis we propose novel solutions to address these problems. First, we introduce Marco, a system that leverages the wealth of spatial, temporal and network information gleaned from Yelp, to detect venues whose ratings are impacted by fraudulent reviews. Second, we propose FairPlay, a system that correlates review activities, linguistic and behavioral signals gleaned from longitudinal app data, to identify not only search rank fraud but also malware in Google Play, the most popular Android app market. Third, we describe Movee, a motion sensor based video liveness verification system, that analyzes the consistency between the motion inferred from the simultaneously and independently captured camera and inertial sensor streams. Finally, we devise SensCrypt, an efficient and secure data storage and communication protocol for affordable and lightweight personal trackers. We provide the correctness and efficacy of our solutions through a detailed theoretic and experimental analysis.
2
Hong, Dan. "Sharing private data in online social networks /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20HONG.
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Xu, Hailu. "Efficient Spam Detection across Online Social Networks." University of Toledo / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1470416658.
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Li, Jingxuan. "Mining the Online Social Network Data: Influence, Summarization, and Organization." FIU Digital Commons, 2014. http://digitalcommons.fiu.edu/etd/1241.
Повний текст джерелаАнотація:
Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it.
This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign?
The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.
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Fan, Xiaoguang, and 樊晓光. "Study of social-network-based information propagation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B50899600.
Повний текст джерелаАнотація:
Information propagation has attracted increasing attention from sociologists, marketing researchers and Information Technology entrepreneurs. With the rapid developments in online and mobile social applications like Facebook, Twitter, and LinkedIn, large-scale, high-speed and instantaneous information dissemination becomes possible, spawning tremendous opportunities for electronic commerce. It is non-trivial to make an accurate analysis on how information is propagated due to the uncertainty of human behavior and the complexity of the social environment. This dissertation is concerned with exploring models, formulations, and heuristics for the social-network-based information propagation process. It consists of three major parts: information diffusion through online social network, modeling social influence propagation, and social-network-based information spreading in opportunistic mobile networks.
Firstly, I consider the problem of maximizing the influence propagation through online social networks. To solve it, I introduce a probabilistic maximum coverage problem, and propose a cluster-based heuristic and a neighbor-removal heuristic for two basic diffusion models, namely, the Linear Threshold Model and the Independent Cascade Model, respectively. Realizing that the selection of influential nodes is mainly based on the accuracy and efficiency in estimating the social influence, I build a framework of up-to-2-hop hierarchical network to approximate the spreading of social influence, and further propose a hierarchy-based algorithm to solve the influence maximization problem. Our heuristic is proved to be efficient and robust with competitive performance, low computation cost, and high scalability.
The second part explores the modeling on social influence propagation. I develop an analytical model for the influence propagation process based on discrete-time Markov chains, and deduce a close-form equation to express the n-step transition probability matrix. We show that given any initial state the probability distribution of the converged network state could be easily obtained by calculating a matrix product.
Finally, I study the social-network-based information spreading in opportunistic mobile networks by analyzing the opportunistic routing process. I propose three social-network-based communication pattern models and utilize them to evaluate the performance of different social-network-based routing protocols based on several human mobility traces. Moreover, I discuss the fairness evaluation in opportunistic routing, and propose a fair packet forwarding strategy which operates as a plugin for traditional social- network-based routing protocols. My strategy improves the imbalance of success rates among users while maintaining approximately the same system throughput.published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy
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Ahmad, Waqar, and Asim Riaz. "Predicting Friendship Levels in Online Social Networks." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3351.
Повний текст джерелаАнотація:
Abstract Context: Online social networks such as Facebook, Twitter, and MySpace have become the preferred interaction, entertainment and socializing facility on the Internet. However, these social network services also bring privacy issues in more limelight than ever. Several privacy leakage problems are highlighted in the literature with a variety of suggested countermeasures. Most of these measures further add complexity and management overhead for the user. One ignored aspect with the architecture of online social networks is that they do not offer any mechanism to calculate the strength of relationship between individuals. This information is quite useful to identify possible privacy threats. Objectives: In this study, we identify users’ privacy concerns and their satisfaction regarding privacy control measures provided by online social networks. Furthermore, this study explores data mining techniques to predict the levels/intensity of friendship in online social networks. This study also proposes a technique to utilize predicted friendship levels for privacy preservation in a semi-automatic privacy framework. Methods: An online survey is conducted to analyze Facebook users’ concerns as well as their interaction behavior with their good friends. On the basis of survey results, an experiment is performed to justify practical demonstration of data mining phases. Results: We found that users are concerned to save their private data. As a precautionary measure, they restrain to show their private information on Facebook due to privacy leakage fears. Additionally, individuals also perform some actions which they also feel as privacy vulnerability. This study further identifies that the importance of interaction type varies while communication. This research also discovered, “mutual friends” and “profile visits”, the two non-interaction based estimation metrics. Finally, this study also found an excellent performance of J48 and Naïve Bayes algorithms to classify friendship levels. Conclusions: The users are not satisfied with the privacy measures provided by the online social networks. We establish that the online social networks should offer a privacy mechanism which does not require a lot of privacy control effort from the users. This study also concludes that factors such as current status, interaction type need to be considered with the interaction count method in order to improve its performance. Furthermore, data mining classification algorithms are tailor-made for the prediction of friendship levels.
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Pochet, Gilberto Flores. "Analysis of online virtual environments using Data Mining and social networks." reponame:Repositório Institucional da UFABC, 2015.
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8
Berg, Marcus. "Evaluating Quality of Online Behavior Data." Thesis, Stockholms universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-97524.
Повний текст джерелаАнотація:
This thesis has two purposes; emphasizing the importance of data quality of Big Data, and identifying and evaluating potential error sources in JavaScript tracking (a client side on - site online behavior clickstream data collection method commonly used in web analytics). The importance of data quality of Big Data is emphasized through the evaluation of JavaScript tracking. The Total Survey Error framework is applied to JavaScript tracking and 17 nonsampling error sources are identified and evaluated. The bias imposed by these error sources varies from large to small, but the major takeaway is the large number of error sources actually identified. More work is needed. Big Data has much to gain from quality work. Similarly, there is much that can be done with statistics in web analytics.
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Sang, Lin. "Social Big Data and Privacy Awareness." Thesis, Uppsala universitet, Institutionen för informatik och media, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242444.
Повний текст джерелаАнотація:
Based on the rapid development of Big Data, the data from the online social network becomea major part of it. Big data make the social networks became data-oriented rather than social-oriented. Taking this into account, this dissertation presents a qualitative study to research howdoes the data-oriented social network affect its users’ privacy management for nowadays. Within this dissertation, an overview of Big Data and privacy issues on the social network waspresented as a background study. We adapted the communication privacy theory as a frameworkfor further analysis how individuals manage their privacy on social networks. We study socialnetworks as an entirety in this dissertation. We selected Facebook as a case study to present theconnection between social network, Big Data and privacy issues. The data that supported the result of this dissertation collected by the face-to-face and in-depthinterview study. As consequence, we found that the people divided the social networks intodifferent level of openness in order to avoid the privacy invasions and violations, according totheir privacy concern. They reduced and transferred their sharing from an open social networkto a more close one. However, the risk of privacy problems actually raised because peopleneglected to understand the data process on social networks. They focused on managed theeveryday sharing but too easily allowed other application accessed their personal data on thesocial network (such like the Facebook profile).
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Manalo, Cornejo Darryl, and Ali Sabet. "Online Social Lookup: A Study of a Future Employment Tool." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186402.
Повний текст джерелаАнотація:
From the days of Antonio Meucci and Alexander Graham Bell when the telephone was invented, people have been looking for ways to communicate with each other through the use of technology. With the introduction of the Internet new possibilities were created to communicate with other persons around the world. As more and more individuals got access to the Internet more and more data got sent through the network. As with any platform where number of individuals keeps growing, business is to be made. Different companies offer different types of services, and many of these companies are niched in services where people in some way interact with each other. Social communities are some of the largest websites that are used and on these websites a lot of data is being shared. Pictures, life stories and interests are shared and this data is something that advertisement companies pay money to obtain. We can see this since a large part of revenues from social communities come from ad companies. Since these websites save data about individuals one could collect this data to sum up how an individual act online. In this thesis, we wanted to see if there was any interest in collecting this type of data to develop a business model where the data of individuals would be sold to a third party, and how individuals feel about this type data collection. To get an understanding of what type of data that can be collected, different companies that gather data were looked into. To get an understanding if this business model is something wanted recruiters of different IT companies were contacted for interviews. Focus groups and surveys were used to see how individuals close to their college graduation feel about data collection and the business model that is under construction. From the data collected, we saw that recruiters were not interested in a business model that gathered personal data, but rather professional data such as education, projects and other job related facts that can prove what the job applicant have done. And from the public we got the response that data collection of this sort is not something that is desirable. With the data collected we saw that our business model did not fit our targeted audience, but rather that a modified business model aimed on professional data rather than social data is something that could be developed.Ända sedan den dagen telefonen skapades av Antonio Meucci och Alexander Graham Bell har människan letat efter nya sätt att kommunicera med varandra via teknologin som finns idag. Internet har introducerade nya sätt att dela olika typer av data världen över. Varje dag får fler och fler människor tillgång till internet det betyder då också att mer data skickas via nätet. Som med alla plattform där antalet individer växer skapas då nya affärsmodeller. Olika företag erbjuder olika typer av tjänster och många av dessa företag fördjupar sig inom kommunikationssektorn så att människor kan integrera med varandra. Socialmedia är bland de populäraste webbsidorna idag och här kan användarna dela data och information med varandra. Dessa data är viktiga för annonseringsföretagen då de vill rikta rätt reklam till användarna. Detta ser vi nu eftersom sociala mediernas största inkomstkälla kommer ifrån säljandet av data till annonsering bolagen. Man skulle kunna ta all data som dessa företag har sparat på sina användare för att sammanställa hur de använder tjänsten. I vår rapport ville vi se om det dann något intresse för att samla in denna typ av data för att utveckla vår affärsmodell där individens data och information säljes till en tredje part. Vi ville även undersöka hur användaren känner när det gäller datasamling på internet. För att få en uppfattning på vad för data som kan samlas in på internet har vi undersökt två företag för att se vad för data de tar. När det gäller vår affärsmodell har vi kontaktat och intervjuat rekryterare från olika företag för att se om vår affärsmodell är något som de behöver. Focused Groups och enkäter skickades ut till studenter som nästan har sin examen för att höra vad de har för åsikt är gällande datainsamling och vår affärsmodell. Vår undersökning visade att datainsamling inte var eftertraktad, men de ville däremot samla kompetens information istället. Information så som utbildning, projekt och arbetskarriär. Enkäten och Focused Groups visade även där att personlig datainsamling inte var något som de ville ha. Med de data vi fått under vår undersökning tydde det på att vår affärside inte var riktad mot rätt målgrupp, men en justering av vår affärsmodell i form av datainsamling av kompetens information var något de ville ha.
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Hassanzadeh, Reza. "Anomaly detection in online social networks : using data-mining techniques and fuzzy logic." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/78679/1/Reza_Hassanzadeh_Thesis.pdf.
Повний текст джерелаАнотація:
This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.
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Alim, Sophia. "Vulnerability in online social network profiles : a framework for measuring consequences of information disclosure in online social networks." Thesis, University of Bradford, 2011. http://hdl.handle.net/10454/5507.
Повний текст джерелаАнотація:
The increase in online social network (OSN) usage has led to personal details known as attributes being readily displayed in OSN profiles. This can lead to the profile owners being vulnerable to privacy and social engineering attacks which include identity theft, stalking and re identification by linking. Due to a need to address privacy in OSNs, this thesis presents a framework to quantify the vulnerability of a user's OSN profile. Vulnerability is defined as the likelihood that the personal details displayed on an OSN profile will spread due to the actions of the profile owner and their friends in regards to information disclosure. The vulnerability measure consists of three components. The individual vulnerability is calculated by allocating weights to profile attribute values disclosed and neighbourhood features which may contribute towards the personal vulnerability of the profile user. The relative vulnerability is the collective vulnerability of the profiles' friends. The absolute vulnerability is the overall profile vulnerability which considers the individual and relative vulnerabilities. The first part of the framework details a data retrieval approach to extract MySpace profile data to test the vulnerability algorithm using real cases. The profile structure presented significant extraction problems because of the dynamic nature of the OSN. Issues of the usability of a standard dataset including ethical concerns are discussed. Application of the vulnerability measure on extracted data emphasised how so called 'private profiles' are not immune to vulnerability issues. This is because some profile details can still be displayed on private profiles. The second part of the framework presents the normalisation of the measure, in the context of a formal approach which includes the development of axioms and validation of the measure but with a larger dataset of profiles. The axioms highlight that changes in the presented list of profile attributes, and the attributes' weights in making the profile vulnerable, affect the individual vulnerability of a profile. iii Validation of the measure showed that vulnerability involving OSN profiles does occur and this provides a good basis for other researchers to build on the measure further. The novelty of this vulnerability measure is that it takes into account not just the attributes presented on each individual profile but features of the profiles' neighbourhood.
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Lim, Chong-U. "Modeling player self-representation in multiplayer online games using social network data." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82409.
Повний текст джерелаАнотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 101-105).
Game players express values related to self-expression through various means such as avatar customization, gameplay style, and interactions with other players. Multiplayer online games are now often integrated with social networks that provide social contexts in which player-to-player interactions take place, such as conversation and trading of virtual items. Building upon a theoretical framework based in machine learning and cognitive science, I present results from a novel approach to modeling and analyzing player values in terms of both preferences in avatar customization and patterns in social network use. To facilitate this work, I developed the Steam-Player- Preference Analyzer (Steam-PPA) system, which performs advanced data collection on publicly available social networking profile information. The primary contribution of this thesis is the AIR Toolkit Status Performance Classifier (AIR-SPC), which uses machine learning techniques including k-means clustering, natural language processing (NLP), and support vector machines (SVM) to perform inference on the data. As an initial case study, I use Steam-PPA to collect gameplay and avatar customization information from players in the popular, and commercially successful, multi-player first-person-shooter game Team Fortress 2 (TF2). Next, I use AIR-SPC to analyze the information from profiles on the social network Steam. The upshot is that I use social networking information to predict the likelihood of players customizing their profile in several ways associated with the monetary values of their avatars. In this manner I have developed a computational model of aspects of players' digital social identity capable of predicting specific values in terms of preferences exhibited within a virtual game-world.
by Chong-U Lim.
S.M.
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Leung, Kwan Wai. "Commentary-based social media clustering with concept and social network discovery." HKBU Institutional Repository, 2011. https://repository.hkbu.edu.hk/etd_ra/1303.
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Yeratziotis, Alexandros. "A framework to evaluate usable security in online social networking." Thesis, Nelson Mandela Metropolitan University, 2011. http://hdl.handle.net/10948/d1012933.
Повний текст джерелаАнотація:
It is commonly held in the literature that users find security and privacy difficult to comprehend. It is also acknowledged that most end-user applications and websites have built-in security and privacy features. Users are expected to interact with these in order to protect their personal information. However, security is generally a secondary goal for users. Considering the complexity associated with security in combination with the notion that it is not users’ primary task, it makes sense that users tend to ignore their security responsibilities. As a result, they make poor security-related decisions and, consequently, their personal information is at risk. Usable Security is the field that investigates these types of issue, focusing on the design of security and privacy features that are usable. In order to understand and appreciate the complexities that exist in the field of Usable Security, the research fields of Human-Computer Interaction and Information Security should be examined. Accordingly, the Information Security field is concerned with all aspects pertaining to the security and privacy of information, while the field of Human-Computer Interaction is concerned with the design, evaluation and implementation of interactive computing systems for human use. This research delivers a framework to evaluate Usable Security in online social networks. In this study, online social networks that are particular to the health domain were used as a case study and contributed to the development of a framework consisting of three components: a process, a validation tool and a Usable Security heuristic evaluation. There is no existing qualitative process that describes how one would develop and validate a heuristic evaluation. In this regard a heuristic evaluation is a usability inspection method that is used to evaluate the design of an interface for any usability violations in the field of Human-Computer Interaction. Therefore, firstly, a new process and a validation tool were required to be developed. Once this had been achieved, the process could then be followed to develop a new heuristic evaluation that is specific to Usable Security. In order to assess the validity of a new heuristic evaluation a validation tool is used. The development of tools that can improve the design of security and privacy features on end-user applications and websites in terms of their usability is critical, as this will ensure that the intended users experience them as usable and can utilise them effectively. The framework for evaluating Usable Security contributes to this objective in the context of online social networks.
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Ip, Lai Cheng. "Mining on social network community for marketing." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950661.
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Rezayidemne, Seyedsaed. "Characterizing Online Social Media: Topic Inference and Information Propagation." Thesis, University of Oregon, 2018. http://hdl.handle.net/1794/23904.
Повний текст джерелаАнотація:
Word-of-mouth (WOM) communication is a well studied phenomenon in the literature and content propagation in Online Social Networks (OSNs) is one of the forms of WOM mechanism that have been prevalent in recent years specially with the widespread surge of online communities and online social networks. The basic piece of information in most OSNs is a post (e.g., a tweet in Twitter or a post in Facebook). A post can contain different types of content such as text, photo, video, etc, or a mixture of two or more them. There are also various ways to enrich the text by mentioning other users, using hashtags, and adding URLs to external contents. The goal of this study is to investigate what factors contribute into the propagation of messages in Google+. To answer to this question a multidimensional study will be conducted. On one hand this question could be viewed as a natural language processing problem where topic or sentiment of posts cause message dissemination. On the other hand the propagation can be effect of graph properties i.e., popularity of message originators (node degree) or activities of communities. Other aspects of this problem are time, external contents, and external events. All of these factors are studied carefully to find the most highly correlated attribute(s) in the propagation of posts.
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Deirmenci, Hazim. "Enabling Content Discovery in an IPTV System : Using Data from Online Social Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200922.
Повний текст джерелаАнотація:
Internet Protocol television (IPTV) is a way of delivering television over the Internet, which enables two-way communication between an operator and its users. By using IPTV, users have freedom to choose what content they want to consume and when they want to consume it. For example, users are able to watch TV shows after they have been aired on TV, and they can access content that is not part of any linear TV broadcasts, e.g. movies that are available to rent. This means that, by using IPTV, users can get access to more video content than is possible with the traditional TV distribution formats. However, having more options also means that deciding what to watch becomes more difficult, and it is important that IPTV providers facilitate the process of finding interesting content so that the users find value in using their services. In this thesis, the author investigated how a user’s online social network can be used as a basis for facilitating the discovery of interesting movies in an IPTV environment. The study consisted of two parts, a theoretical and a practical. In the theoretical part, a literature study was carried out in order to obtain knowledge about different recommender system strategies. In addition to the literature study, a number of online social network platforms were identified and empirically studied in order to gain knowledge about what data is possible to gather from them, and how the data can be gathered. In the practical part, a prototype content discovery system, which made use of the gathered data, was designed and built. This was done in order to uncover difficulties that exist with implementing such a system. The study shows that, while it is is possible to gather data from different online social networks, not all of them offer data in a form that is easy to make use of in a content discovery system. Out of the investigated online social networks, Facebook was found to offer data that is the easiest to gather and make use of. The biggest obstacle, from a technical point of view, was found to be the matching of movie titles gathered from the online social network with the movie titles in the database of the IPTV service provider; one reason for this is that movies can have titles in different languages.Internet Protocol television (IPTV) är ett sätt att leverera tv via Internet, vilket möjliggör tvåvägskommunikation mellan en operatör och dess användare. Genom att använda IPTV har användare friheten att välja vilket innehåll de vill konsumera och när de vill konsumera det. Användare har t.ex. möjlighet att titta på tv program efter att de har sänts på tv, och de kan komma åt innehåll som inte är en del av någon linjär tv-sändning, t.ex. filmer som är tillgängliga att hyra. Detta betyder att användare, genom att använda IPTV, kan få tillgång till mer videoinnhåll än vad som är möjligt med traditionella tv-distributionsformat. Att ha fler valmöjligheter innebär dock även att det blir svårare att bestämma sig för vad man ska titta på, och det är viktigt att IPTV-leverantörer underlättar processen att hitta intressant innehåll så att användarna finner värde i att använda deras tjänster. I detta exjobb undersökte författaren hur en användares sociala nätverk på Internet kan användas som grund för att underlätta upptäckandet av intressanta filmer i en IPTV miljö. Undersökningen bestod av två delar, en teoretisk och en praktisk. I den teoretiska delen genomfördes en litteraturstudie för att få kunskap om olika rekommendationssystemsstrategier. Utöver litteraturstudien identifierades ett antal sociala nätverk på Internet som studerades empiriskt för att få kunskap om vilken data som är möjlig att hämta in från dem och hur datan kan inhämtas. I den praktiska delen utformades och byggdes en prototyp av ett s.k. content discovery system (“system för att upptäcka innehåll”), som använde sig av den insamlade datan. Detta gjordes för att exponera svårigheter som finns med att implementera ett sådant system. Studien visar att, även om det är möjligt att samla in data från olika sociala nätverk på Internet så erbjuder inte alla data i en form som är lätt att använda i ett content discovery system. Av de undersökta sociala nätverkstjänsterna visade det sig att Facebook erbjuder data som är lättast att samla in och använda. Det största hindret, ur ett tekniskt perspektiv, visade sig vara matchningen av filmtitlar som inhämtats från den sociala nätverkstjänsten med filmtitlarna i IPTV-leverantörens databas; en anledning till detta är att filmer kan ha titlar på olika språk.
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Sheng, Jie. "Managing big data from the crowd : strategic firm engagement with online social interactions." Thesis, University of Bristol, 2018. http://hdl.handle.net/1983/0fffae64-51e3-4da2-83f6-e5fd7d4b1bcc.
Повний текст джерелаАнотація:
In today’s digital economy, information sharing has become common practice and significantly influences individuals’ behaviours and preferences. The interactive and participative environment fosters customer engagement in voicing and communicating in the virtual network. The sheer amount of user-generated content from online social interactions offers intriguing opportunities for businesses to develop sustainable competitive advantages; yet, how firms can create value by managing and capitalising on crowd voices remains an under-explored facet of big data research. This thesis discusses strategies for firms to engage in the online social interaction network so as to improve performance and achieve competitive advantages. The developed holistic framework for strategic firm engagement articulates three distinct but non-mutually exclusive roles of firms in the online communication network: observer, participant and strategic leader. Correspondingly, three studies are designed to examine business impacts of these firm engagement roles using a large-scale data set of over 800,000 online customer reviews and over 360,000 online managerial responses of London hotels. The first study investigates the observer role and validates an analytical strategy for mining customers’ textual reviews and exploiting the discovered knowledge to improve service quality. The second study considers the participant role and explores how firms respond to customer reviews and the efficacy of different response styles in future rating improvement. The third study examines the strategic leader role by testing the effects of firms being present and active online in stimulating customer engagement behaviour. Findings from the empirical studies demonstrate the strategic value of firm engagement in the online social interaction network. This thesis contributes to big data research, strategy and marketing literature in terms of strategising big data from the crowd by developing data-driven strategies. It also offers practical insights into strategic planning for businesses engaging in online social interactions.
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Wang, Hao, and Yun Xie. "The use of Online Social Networks in Chinese Collaborative E-learning Education." Thesis, Örebro universitet, Handelshögskolan vid Örebro universitet, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-16037.
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Abdulrahman, Ruqayya. "Multi agent system for web database processing, on data extraction from online social networks." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5502.
Повний текст джерелаАнотація:
In recent years, there has been a flood of continuously changing information from a variety of web resources such as web databases, web sites, web services and programs. Online Social Networks (OSNs) represent such a field where huge amounts of information are being posted online over time. Due to the nature of OSNs, which offer a productive source for qualitative and quantitative personal information, researchers from various disciplines contribute to developing methods for extracting data from OSNs. However, there is limited research which addresses extracting data automatically. To the best of the author's knowledge, there is no research which focuses on tracking the real time changes of information retrieved from OSN profiles over time and this motivated the present work. This thesis presents different approaches for automated Data Extraction (DE) from OSN: crawler, parser, Multi Agent System (MAS) and Application Programming Interface (API). Initially, a parser was implemented as a centralized system to traverse the OSN graph and extract the profile's attributes and list of friends from Myspace, the top OSN at that time, by parsing the Myspace profiles and extracting the relevant tokens from the parsed HTML source files. A Breadth First Search (BFS) algorithm was used to travel across the generated OSN friendship graph in order to select the next profile for parsing. The approach was implemented and tested on two types of friends: top friends and all friends. In case of top friends, 500 seed profiles have been visited; 298 public profiles were parsed to get 2197 top friends' profiles and 2747 friendship edges, while in case of all friends, 250 public profiles have been parsed to extract 10,196 friends' profiles and 17,223 friendship edges. This approach has two main limitations. The system is designed as a centralized system that controlled and retrieved information of each user's profile just once. This means that the extraction process will stop if the system fails to process one of the profiles; either the seed profile (first profile to be crawled) or its friends. To overcome this problem, an Online Social Network Retrieval System (OSNRS) is proposed to decentralize the DE process from OSN through using MAS. The novelty of OSNRS is its ability to monitor profiles continuously over time. The second challenge is that the parser had to be modified to cope with changes in the profiles' structure. To overcome this problem, the proposed OSNRS is improved through use of an API tool to enable OSNRS agents to obtain the required fields of an OSN profile despite modifications in the representation of the profile's source web pages. The experimental work shows that using API and MAS simplifies and speeds up the process of tracking a profile's history. It also helps security personnel, parents, guardians, social workers and marketers in understanding the dynamic behaviour of OSN users. This thesis proposes solutions for web database processing on data extraction from OSNs by the use of parser and MAS and discusses the limitations and improvements.
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Van, der Westhuizen Eldridge Welner. "A framework for personal health records in online social networking." Thesis, Nelson Mandela Metropolitan University, 2012. http://hdl.handle.net/10948/d1012382.
Повний текст джерелаАнотація:
Since the early 20th century, the view has developed that high quality health care can be delivered only when all the pertinent data about the health of a patient is available to the clinician. Various types of health records have emerged to serve the needs of healthcare providers and more recently, patients or consumers. These health records include, but are not limited to, Personal Health Records, Electronic Heath Records, Electronic Medical Records and Payer-Based Health Records. Payer-Based Health Records emerged to serve the needs of medical aids or health care plans. Electronic Medical Records and Electronic Health Records were targeted at the healthcare provider market, whereas a gap developed in the patient market. Personal Health Records were developed to address the patient market, but adoption was slow at first. The success of online social networking reignited the flame that Personal Health Records needed and online consumer-based Personal Health Records were developed. Despite all the various types of health records, there still seems to be a lack of meaningful use of personal health records in modern society. The purpose of this dissertation is to propose a framework for Personal Health Records in online social networking, to address the issue of a lack of a central, accessible repository for health records. In order for a Personal Health Record to serve this need it has to be of meaningful use. The capability of a PHR to be of meaningful use is core to this research. In order to determine whether a Personal Health Record is of meaningful use, a tool is developed to evaluate Personal Health Records. This evaluation tool takes into account all the attributes that a Personal Health Record which is of meaningful use should comprise of. Suitable ratings are allocated to enable measuring of each attribute. A model is compiled to facilitate the selection of six Personal Health Records to be evaluated. One of these six Personal Health Records acts as a pilot site to test the evaluation tool in order to determine the tool’s utility and effect improvements. The other five Personal Health Records are then evaluated to measure their adherence to the attributes of meaningful use. These findings, together with a literature study on the various types of health records and the evaluation tool, inform the building blocks used to present the framework. It is hoped that the framework for Personal Health Records in online social networking proposed in this research, may be of benefit to provide clear guidance for the achievement of a central or integrated, accessible repository for health records through the meaningful use of Personal Health Records.
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Grabowicz, Przemyslaw Adam. "Complex networks approach to modeling online social systems. The emergence of computational social science." Doctoral thesis, Universitat de les Illes Balears, 2014. http://hdl.handle.net/10803/131220.
Повний текст джерелаАнотація:
This thesis is devoted to quantitative description, analysis, and modeling of complex social systems in the form of online social networks. Statistical patterns of the systems under study are unveiled and interpreted using concepts and methods of network science, social network analysis, and data mining. A long-term promise of this research is that predicting the behavior of complex techno-social systems will be possible in a way similar to contemporary weather forecasting, using statistical inference and computational modeling based on the advancements in understanding and knowledge of techno-social systems. Although the subject of this study are humans, as opposed to atoms or molecules in statistical physics, the availability of extremely large datasets on human behavior permits the use of tools and techniques of statistical physics. This dissertation deals with large datasets from online social networks, measures statistical patterns of social behavior, and develops quantitative methods, models, and metrics for complex techno-social systems.La presente tesis está dedicada a la descripción, análisis y modelado cuantitativo de sistemas complejos sociales en forma de redes sociales en internet. Mediante el uso de métodos y conceptos provenientes de ciencia de redes, análisis de redes sociales y minería de datos se descubren diferentes patrones estadísticos de los sistemas estudiados. Uno de los objetivos a largo plazo de esta línea de investigación consiste en hacer posible la predicción del comportamiento de sistemas complejos tecnológico-sociales, de un modo similar a la predicción meteorológica, usando inferencia estadística y modelado computacional basado en avances en el conocimiento de los sistemas tecnológico-sociales. A pesar de que el objeto del presente estudio son seres humanos, en lugar de los átomos o moléculas estudiados tradicionalmente en la física estadística, la disponibilidad de grandes bases de datos sobre comportamiento humano hace posible el uso de técnicas y métodos de física estadística. En el presente trabajo se utilizan grandes bases de datos provenientes de redes sociales en internet, se miden patrones estadísticos de comportamiento social, y se desarrollan métodos cuantitativos, modelos y métricas para el estudio de sistemas complejos tecnológico-sociales.
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Han, Xiao. "Mining user similarity in online social networks : analysis,modeling and applications." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0013/document.
Повний текст джерелаАнотація:
Réseaux sociaux (RS) (par exemple, Facebook, Twitter et LinkedIn) ont gagné en popularité écrasante et accumulé des données numériques massives sur la société humaine. Ces données massives, représentant de l’information personnelle et sociale des individus, nous offrent des possibilités sans précédent pour étudier, analyser et modéliser la structure de réseau complexe, les relations humaines, les gens similitude, etc. Pendant ce temps, les RS ont déclenché un grand nombre d’applications et de services qui rentables chercher à maintenir des liens de vibrer et l’expérience des utilisateurs d’avance. Dans ce contexte, comment concevoir ces applications et les services, en particulier comment extraire et d’exploiter des fonctionnalités sociales efficaces à partir des données massives disponibles pour améliorer les applications et les services, a reçu beaucoup d’attention. Cette thèse, visant à améliorer les applications et les services sociaux, étudie trois questions essentielles et pratiques RS: (1) Comment pouvons-nous explorer les amis potentiels pour un utilisateur d’établir et d’élargir ses liens sociaux? (2) comment pouvons-nous découvrir un contenu intéressant pour un utilisateur pour satisfaire ses goûts personnels? (3) comment pouvons-nous informer un utilisateur du risque d’exposition de son information privée pour préserver sa vie privée? S’appuyant sur les idées sur la similarité de personnes dans les sciences sociales, cette thèse étudie les effets et les applications de l’utilisateur similitude dans les RS pour résoudre les problèmes mentionnés ci-dessus. Plus précisément, les sociologues suggèrent que la similitude engendre connexion et induit principe homophilie que les gens similaires (par exemple, même âge, l’éducation ou la profession) sont plus susceptibles de communiquer, de confiance et de partager l’information avec l’autre que ceux dissemblables. Inspiré par ces résultats, cette thèse étudie le principe de similitude répandue dans RS en termes de savoir si les utilisateurs similaires seraient proches dans leurs relations sociales, similaire dans leurs intérêts, ou approximative dans leur géo distance, en se appuyant sur 500K profils d’utilisateurs recueillies auprès de Facebook; il explore en outre des solutions pour exploiter efficacement le principe de similitude observée pour concevoir les quatre applications et des services sociaux suivantes: • Effets de Similarité de L’utilisateur sur Lien Prévision pour les Nouveaux Utilisateurs : nous analysons la prédiction de liaison pour les nouveaux utilisateurs qui n’ont pas créé de lien. Basé sur l’information limitée obtenu lors de votre inscription la procédure de nouveaux utilisateurs, ainsi que les attributs et les liens des utilisateurs existants dans un RS, nous étudions la façon dont beaucoup de similitude entre deux utilisateurs affecterait la probabilité qu’ils se lient d’amitié. En conséquence, nous proposons un modèle de prédiction de liaison efficace pour les nouveaux utilisateurs. • Similarité Minière de L’utilisateur pour la Découverte de Contenu en Réseaux P2P Sociale : nous examinons comment similarité et connaissances des participants dans RS pourraient bénéficier leur découverte de contenu dans les réseaux P2P. Nous construisons un modèle de réseau P2P sociale où chaque pair attribue plus de poids à ses amis dans RS qui ont similarité supérieur et plus de connaissances. Utilisation de marche aléatoire avec la méthode de redémarrage, nous présentons un nouveau contenu algorithme de découverte le dessus du modèle de réseau P2P sociale proposé. • Inspection intérêt similarité - Prédiction et Application : nous présentons des études empiriques détaillées sur les intérêts similitude et de révéler que les gens sont susceptibles de présenter des goûts similaires s’ils ont des informations démographiques similaires (par exemple, âge, lieu), ou s’elles sont amis. Par conséquent, étant donné un nouvel utilisateur dont les intérêts (...)Online Social Networks (OSNs) (e.g., Facebook, Twitter and LinkedIn) have gained overwhelming popularity and accumulated massive digital data about human society. These massive data, representing individuals' personal and social information, provide us with unprecedented opportunities to study, analyze and model the complex network structure, human connections, people similarity, etc. Meanwhile, OSNs have triggered a large number of profitable applications and services which seek to maintain vibrate connections and advance users' experience. In this context, how to devise such applications and services, especially how to extract and exploit effective social features from the massive available data to enhance the applications and services, has received much attention. This dissertation, aiming to enhance the social applications and services, investigates three critical and practical issues in OSNs: (1) How can we explore potential friends for a user to establish and enlarge her social connections? (2) How can we discover interesting content for a user to satisfy her personal tastes? (3) How can we inform a user the exposure risk of her private information to preserve her privacy? Drawing on the insights about people's similarity in social science, this dissertation studies the widespread similarity principle in OSN in terms of whether similar users would be close in their social relationships, similar in their interests, or approximate in their geo-distance, relying on 500K user profiles collected from Facebook; it further explores solutions to effectively leverage the observed similarity principle to address the aforementioned practical issues
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Washha, Mahdi. "Information quality in online social media and big data collection : an example of Twitter spam detection." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30080/document.
Повний текст джерелаАнотація:
La popularité des médias sociaux en ligne (Online Social Media - OSM) est fortement liée à la qualité du contenu généré par l'utilisateur (User Generated Content - UGC) et la protection de la vie privée des utilisateurs. En se basant sur la définition de la qualité de l'information, comme son aptitude à être exploitée, la facilité d'utilisation des OSM soulève de nombreux problèmes en termes de la qualité de l'information ce qui impacte les performances des applications exploitant ces OSM. Ces problèmes sont causés par des individus mal intentionnés (nommés spammeurs) qui utilisent les OSM pour disséminer des fausses informations et/ou des informations indésirables telles que les contenus commerciaux illégaux. La propagation et la diffusion de telle information, dit spam, entraînent d'énormes problèmes affectant la qualité de services proposés par les OSM. La majorité des OSM (comme Facebook, Twitter, etc.) sont quotidiennement attaquées par un énorme nombre d'utilisateurs mal intentionnés. Cependant, les techniques de filtrage adoptées par les OSM se sont avérées inefficaces dans le traitement de ce type d'information bruitée, nécessitant plusieurs semaines ou voir plusieurs mois pour filtrer l'information spam. En effet, plusieurs défis doivent être surmontées pour réaliser une méthode de filtrage de l'information bruitée . Les défis majeurs sous-jacents à cette problématique peuvent être résumés par : (i) données de masse ; (ii) vie privée et sécurité ; (iii) hétérogénéité des structures dans les réseaux sociaux ; (iv) diversité des formats du UGC ; (v) subjectivité et objectivité. Notre travail s'inscrit dans le cadre de l'amélioration de la qualité des contenus en termes de messages partagés (contenu spam) et de profils des utilisateurs (spammeurs) sur les OSM en abordant en détail les défis susmentionnés. Comme le spam social est le problème le plus récurant qui apparaît sur les OSM, nous proposons deux approches génériques pour détecter et filtrer le contenu spam : i) La première approche consiste à détecter le contenu spam (par exemple, les tweets spam) dans un flux en temps réel. ii) La seconde approche est dédiée au traitement d'un grand volume des données relatives aux profils utilisateurs des spammeurs (par exemple, les comptes Twitter)The popularity of OSM is mainly conditioned by the integrity and the quality of UGC as well as the protection of users' privacy. Based on the definition of information quality as fitness for use, the high usability and accessibility of OSM have exposed many information quality (IQ) problems which consequently decrease the performance of OSM dependent applications. Such problems are caused by ill-intentioned individuals who misuse OSM services to spread different kinds of noisy information, including fake information, illegal commercial content, drug sales, mal- ware downloads, and phishing links. The propagation and spreading of noisy information cause enormous drawbacks related to resources consumptions, decreasing quality of service of OSM-based applications, and spending human efforts. The majority of popular social networks (e.g., Facebook, Twitter, etc) over the Web 2.0 is daily attacked by an enormous number of ill-intentioned users. However, those popular social networks are ineffective in handling the noisy information, requiring several weeks or months to detect them. Moreover, different challenges stand in front of building a complete OSM-based noisy information filtering methods that can overcome the shortcomings of OSM information filters. These challenges are summarized in: (i) big data; (ii) privacy and security; (iii) structure heterogeneity; (iv) UGC format diversity; (v) subjectivity and objectivity; (vi) and service limitations In this thesis, we focus on increasing the quality of social UGC that are published and publicly accessible in forms of posts and profiles over OSNs through addressing in-depth the stated serious challenges. As the social spam is the most common IQ problem appearing over the OSM, we introduce a design of two generic approaches for detecting and filtering out the spam content. The first approach is for detecting the spam posts (e.g., spam tweets) in a real-time stream, while the other approach is dedicated for handling a big data collection of social profiles (e.g., Twitter accounts)
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Hillhouse, Linden, and Ginette Blackhart. "Data Quality: Does Time of Semester Matter?" Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/asrf/2019/schedule/84.
Повний текст джерелаАнотація:
When conducting scientific research, obtaining high-quality data is important. When collecting data from a college student participant pool, however, factors such as the time of the semester in which data are collected could cause validity issues, especially if the survey is completed in an online, non-laboratory setting. Near the end of the semester, students may experience more time pressures and constraints than at other times in the semester. These additional pressures may encourage participants to multi-task while completing the study, or to rush through the survey in order to receive credits as quickly as possible. The hypothesis of this study was that responses collected at the end of the semester would exhibit lower data quality than responses collected at the beginning of the semester. Data were collected online during the last two weeks of the fall 2018 semester (n = 312) and the first two weeks of the spring 2019 semester (n = 55). Participants were asked to write about an embarrassing situation and then completed a number of questionnaires assessing their thoughts and feelings about the event, personality traits, and participant engagement. Data quality was assessed using several different previously validated methods, including time spent on survey; the number of missed items; the number of incorrect embedded attention-check items (out of 12); the length of responses on two open-ended questions; self-reported diligence, interest, effort, attention, and whether their data should be used; and Cronbach’s alphas on the scales. Results showed that between the two groups, there were significant differences on length of open-ended responses, self-reported diligence, self-reported interest, effort, attention, neuroticism, and conscientiousness. Participants completing the study in the first two weeks of the spring 2019 semester had significantly longer open-ended responses and significantly higher levels of self-reported diligence, self-reported interest, effort, attention, neuroticism, and conscientiousness. Although there was not a significant difference in number of incorrect attention-check items between the two groups, it should be noted that only 46% of the total participants did not miss any check items. These results lend support to the hypothesis that data collected at the end of the semester may be of lower quality than data collected at the beginning of the semester. However, because the groups significantly differed on neuroticism and conscientiousness, we cannot determine whether the time of semester effect is a product of internal participant characteristics or external pressures. Nevertheless, researchers should take into account this end-of-semester data quality difference when deciding the time-frame of their data collection.
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Schlenkrich, Lara. "An investigation of social computing." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1006194.
Повний текст джерелаАнотація:
Social network sites have recently become extremely popular online destinations as they offer users easy ways to build and maintain their relationships with each other. Consequently, students, lecturers, teachers, parents and businesses are using these tools to communicate with each other in a fast and cost-effective manner. However, literature suggests that the full potential of social network sites has not yet been revealed since users are still battling to overcome the various negative characteristics surrounding these sites. A framework for appropriate use of these sites is needed so that users are able to overcome these negative aspects, allowing them to be more effective and use the sites successfully. The goal of this research is to construct a framework for perceived successful use of social computing tools in educational institutions. This framework will include critical success factors that need to be adopted by users in order to develop the positive aspects of social computing, while at the same time overcoming the disadvantages experienced by users. Factors for successful use were derived from the literature and consolidated into a theoretical framework in order to understand the factors that drive successful use of social network sites. Measures used to test successful use of social network sites were also derived from these sources and were included in the same theoretical framework; these measures allow users to evaluate the extent of perceived successful use of social network sites. This framework was tested empirically by means of a pilot study and online survey, and revised according to the results of the survey. The factors were identified using Cronbach alpha coefficients (in the pilot study) and exploratory factor analysis to confirm the reliability of the scales developed. Pearson product-moment correlation coefficient analysis, t-tests and Pearson Chi-Square tests were used to measure the relationships amongst the variables in the framework proposed in this research. The factors influencing perceived successful use of social network sites were identified by the empirical study as: • Privacy and Security Settings need to be enabled. These are split into: - Settings: content that users allow others to see - Viewers: people who are allowed onto a user's profile • It is necessary for users to practise Legal and Acceptable Activities when using social network sites • Suspect Information needs to be checked before sharing it with others • Personal and Professional Time needs to be separated to ensure that work is completed before social activities occur • Users need to practise Professional and Ethical Behaviour • Users need to have a Positive Attitude when using social network sites • Usability of sites affects their success. This includes: - technical capacity (broadband) - ease of use - functionality (range of features and functions) • Current and Controversial Issues need to be discussed on social network sites. The extent to which social network sites are being used successfully can be evaluated by the presence of the following measures: • Range of Content must be available to users. This includes: - Content displayed on profiles - Viewers able to visit profiles • Visitors Behaviour is monitored and no unwanted visitors are present users' profiles • Social Contracts found on sites are followed by users • Critical Thinking Skills and Accurate Information are displayed by users • Work is completed before social activities occur on sites • A Variety of Users is present on sites • Collaboration between people as well as variety of opinions exist on sites • Social Capital (well-being) is present after users have been on sites • Learning and Advising Skills are enhanced on sites. The framework developed provides users with a useful instrument to overcome the negative characteristics associated with social network sites. If used successfully, social network sites can offer lecturers and students a unique method to develop their relationship, creating a positive learning experience.
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Hughes, Jessie. "Differences in self-reported perceptions of privacy between online social and commercial networking users /." Online version of thesis, 2008. http://hdl.handle.net/1850/8221.
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Ruan, Yiye. "Joint Dynamic Online Social Network Analytics Using Network, Content and User Characteristics." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420765022.
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Haynes, J. D. "Risk and regulation of access to personal data on online social networking services in the UK." Thesis, City University London, 2015. http://openaccess.city.ac.uk/11972/.
Повний текст джерелаАнотація:
This research investigates the relative effectiveness of different modes of regulation of access to personal data on social networking services in the UK. A review of the literature demonstrated that there was a gap in research comparing different regulatory modes applied to online social networking services (SNSs). A model of regulation was developed based on Lessig’s four modes of regulating the internet. Risk to individual users was selected as a way of testing different regulatory approaches, using the premise that risk-based regulation has become a key consideration in European regulation. The regulatory effects were tested using: online surveys, interviews with industry experts, content analysis of privacy policies, and a legislative review. The research data are appended to the main body of the thesis. The research demonstrated the potential of risk as a means of distinguishing between different regulatory modes and concluded that a combination of regulatory approaches was the most effective way of protecting individuals against abuse of personal data on online SNSs. Further research suggested includes: looking at risk from the perspective of companies, and of society; further development of the regulatory model; and country comparisons to discover whether the findings of this study are more generally applicable.
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Kempa, Ewelina. "Social media addiction : The paradox of visibility & vulnerability." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-1030.
Повний текст джерелаАнотація:
We currently post a large amount of personal information about ourselves on social media sites. Many times though, users of these services are poorly aware of what kind of terms and conditions they agree to. There are in fact many techniques available that ensure users privacy, yet not many organizations make the effort to have those in place. Making a profit is what matters for companies and information on users is highly valued. It is the lack of regulations regarding data collection that enable organizations not to consider their users privacy. The data that can be collected is vast, it is important to understand that everything we do online, every search, click, shop and view is stored and the information is many times sold along to third-parties. Using information on users, companies can make profit by for example making predictions on the users, figuring out what they are interested in buying. It is nevertheless very difficult to make long-lasting regulations as the web constantly changes and grows. A qualitative research was conducted to observe to what extent social media addiction and its consequences is being discussed and researched. Interviews with social media users were also conducted. After an analysis on the findings it is clear that many users in fact would like to have more privacy online yet they feel the need to accept the term and conditions any way. Many users also state that they happily would like to read the terms and conditions, had they been written in a different way.
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Blackburn, Jeremy. "An Analysis of (Bad) Behavior in Online Video Games." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5412.
Повний текст джерелаАнотація:
This dissertation studies bad behavior at large-scale using data traces from online video games. Video games provide a natural laboratory for exploring bad behavior due to their popularity, explicitly defined (programmed) rules, and a competitive nature that provides motivation for bad behavior. More specifically, we look at two forms of bad behavior: cheating and toxic behavior.
Cheating is most simply defined as breaking the rules of the game to give one player an edge over another. In video games, cheating is most often accomplished using programs, or "hacks," that circumvent the rules implemented by game code. Cheating is a threat to the gaming industry in that it diminishes the enjoyment of fair players, siphons off money that is paid to cheat creators, and requires investment in anti-cheat technologies.
Toxic behavior is a more nebulously defined term, but can be thought of as actions that violate social norms, especially those that harm other members of the society. Toxic behavior ranges from insults or harassment of players (which has clear parallels to the real world) to domain specific instances such as repeatedly "suiciding"" to help an enemy team. While toxic behavior has clear parallels to bad behavior in other online domains, e.g., cyberbullying, if gone unchecked it has the potential to "kill" a game by driving away its players.
We first present a distributed architecture and reference implementation for the collection and analysis of large-scale social data. Using this implementation we then study the social structure of over 10 million gamers collected from a planetary scale Online Social Network, about 720 thousand of whom have been labeled cheaters, finding a significant correlation between social structure and the probability of partaking in cheating behavior. We additionally collect over half a billion daily observations of the cheating status of these gamers. Using about 10 months of detailed server logs from a community owned and operated game server we next analyze how relationships in the aforementioned online social network are backed by in-game interactions. Next, we use the insights gained and find evidence for a contagion process underlying the spread of cheating behavior and perform a data driven simulation using mathematical models for contagion. Finally, we build a model using millions of crowdsourced decisions for predicting toxic behavior in online games.
To the best of our knowledge, this dissertation presents the largest study of bad behavior to date. Our findings confirm theories about cheating and unethical behavior that have previously remained untested outside of controlled laboratory experiments or only with small, survey based studies. We find that the intensity of interactions between players is a predictor of a future relationship forming. We provide statistically significant evidence for cheating as a contagion. Finally, we extract insights from our model for detecting toxic behavior on how human reviewers perceive the presence and severity of bad behavior.
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Yang, Shuang-Hong. "Predictive models for online human activities." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43689.
Повний текст джерелаАнотація:
The availability and scale of user generated data in online systems raises tremendous challenges and opportunities to analytic study of human activities. Effective modeling of online human activities is not only fundamental to the understanding of human behavior, but also important to the online industry. This thesis focuses on developing models and algorithms to predict human activities in online systems and to improve the algorithmic design of personalized/socialized systems (e.g., recommendation, advertising, Web search systems). We are particularly interested in three types of online user activities, i.e., decision making, social interactions and user-generated contents. Centered around these activities, the thesis focuses on three challenging topics:
1. Behavior prediction, i.e., predicting users' online decisions. We present Collaborative-Competitive Filtering, a novel game-theoretic framework for predicting users' online decision making behavior and leverage the knowledge to optimize the design of online systems (e.g., recommendation systems) in respect of certain strategic goals (e.g., sales revenue, consumption diversity).
2. Social contagion, i.e., modeling the interplay between social interactions and individual behavior of decision making. We establish the joint Friendship-Interest Propagation model and the Behavior-Relation Interplay model, a series of statistical approaches to characterize the behavior of individual user's decision making, the interactions among socially connected users, and the interplay between these two activities. These techniques are demonstrated by applications to social behavior targeting.
3. Content mining, i.e., understanding user generated contents. We propose the Topic-Adapted Latent Dirichlet Allocation model, a probabilistic model for identifying a user's hidden cognitive aspects (e.g., knowledgability) from the texts created by the user. The model is successfully applied to address the challenge of ``language gap" in medical information retrieval.
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Jiao, Lei [Verfasser], Xiaoming [Akademischer Betreuer] Fu, Jun [Akademischer Betreuer] Li, and Dieter [Akademischer Betreuer] Hogrefe. "Online Social Network Data Placement over Clouds / Lei Jiao. Gutachter: Jun Li ; Dieter Hogrefe. Betreuer: Xiaoming Fu." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2014. http://d-nb.info/1055814485/34.
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Syed, Romilla. "DYNAMICS OF IDENTITY THREATS IN ONLINE SOCIAL NETWORKS: MODELLING INDIVIDUAL AND ORGANIZATIONAL PERSPECTIVES." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3906.
Повний текст джерелаАнотація:
This dissertation examines the identity threats perceived by individuals and organizations in Online Social Networks (OSNs). The research constitutes two major studies. Using the concepts of Value Focused Thinking and the related methodology of Multiple Objectives Decision Analysis, the first research study develops the qualitative and quantitative value models to explain the social identity threats perceived by individuals in Online Social Networks. The qualitative value model defines value hierarchy i.e. the fundamental objectives to prevent social identity threats and taxonomy of user responses, referred to as Social Identity Protection Responses (SIPR), to avert the social identity threats. The quantitative value model describes the utility of the current social networking sites and SIPR to achieve the fundamental objectives for averting social identity threats in OSNs. The second research study examines the threats to the external identity of organizations i.e. Information Security Reputation (ISR) in the aftermath of a data breach. The threat analysis is undertaken by examining the discourses related to the data breach at Home Depot and JPMorgan Chase in the popular microblogging website, Twitter, to identify: 1) the dimensions of information security discussed in the Twitter postings; 2) the attribution of data breach responsibility and the related sentiments expressed in the Twitter postings; and 3) the subsequent diffusion of the tweets that threaten organizational reputation.
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Ray, Aaron Parker. "Planning Connected: Using Online Social Networks to Improve Knowledge about Places and Communities." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/580.
Повний текст джерелаАнотація:
The advent of Social Networking Systems (SNS) has introduced new possibilities for planners to refine and extend conventional engagement and data-gathering techniques by leveraging user-contributed, spatially-referenced content freely available online. This study examines the use of SNS content as community input, complementing input gathered through traditional participatory processes such as workshops, public comment hearings, and charrettes. Four case studies of recent community planning projects in the United States are analyzed, comparing the data gathered from traditional participatory processes with available SNS content related to each project study area, to determine to what extent the inclusion of SNS data would improve the overall data- gathering efforts of these projects. Three significant findings emerge from this analysis: (i) that SNS data analysis can positively complement data gathered from traditional participatory processes, (ii) that although SNS data analysis can provide useful data to planners, it is not a direct replacement for conventional engagement techniques, and (iii) that SNS data analysis is most effective for projects in neighborhoods with a well- defined identity. The study also examines the characteristics of effective SNS data analysis integration and discusses broader implications for planning practitioners and additional research needed.
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Warfel, Elizabeth A. "Perceptions of privacy on Facebook /." Online version of thesis, 2008. http://hdl.handle.net/1850/6973.
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Suntaxi, Gabriela [Verfasser], and K. [Akademischer Betreuer] Böhm. "Preserving Secrecy in Online Social Networks: Data Outsourcing, Access Control, and Secrecy Schemes / Gabriela Suntaxi ; Betreuer: K. Böhm." Karlsruhe : KIT-Bibliothek, 2020. http://d-nb.info/1223985938/34.
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Abufouda, Mohammed [Verfasser], and Katharina [Akademischer Betreuer] Zweig. "Learning From Networked-data: Methods and Models for Understanding Online Social Networks Dynamics / Mohammed Abufouda ; Betreuer: Katharina Zweig." Kaiserslautern : Technische Universität Kaiserslautern, 2020. http://d-nb.info/1221599747/34.
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Mohammadi, Samin. "Analysis of user popularity pattern and engagement prediction in online social networks." Thesis, Evry, Institut national des télécommunications, 2018. http://www.theses.fr/2018TELE0019/document.
Повний текст джерелаАнотація:
De nos jours, les médias sociaux ont largement affecté tous les aspects de la vie humaine. Le changement le plus significatif dans le comportement des gens après l'émergence des réseaux sociaux en ligne (OSNs) est leur méthode de communication et sa portée. Avoir plus de connexions sur les OSNs apporte plus d'attention et de visibilité aux gens, où cela s'appelle la popularité sur les médias sociaux. Selon le type de réseau social, la popularité se mesure par le nombre d'adeptes, d'amis, de retweets, de goûts et toutes les autres mesures qui servaient à calculer l'engagement. L'étude du comportement de popularité des utilisateurs et des contenus publiés sur les médias sociaux et la prédiction de leur statut futur sont des axes de recherche importants qui bénéficient à différentes applications telles que les systèmes de recommandation, les réseaux de diffusion de contenu, les campagnes publicitaires, la prévision des résultats des élections, etc. Cette thèse porte sur l'analyse du comportement de popularité des utilisateurs d'OSN et de leurs messages publiés afin, d'une part, d'identifier les tendances de popularité des utilisateurs et des messages et, d'autre part, de prévoir leur popularité future et leur niveau d'engagement pour les messages publiés par les utilisateurs. A cette fin, i) l'évolution de la popularité des utilisateurs de l'ONS est étudiée à l'aide d'un ensemble de données d'utilisateurs professionnels 8K Facebook collectées par un crawler avancé. L'ensemble de données collectées comprend environ 38 millions d'instantanés des valeurs de popularité des utilisateurs et 64 millions de messages publiés sur une période de 4 ans. Le regroupement des séquences temporelles des valeurs de popularité des utilisateurs a permis d'identifier des modèles d'évolution de popularité différents et intéressants. Les grappes identifiées sont caractérisées par l'analyse du secteur d'activité des utilisateurs, appelé catégorie, leur niveau d'activité, ainsi que l'effet des événements externes. Ensuite ii) la thèse porte sur la prédiction de l'engagement des utilisateurs sur les messages publiés par les utilisateurs sur les OSNs. Un nouveau modèle de prédiction est proposé qui tire parti de l'information mutuelle par points (PMI) et prédit la réaction future des utilisateurs aux messages nouvellement publiés. Enfin, iii) le modèle proposé est élargi pour tirer profit de l'apprentissage de la représentation et prévoir l'engagement futur des utilisateurs sur leurs postes respectifs. L'approche de prédiction proposée extrait l'intégration de l'utilisateur de son historique de réaction au lieu d'utiliser les méthodes conventionnelles d'extraction de caractéristiques. La performance du modèle proposé prouve qu'il surpasse les méthodes d'apprentissage conventionnelles disponibles dans la littérature. Les modèles proposés dans cette thèse, non seulement déplacent les modèles de prédiction de réaction vers le haut pour exploiter les fonctions d'apprentissage de la représentation au lieu de celles qui sont faites à la main, mais pourraient également aider les nouvelles agences, les campagnes publicitaires, les fournisseurs de contenu dans les CDN et les systèmes de recommandation à tirer parti de résultats de prédiction plus précis afin d'améliorer leurs services aux utilisateursNowadays, social media has widely affected every aspect of human life. The most significant change in people's behavior after emerging Online Social Networks (OSNs) is their communication method and its range. Having more connections on OSNs brings more attention and visibility to people, where it is called popularity on social media. Depending on the type of social network, popularity is measured by the number of followers, friends, retweets, likes, and all those other metrics that is used to calculate engagement. Studying the popularity behavior of users and published contents on social media and predicting its future status are the important research directions which benefit different applications such as recommender systems, content delivery networks, advertising campaign, election results prediction and so on. This thesis addresses the analysis of popularity behavior of OSN users and their published posts in order to first, identify the popularity trends of users and posts and second, predict their future popularity and engagement level for published posts by users. To this end, i) the popularity evolution of ONS users is studied using a dataset of 8K Facebook professional users collected by an advanced crawler. The collected dataset includes around 38 million snapshots of users' popularity values and 64 million published posts over a period of 4 years. Clustering temporal sequences of users' popularity values led to identifying different and interesting popularity evolution patterns. The identified clusters are characterized by analyzing the users' business sector, called category, their activity level, and also the effect of external events. Then ii) the thesis focuses on the prediction of user engagement on the posts published by users on OSNs. A novel prediction model is proposed which takes advantage of Point-wise Mutual Information (PMI) and predicts users' future reaction to newly published posts. Finally, iii) the proposed model is extended to get benefits of representation learning and predict users' future engagement on each other's posts. The proposed prediction approach extracts user embedding from their reaction history instead of using conventional feature extraction methods. The performance of the proposed model proves that it outperforms conventional learning methods available in the literature. The models proposed in this thesis, not only improves the reaction prediction models to exploit representation learning features instead of hand-crafted features but also could help news agencies, advertising campaigns, content providers in CDNs, and recommender systems to take advantage of more accurate prediction results in order to improve their user services
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Rörsch, Jonatan, and Mikael Johansson. "Social Networks : Creating Organizational Benefits out of an Online Conversation." Thesis, Uppsala universitet, Industriell teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-207086.
Повний текст джерелаАнотація:
Scania is increasing its production by improving its efficiency. The company is striving to achieve its new productivity goals without making large investments. In large organizations such as Scania there are many competent and skilled employees. However, since many of their offices are located worldwide communication is not always efficient and optimal. The purpose of this thesis is to show the potential benefits when implementing and operating online social networks within global organizations including Scania. Through empirical studies of global organizations which have utilized online social networks for a relatively long period this study intends to collect information which can help generate knowledge about the implementation and operation of online social networks. Our research revealed that important aspects of the implementation of an organization's online social network are knowledge management, dissemination, social ties and links between micro-and macro-networks. Thereby we concluded that online social networks lead to the creation of benefits for the individual as well as for the organization. Based on the theoretical framework and empirical evidence gathered in this study, we have concluded that an implemented model of an online social network fosters such benefits as improved communication channels and increased efficiency in the workplace.
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MORENO, Bruno Neiva. "Representação e análise de encontros espaço-temporais publicados em redes sociais online." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18621.
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O crescente uso de redes sociais online tem feito com que usuários compartilhem, também, informações detalhadas a respeito dos locais que os mesmos frequentam, criando uma ligação entre o mundo físico (o movimento destes usuários no globo) e o mundo virtual (o que eles expressam sobre esses movimentos nas redes). O “check-in” é a funcionalidade responsável pelo compartilhamento da localização. Em uma rede social com essa funcionalidade, qualquer usuário pode publicar o local em que o mesmo está em determinado instante de tempo. Esta tese apresenta novas abordagens de análise de redes sociais online considerando as dimensões social, espacial e temporal que são inerentes à publicação de check-ins de usuários. As informações sociais, espaciais e temporais são definidas sob a perspectiva de encontros de usuários, sendo este o objeto de estudo dessa tese. Encontros ocorrem quando duas pessoas (dimensão social), estão em algum local (dimensão espacial), em determinado instante de tempo (dimensão temporal) e decidem publicar esse encontro através de check-ins. Além de apresentar um algoritmo para detecção de encontros, é definido um modelo para representação desses encontros. Este modelo é chamado de SiST (do inglês, SocIal, Spatial and Temporal) e modela encontros por meio de redes complexas. Para validar o modelo proposto, foram utilizados dados reais de redes sociais online. Com esses dados, os encontros foram detectados e analisados sob diferentes perspectivas com o objetivo de investigar a existência de alguma lei que governe a publicação dos mesmos, bem como para identificar padrões relativos a sua ocorrência, como padrões temporais, por exemplo. Além disso, as redes construídas a partir do modelo SiST também foram analisadas em termos de suas propriedades estruturais e topológicas. Por meio de redes SiST também foram estudados padrões de movimentação de usuários, como situações em que usuários se movimentam em grupo no globo ou situações em que um usuário é seguido por outros.
The growing use of online social networks has caused users to share detailed information about the places they visit, resulting on a clear connection between the physical world (i.e. the movement of these users on the globe) and the virtual world (which they express about these movements in the social network). The functionality responsible for sharing location by users is named as “check in”. In a social network with this feature, any user can publish their visited places. This thesis presents new approaches for online social networks analysis considering the social, spatial and temporal dimensions that are implicit in the publication of users check-ins. Social, spatial and temporal information is defined from the perspective of “user encounters”, which is the study object of this thesis. Users encounters occur when two people (social dimension) are somewhere (spatial dimension) in a given time (temporal dimension) and decide to publish this meeting through check-ins. In addition to the algorithm presented for encounters detection, we also defined a model for representation of these encounters. This model is called as SiST (SocIal, Spatial and Temporal). The SiST model basically represent encounters by a graph structure. To validate the proposed approach, we used real data from online social networks. With these data the users encounters were detected and analyzed from different perspectives aiming at investigating the existence of any law governing the publication of encounters and also to identify patterns related to its occurrence, like temporal patterns, for example. Furthermore, the graphs built from SiST model were also analyzed in terms of its structural and topological properties. Through the SiST networks the users movements were studied as well, like in situations in which users move in group or situations where users are followed by other users.
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Liu, Zhi. "Location Estimation and Geo-Correlated Information Trends." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc1062799/.
Повний текст джерелаАнотація:
A tremendous amount of information is being shared every day on social media sites such as Facebook, Twitter or Google+. However, only a small portion of users provide their location information, which can be helpful in targeted advertising and many other services. Current methods in location estimation using social relationships consider social friendship as a simple binary relationship. However, social closeness between users and structure of friends have strong implications on geographic distances. In the first task, we introduce new measures to evaluate the social closeness between users and structure of friends. Then we propose models that use them for location estimation. Compared with the models which take the friend relation as a binary feature, social closeness can help identify which friend of a user is more important and friend structure can help to determine significance level of locations, thus improving the accuracy of the location estimation models. A confidence iteration method is further introduced to improve estimation accuracy and overcome the problem of scarce location information. We evaluate our methods on two different datasets, Twitter and Gowalla. The results show that our model can improve the estimation accuracy by 5% - 20% compared with state-of-the-art friend-based models.
In the second task, we also propose a Local Event Discovery and Summarization (LEDS) framework to detect local events from Twitter. Many existing algorithms for event detection focus on larger-scale events and are not sensitive to smaller-scale local events. Most of the local events detected by these methods are major events like important sports, shows, or big natural disasters. In this work, we propose the LEDS framework to detect both bigger and smaller events. LEDS contains three key steps: 1) Detecting possible event related terms by monitoring abnormal distribution in different locations and times; 2) Clustering tweets based on their key terms, time, and location distribution; and 3) Extracting descriptions include time, location, and key sentences of local events from clusters. The model is evaluated on a real-world Twitter dataset with more than 60 million tweets. The analysis of Twitter data can help to predict or explain many real-world phenomena. The relationships among events in the real world can be reflected among the topics on social media. In the third task, we propose the concept of topic association and the associated mining algorithms. Topics with close temporal and spatial relationship may have direct or potential association in the real world. Our goal is to mine such topic associations and show their relationships in different time-region frames. We propose to use the concepts of participation ratio and participation index to measure the closeness among topics and propose a spatiotemporal index to calculate them efficiently. With the topic filtering and the topic combination, we further optimize the mining process and the mining results.
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Riley, Will. "We the undersigned." Thesis, Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28102.
Повний текст джерелаАнотація:
Thesis (M. S.)--Literature, Communication, and Culture, Georgia Institute of Technology, 2009.Committee Chair: DiSalvo, Carl; Committee Member: Bogost, Ian; Committee Member: Klein, Hans; Committee Member: Murray, Janet; Committee Member: Pearce, Celia
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Pscheida, Daniela, Claudia Minet, Sabrina Herbst, Steffen Albrecht, and Thomas Köhler. "Use of Social Media and Online-based Tools in Academia: Results of the Science 2.0-Survey 2014: Data Report 2014." Technische Universität Dresden, 2015. https://tud.qucosa.de/id/qucosa%3A29117.
Повний текст джерелаАнотація:
The Science 2.0-Survey investigates the dissemination and use of online tools and social media applications among scientists of all disciplines at German universities (institutions of higher education) and research institutions (Leibniz, Helmholtz, Max Planck institutes). Results show that digital, online-based tools have found widespread use and acceptance in academia and must therefore be considered a central component of scientific working processes. Furthermore the data gathered also make it clear that certain usage patterns begin to emerge and stabilise as routines in everyday academic work.
The most popular tools are the online encyclopedia Wikipedia (95% of all respondents use it professionally), mailing lists (78%), online archives/databases (75%) and content sharing/cloud services such as Dropbox or Slideshare (70%). Meanwhile, social bookmarking services remain largely untapped and unknown among scientists (only 5% professional usage).
Online tools and social media applications are most commonly utilised in a research context. In addition to Wikipedia (67%), the top three tools used for research purposes are online archives/databases (63%), reference management software (49%) and content sharing/cloud services (43%). In teaching, learning management systems (32%) play a significant role, even though this mainly applies to universities. Video/photo communities (25%), online archives/databases (23%) and content sharing/cloud services (21%) are also used by scientists in the context of teaching. However, there seems to be some backlog in the fi eld of science communication. Scientists are rarely active in this area; 45 per cent of respondents say science communication is not part of their range of duties, while for another 40 per cent such activities comprise no more than 10 per cent of their daily workload. When active in the fi eld of science communication, scientists seem to favour classic online-based tools such as mailing lists (44%) or videoconferences/VoIP (35%), while typical Web 2.0 tools such as weblogs (10%) or microblogs (6%) are rarely used in this context. Social network sites (SNS) with a professional and/or academic orientation (30%), however, are relatively common for communication purposes in academia. The situation is similar for science administration practices where, although the use of online-based tools and social media applications is more common, no more than one-quarter of the scientists use a particular tool, while personal organizers/schedule managers (27%) dominate.
The main factors cited by scientists as preventing them from using online-based tools and social media applications professionally are a lack of added value for their own work (30%), insufficient technical assistance (21%) and insufficient time to become familiar with the handling of the tools (15%). In particular, many scientists do not use microblogs (53%), discussion forums (41%) and weblogs (40%) professionally because they cannot see any added value in using them.
With regard to the attitudes of scientists in relation to the use of online tools and social media applications, results show that they are aware of privacy issues and have relatively high concerns about the spread of and access to personal data on the Internet. However, scientists generally have few reservations about dealing with social media and show themselves to be open to new technological developments.
This report documents the results of a Germany-wide online survey of a total of 2,084 scientists at German universities (1,419) and research institutions (665). The survey explores the usage of 18 online tools and social media applications for daily work in research, teaching, science administration and science communication. In addition to the frequency and context of use, the survey also documents reasons for the non-use of tools, as well as general attitudes towards the Internet and social media. The survey was conducted between 23 June 2014 and 20 July 2014 and is a joint project of the Leibniz Research Alliance „Science 2.0“, led by the Technische Universität Dresden’s Media Center.:Executive summary
1. Introduction
2. Methodology and research design
3. Characterisation of the data sample
Gender
Age
Type of institution
Academic position
Duration of employment in academic context
Subject group
Fields of activity
4. Use of social media and online-based tools
4.1 General use of social media und online-based tools
General usage
Devices
4.2 Use of social media und online-based tools in academic work
Professional and private usage
Frequency of professional usage
Professional usage by gender
Professional usage by age
Professional usage by subject group
Professional usage by position
4.3 Use of online-based tools and social media applications in various areas of academic activity
4.3.1 Use of online-based tools and social media applications in research
4.3.2 Use of online-based tools and social media applications in teaching
4.3.3 Use of online-based tools and social media applications in science administration
4.3.4 Use of online-based tools and social media applications in science communication
4.4 Barriers to the use of social media applications and online-based tools in everyday academic life
Reasons for professional non-use of online tools
4.5 Active and passive use of social media applications in everyday academic life
5. Attitudes to the use of social media applications and online-based tools in
everyday academic life
Overall attitudes
Attitude measurement reliability analysis
Attitudes by gender
Attitudes by age
Attitudes by position
Attitudes by subject group
References
Cover letter English
Cover letter German
Questionnaire English
Questionnaire German
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Chiarella, Andrew Francesco 1971. "Enabling the collective to assist the individual : a self-organising systems approach to social software and the creation of collaborative text signals." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=115618.
Повний текст джерелаАнотація:
Authors augment their texts using devices such as bold and italic typeface to signal important information to the reader. These typographical text signals are an example of a signal designed to have some affect on others. However, some signals emerge through the unplanned, indirect, and collective efforts of a group of individuals. Paths emerge in parks without having been designed by anyone. Objects accumulate wear patterns that signal how others have interacted with the object. Books open to important, well studied pages because the spine has worn, for example (Hill, Hollan, Wroblewski, & McCandless, 1992). Digital text and the large-scale collaboration made possible through the internet provide an opportunity to examine how unplanned, collaborative text signals could emerge. A software application was designed, called CoREAD, that enables readers to highlight sections of the text they deem important. In addition, CoREAD adds text signals to the text using font colour, based on the group's collective history and an aggregation function based on self-organising systems. The readers are potentially influenced by the text signals presented by CoREAD but also help to modify these same signals. Importantly, readers only interact with each other indirectly through the text. The design of CoREAD was greatly inspired by the previous work on history-enriched digital objects (Hill & Hollan, 1993) and at a more general level it can be viewed as an example of distributed cognition (Hollan, Hutchins, & Kirsh, 2000).Forty undergraduate students read two texts on topics from psychology using CoREAD. Students were asked to read each text in order to write a summary of it. After each new student read the text, the text signals were changed to reflect the current group of students. As such, each student read the text with different text signals presented.
The data were analysed for each text to determine if the text signals that emerged were stable and valid representations of the semantic content of the text. As well, the students' summaries were analysed to determine if students who read the text after the text signals had stabilised produced better summaries. Three methods demonstrated that CoREAD was capable of generating stable typographical text signals. The high importance text signals also appeared to capture the semantic content of the texts. For both texts, a summary made of the high signals performed as well as a benchmark summary. The results did not suggest that the stable text signals assisted readers to produce better summaries, however. Readers may not respond to these collaborative text signals as they would to authorial text signals, which previous research has shown to be beneficial (Lorch, 1989). The CoREAD project has demonstrated that readers can produce stable and valid text signals through an unplanned, self-organising process.
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Hsieh, Jane W. "Asking questions is easy, asking great questions is hard: Constructing Effective Stack Overflow Questions." Oberlin College Honors Theses / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1589722602631253.
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Silva, Jesús, Naveda Alexa Senior, Suarez Ramiro Gamboa, Palma Hugo Hernández, and Núẽz William Niebles. "Method for Collecting Relevant Topics from Twitter supported by Big Data." Institute of Physics Publishing, 2020. http://hdl.handle.net/10757/652145.
Повний текст джерелаАнотація:
There is a fast increase of information and data generation in virtual environments due to microblogging sites such as Twitter, a social network that produces an average of 8, 000 tweets per second, and up to 550 million tweets per day. That's why this and many other social networks are overloaded with content, making it difficult for users to identify information topics because of the large number of tweets related to different issues. Due to the uncertainty that harms users who created the content, this study proposes a method for inferring the most representative topics that occurred in a time period of 1 day through the selection of user profiles who are experts in sports and politics. It is calculated considering the number of times this topic was mentioned by experts in their timelines. This experiment included a dataset extracted from Twitter, which contains 10, 750 tweets related to sports and 8, 758 tweets related to politics. All tweets were obtained from user timelines selected by the researchers, who were considered experts in their respective subjects due to the content of their tweets. The results show that the effective selection of users, together with the index of relevance implemented for the topics, can help to more easily find important topics in both sport and politics.
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Zhao, Tao [Verfasser], Xiaoming [Akademischer Betreuer] Fu, Xiaoming [Gutachter] Fu, and Marcus [Gutachter] Baum. "Identification of Online Users' Social Status via Mining User-Generated Data / Tao Zhao ; Gutachter: Xiaoming Fu, Marcus Baum ; Betreuer: Xiaoming Fu." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/1194646069/34.
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Tam, Weng Tong. "WeChat in work environment in Macao, a use and gratification study." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3952599.
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