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Topal, Kamil. "ALGORITHMS ON SOCIAL MEDIA DATA AND EMOTION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1491475766933736.
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Albrektsson, Fredrik. "Detecting Sockpuppets in Social Media with Plagiarism Detection Algorithms." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208553.
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As new forms of propaganda and information control spread across the internet, the need for novel ways of identifying them increases as well. One increasingly popular method of spreading false messages on microblogs like Twitter is to disseminate them from seemingly ordinary, but centrally controlled and coordinated user accounts – sockpuppets. In this paper we examine a number of potential methods for identifying these by way of applying plagiarism detection algorithms for text, and evaluate their performance against this type of threat. We identify one type of algorithm in particular – that using vector space modeling of text – as particularly useful in this regard.Allteftersom nya former av propaganda och informationskontroll sprider sig över internet krävs också nya sätt att identifiera dessa. En allt mer populär metod för att sprida falsk information på mikrobloggar som Twitter är att göra det från till synes ordinära, men centralt kontrollerade och koordinerade användarkonton – på engelska kända som “sockpuppets”. I denna undersökning testar vi ett antal potentiella metoder för att identifiera dessa genom att applicera plagiatkontrollalgoritmer ämnade för text, och utvärderar deras prestanda mot denna sortens hot. Vi identifierar framför allt en typ av algoritm – den som nyttjar vektorrymdsmodellering av text – som speciellt användbar i detta avseende.
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Hsiao, Shih-Hui. "SOCIAL MEDIA ANALYTICS − A UNIFYING DEFINITION, COMPREHENSIVE FRAMEWORK, AND ASSESSMENT OF ALGORITHMS FOR IDENTIFYING INFLUENCERS IN SOCIAL MEDIA." UKnowledge, 2016. http://uknowledge.uky.edu/busadmin_etds/8.
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Given its relative infancy, there is a dearth of research on a comprehensive view of business social media analytics (SMA). This dissertation first examines current literature related to SMA and develops an integrated, unifying definition of business SMA, providing a nuanced starting point for future business SMA research. This dissertation identifies several benefits of business SMA, and elaborates on some of them, while presenting recent empirical evidence in support of foregoing observations. The dissertation also describes several challenges facing business SMA today, along with supporting evidence from the literature, some of which also offer mitigating solutions in particular contexts.
The second part of this dissertation studies one SMA implication focusing on identifying social influencer. Growing social media usage, accompanied by explosive growth in SMA, has resulted in increasing interest in finding automated ways of discovering influencers in online social interactions. Beginning 2008, many variants of multiple basic approaches have been proposed. Yet, there is no comprehensive study investigating the relative efficacy of these methods in specific settings. This dissertation investigates and reports on the relative performance of multiple methods on Twitter datasets containing between them tens of thousands to hundreds of thousands of tweets. Accordingly, the second part of the dissertation helps further an understanding of business SMA and its many aspects, grounded in recent empirical work, and is a basis for further research and development. This dissertation provides a relatively comprehensive understanding of SMA and the implementation SMA in influencer identification.
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Polishuk, Natali. "Implementing social media data in algorithms for estimating crowdedness in tourist's attractions." Thesis, Linnéuniversitetet, Institutionen för medieteknik (ME), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-66075.
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Doo, Myungcheol. "Spatial and social diffusion of information and influence: models and algorithms." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44740.
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With the ubiquity of broadband, wireless and mobile networking and the diversity of user-driven social networks and social channels, we are entering an information age where people and vehicles are connected at all times, and information and influence are diffused continuously through not only traditional authoritative media such as news papers, TV and radio broadcasting, but also user-driven new channels for disseminating information and diffusing influence. Social network users and mobile travelers can influence and be influenced by the social and spatial connectivity that they share through an impressive array of social and spatial channels, ranging from friendship, activity, professional or social groups to spatial, location-aware, and mobility aware events.
In this dissertation research, we argue that spatial alarms and activity-based social networks are two fundamentally new types of information and influence diffusion channels. Such new channels have the potential of enriching our professional experiences and our personal life quality in many unprecedented ways. For instance, spatial alarms enable people to share their experiences or disseminate certain points of interest by leaving location-dependent greetings, tips or graffiti and location dependent tour guide to their friends, colleagues and family members. Through social networks, people can influence their friends and colleagues by the activities they have engaged, such as reviews and blogs on certain events or products. More interestingly, the power of such spatial and social diffusion of information and influence can go far beyond our physical reach. People can utilize user-generated social and spatial channels as effective means to disseminate information and propagate influence to a much wider and possibly unknown range of audiences and recipients at any time and in any location. A fundamental challenge in embracing such new and exciting ways of information diffusion is to develop effective and scalable models and algorithms as enabling technology and building blocks. This dissertation research is dedicated towards this ultimate objective with three novel and unique contributions.
First, we develop an activity driven and self-configurable social influence model and a suite of computational algorithms to compute and rank social network nodes in terms of activity-based influence diffusion over social network topologies. By activity driven we mean that the real impact of social influence and the speed of such influence propagation should be computed based on the type, the amount and the time window of the activities performed by a social network node in addition to its social connectivity (social network topology). By self-configurable we mean that the diffusion efficiency and effectiveness are dynamically adapted based on the settings and tunings of multiple spatial and social parameters such as diffusion context, diffusion location, diffusion rate, diffusion energy (heat), diffusion coverage and diffusion incentives (e.g., reward points), to name a few. We evaluate our approach through datasets collected from Facebook, Epinions, and DBLP datasets. Our experimental results show that our activity based social influence model outperforms existing topology-based social influence model in terms of effectiveness and quality with respect to influence ranking and influence coverage computation.
Second, we further enhance our activity based social influence model along two dimensions. At first, we use a probabilistic diffusion model to capture the intrinsic properties of social influence such that nodes in a social network may have the choice of whether to participate in a social influence propagation process. We examine threshold based approach and independent probabilistic cascade based approach to determine whether a node is active or inactive in each round of influence diffusion. Secondly, we introduce incentives using multi-scale reward points, which are popularly used in many business settings. We then examine the effectiveness of reward points based incentives in stimulating the diffusion of social influences. We show that given a set of incentives, some active nodes may become more active whereas some inactive nodes may become active. Such dynamics changes the composition of the top-k influential nodes computed by activity-based social influence model. We make several interesting observations: First, popular users who are high degree nodes and have many friends are not necessarily influential in terms of spawning new activities or spreading ideas and information. Second, most influential users are more active in terms of their participation in the social activities and interactions with their friends in the social network. Third, multi-scale reward points based incentives can be effective to both inactive nodes and active nodes.
Third, we introduce spatial alarms as the basic building blocks for location-dependent information sharing and influence diffusion. People can share and disseminate their location based experiences and points of interest to their friends and colleagues in the form of spatial alarms. Spatial alarms are triggered and delivered to the intended subscribers only when the subscribers move into the designated geographical vicinity of the spatial alarms, enabling delivering and sharing of relevant information and experience at the right location and the right time with the right subscribers. We studied how to use locality filters and subscriber filers to enhance the spatial alarm processing using traditional spatial indexing techniques. In addition, we develop a fast spatial alarm indexing structure and algorithms, called Mondrian Tree, and demonstrate that the Mondrian tree enabled spatial alarm system can significantly outperform existing spatial indexing based solutions such as R-tree, $k$-d tree, Quadtree.
This dissertation consists of six chapters. The first chapter introduces the research hypothesis. We describe our activity-based social influence model in Chapter 2. Chapter 3 presents the probabilistic social influence model powered with rewards incentives. We introduce spatial alarms and the basic system architecture for spatial alarm processing in Chapter 4. We describe the design of our Mondrian tree index of spatial alarms and alarm free regions in Chapter 5. In Chapter 6 we conclude the dissertation with a summary of the unique research contributions and a list of open issues closely relevant to the research problems and solution approaches presented in this dissertation.
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KIREEVA, TATIANA. "What strategies should Swedish news media outlets use in order to keep their brand credibility in times of social media?" Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296255.
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Journalism has “always been shaped by technology” (Pavlik, 2000, p.229). Social media could be seen as a technology of today, when news consumption is changing and 31% of the population worldwide are using Facebook and other social media platforms for news (Newman, 2020). Moreover, fewer pay for news (Medieakademin, 2021). Therefore, it is a challenge for news media outlets to adapt to the changing news consumption patterns, as well as they lose advertisers to Big Tech (Facht & Ohlsson, 2021). Furthermore, the credibility for Swedish news media brands is decreasing (Medieakademin, 2021). Here comes the question about the strategies that Swedish news media outlets should use in order to keep their brand credibility in times of social media. Sixteen scientists within media, communication, marketing and brand management were interviewed for this master thesis. Among the conclusions drawn are; the journalistic quality and principles such as objectivity and impartiality should go first; a new innovative business model should be found; social media has changed the landscape for traditional news media outlets; social media algorithms might have increased hostile media effects.Journalistiken har alltid påverkats av teknologin (Pavlik, 2000, p.229). Det är sociala medier som kan ses som “dagens teknologi”. Samtidigt ändras mönster för hur man konsumerar nyheterna, och 31% av världens befolkning använder Facebook och andra sociala medier för nyhetsläsning (Newman, 2020). Dessutom är det färre som betalar för nyheterna år för år (Medieakademin, 2021). Därför blir det en utmaning för nyhetsmedier att anpassa sig till de nya mönster samtidigt som de tappar i reklamintäkter, och annonsörerna går över till de stora techbolagen (Facht & Ohlsson, 2021). Därtill har förtroende för medierna de senaste tio åren minskat (Medieakademin, 2021), och här kommer frågan om hur ska svenska nyhetsmedier bete sig i en tid av sociala medier. För den här studien har sexton forskare inom media, kommunikation, marknadsföring och varumärke intervjuats. Enligt de slutsatser som dras, ska nyhetsmedierna satsa på kvalitativt innehåll; följa de journalistiska principerna; försöka hitta en ny affärsmodell; sociala medier har förändrat medielandskapet; algoritmer på sociala medier kan ha förstärkt hostile media effect.
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Söderberg, Britta. "Inside the echo chamber : A qualitative study on anti-immigration internet media, political polarization and social trust in a fragmented digital landscape." Thesis, Södertörns högskola, Medie- och kommunikationsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-31828.
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“Filter bubble” became one of the most topical words of 2016 and it was even included in the Swedish Language Council's list of new Swedish words that was presented at the end of the year. Referring to algorithmically adapted media bubbles, this phenomenon has particularly been discussed in relation to the UK Brexit referendum and the US election as scholars and journalist argue that the phenomenon, in combination with a fragmented media usage, contribute to a political polarization where each side of the political spectrum is encapsulated in “echo chambers” where opinions and beliefs are repeated like an echo rather than contested and challenged. In a Swedish context, filter bubbles and echo chambers have mainly been discussed in relation to anti-immigration internet media (AIIM), such as Avpixlat, Fria Tider and Exponerat, as these, through their critique of established journalistic media's (EJM) reporting, appear to constitute one side of a polarized debate around immigration. Through online interviews with 13 users of AIIM, this thesis is aimed at understanding why people consume such media and if the consumers are affected by echo chambers. Drawing on theories on online echo chambers and radical media critique, the study's findings suggest that even though the respondents’ appear to thrive on a siege mentality where anti-immigration groups are excluded sub-groups with AIIM as their only solution, the respondents' consumption of AIIM (and critique of EJM) is more likely to be based on a combination of a low level of trust in society and strong political (right-wing) beliefs. Furthermore, the study shows that the respondents are likely to be affected by both fragmentation and filter bubbles, but that they are not completely isolated in an anti-immigration media bubbles as they also rely on EJM's reporting in several ways.
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Rattay, Sonja. "Profiling Algorithms and Content Targeting - An Exploration of the Filter Bubble Phenomenon." Thesis, Malmö högskola, Fakulteten för kultur och samhälle (KS), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-22561.
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Etuk, Anthony Anietie. "Truth discovery under resource constraints." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=225779.
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Social computing initiatives that mark a shift from personal computing towards computations involving collective action, are driving a dramatic evolution in modern decision-making. Decisionmakers or stakeholders can now tap into the power of tremendous numbers and varieties of information sources (crowds), capable of providing information for decisions that could impact individual or collective well-being. More information sources does not necessarily translate to better information quality, however. Social influence in online environments, for example, may bias collective opinions. In addition, querying information sources may be costly, in terms of energy, bandwidth, delay overheads, etc., in real-world applications. In this research, we propose a general approach for truth discovery in resource constrained environments, where there is uncertainty regarding the trustworthiness of sources. First, we present a model of diversity, which allows a decision-maker to form groups, made up of sources likely to provide similar reports. We demonstrate that this mechanism is able to identify different forms of dependencies among information sources, and hence has the potential to mitigate the risk of double-counting evidence due to correlated biases among information sources. Secondly, we present a sampling decision-making model, which combines source diversification and reinforcement learning to drive sampling strategy. We demonstrate that this mechanism is effective in guiding sampling decisions given different task constraints or information needs. We evaluate our model by comparing it with algorithms representing classes of existing approaches reported in the literature.
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Freire, Claudia Pontes. "Método de monitoramento de redes sociais. Epistemologia, técnicas e propostas de mineração de banco de dados para conteúdos gerados por fãs de telenovela em redes sociais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/27/27152/tde-24112015-160556/.
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pesquisa de caráter epistemológico que tem como objeto de estudo contribuições epistemológicas, teórias e práticas oriundas do Campo da Comunicação para o método de monitoramento de redes sociais e técnicas de pesquisa. Objetivo geral propõe reflexões sobre o método de monitoramento de redes sociais aos pesquisadores do Campo da Comunicação proporcionando discussões e contribuições de cunho epistemológico, teórico e prático bem como apresenta limitações e implicações práticas sobre aplicações de técnicas de monitoramento em pesquisas de recepção da telenovela no Brasil. Objetivos específicos: (1) observar graus de autoridade apresentados por algoritmos em técnicas de monitoramento e quais técnicas são mais citadas no contexto de realização da atual pesquisa; (2) propor aplicações de técnicas de monitoramento para conteúdos gerados por fãs de telenovela em sites nos redes sociais, a saber Twitter, Facebook e YouTube. Hipótese de caráter epistemológico: citações de técnicas de monitoramento na Internet parecem esboçar comportamento semelhante ao de \"cauda longa\", seguindo as mesmas regras matemáticas da Lei de Pareto do ponto de vista de alusão ou referência às técnicas. Há citações de miríades de técnicas ao mesmo tempo em que há concentrações de citações que se direcionam para apenas 20% delas ou percentual aproximado. A concentração de citações indica o grau de autoridade de algoritmos que se dedicam à atividade de monitoramento. Do ponto de vista de autores que versam sobre técnicas será possível verificar a ocorrência hubs como resultado da concentração de domínios de conhecimento sobre volume e qualidade de referências às técnicas. Hipótese de caráter prático: combinações de técnicas gratuitas ou em modelo fremium empregadas para monitoramento e mineração de dados de CGU podem auxiliar ao estudo de fãs e estudos de recepção de telenovela. Metodologias quantitativa e qualitativa, a saber: análise de hiperlinks, análise de conteúdo, estudo de caso descritivo. Resultados: amostra inicial intencional bruta de 10.642 links a partir da qual se extraiu subamostra relevante e significativa de aproximadamente 1.579 links. Resultados: a cauda longa formada por 2.139 técnicas de monitoramento identificadas privilegia citações de técnicas revelando graus de autoridade entre algoritmos de monitoramento de conteúdos gerados por usuários em sites de redes sociais.epistemological research whose goal\'s remains in contributions derived from the communication field for the social media monitoring method (SMM) and techniques. The overall objective remains on a propose reflections about the social media monitoring method for communication researchers providing discussions about epistemological, theoretical and practical issues in a research field of telenovela fans. Objectives: (1) observe degrees of authority presented by present algorithms in monitoring techniques and which techniques are most frequently mentioned in the context of realization of such research; (2) propose applications of monitoring techniques for CGU research on social network sites: Twitter, Facebook and YouTube. Epistemological hypothesis: monitoring techniques quotes on the Internet seems like the \"long tail\" behavior, following the same mathematical rules of Pareto Law allusion point of view or reference about the techniques. There are myriad quotes techniques while there quotes concentrations at target in only 20% of them or something close. The concentration of quotes indicates the degree of algorithms that perform monitoring activities authority. From the point of view of authors who deals with monitoring techniques it will be possible to detect hubs as a result of the concentrations of domain knowledge about the volume and quality to the SMM technical references. b) Practical hypothesis: free technical combinations or model fremium employed for monitoring and mining CGU data can help the study of telenovela fans and reception studies. Quantitative and qualitative methodologies were applied: hyperlinks analysis, content analysis and descriptive case study. Results: intentional sample of 10,642 links from which were extracted relevant and significant subsample of 1,579 links. Results: the long tail of 2,139 social medida monitoring techniques founded privileges techniques quotes revealing degrees of authority between monitoring algorithms.
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Wurzer, Dominik Stefan. "Scaling real-time event detection to massive streams." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29013.
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In today’s world the internet and social media are omnipresent and information is accessible to everyone. This shifted the advantage from those who have access to information to those who do so first. Identifying new events as they emerge is of substantial value to financial institutions who consider realtime information in their decision making processes, as well as for journalists that report about breaking news and governmental agencies that collect information and respond to emergencies. First Story Detection is the task of identifying those documents in a stream of documents that talk about new events first. This seemingly simple task is non-trivial as the computational effort increases with every processed document. Standard approaches to solve First Story Detection determine a document’s novelty by comparing it to previously seen documents. This results in the highest reported accuracy but even the currently fastest system only scales to 10% of the Twitter stream. In this thesis, we propose a new algorithm family, called memory-based methods, able to scale to the full Twitter stream on a single core. Our memory-based method computes a document’s novelty up to two orders of magnitude faster than state-of-the-art systems without sacrificing accuracy. This thesis additional provides original work on the impact of processing unbounded data streams on detection accuracy. Our experiments reveal for the first time that the novelty scores of state-of-the-art comparison based and memory-based methods decay over time. We show how to counteract the discovered novelty decay and increase detection accuracy. Additionally, we show that memory-based methods are applicable beyond First Story Detection by building the first real time rumour detection system on social media streams.
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Dang, Mihn Huy. "Is seeing still believing?" Thesis, Umeå universitet, Designhögskolan vid Umeå universitet, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-182464.
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Social media content moderators perform a critical function of modern civic society: they act as first responders on social media platforms, getting confronted with harmful digital content on a daily basis.Without them, the Internet would be at the mercy of the unpredictable, global social climate, likely making the Internet a less safe, less peaceful place to be. Nevertheless, disturbing content screening cannot easily be addressed with the future’s planned full integration of artificial intelligence. Content moderation is not a technical challenge that can be solved by employing artificial intelligence as the solution to the content moderation related issues. Automated tools, for example, which are built to recognise creative permutations of language, fail their job. Cultural nuances and linguistic specificities are challenging to algorithms. For this reason, it is important to keep content moderation as a human process, because algorithms are not infallible in the content screening workflow and despite all the struggles content moderators face with screening content. The design concept explores how the emergence of deep fakes can be tackled by social media content moderators without relying too much on algorithms and computer power, but on human skills.
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Beridzishvili, Jumber. "When the state cannot deal with online content : Reviewing user-driven solutions that counter political disinformation on Facebook." Thesis, Malmö universitet, Malmö högskola, Institutionen för globala politiska studier (GPS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-18502.
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Online disinformation damage on the world’s democracy has been critical. Yet, states fail to handle online content harms. Due to exception from legal liability for hosted content, Facebook, used by a third of the world population, operates ‘duty-free’ along with other social media companies.Concerned with solutions, this has given rise to the idea in studies that social resistance could be one of the most effective ways for combating disinformation. However, how exactly do we resist, is an unsettled subject. Are there any socially-driven processes against disinformation happening out there?This paper aimed to identify such processes for giving a boost to theory-building around the topic. Two central evidence cases were developed: #IAmHere digital movement fighting disinformation and innovative tool ‘Who is Who’ for distinguishing fake accounts. Based on findings, I argue that efforts by even a very small part of society can have a significant impact on defeating online disinformation. This is because digital activism shares phenomenal particularities for shaping online political discourse around disinformation. Tools such as ‘Who is Who’, on the other hand, build social resilience against the issue, also giving boost digital activists for mass reporting of disinformation content. User-driven solutions have significant potential for further research.Keywords: Online disinformation; algorithms; digital activism; user-driven solutions.
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Reje, Franzén Fanny, and Saga Gardelin. "Hide and seek with algorithm : En intervjustudie av cosplay-kreatörers "folk" teorier i förhållande till TikToks algoritm." Thesis, Linnéuniversitetet, Institutionen för medier och journalistik (MJ), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104833.
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This essay aims to study the relationship between cosplay content creators and TikTok’s algorithm. To study this relationship the essay will conduct a qualitative semi-structured interviews with creators from the cosplay community on TikTok. Since the rise of digital plattforms the media and the role of producer as well as consumer has changed drastically. TikTok has been growing rapidly in popularity since its entry on the market, and by 2020 it had 500 million active users. Since many of today's digital platforms have consumer produced content, the consumer of today has taken on a mixed role between consuming and creating content, which creates a new relationship. The content consumers produce vary vastly on TikTok but one kind that has been present in much of TikTok’s existence is cosplay content. Cosplayers are creators who design costumes to already established characters or franchises. Since a discourse has started in the cosplay community on TikTok about the algorithm suppressing their content the study found it to be a good way to start examining content creators as individuals and how they behave towards an algorithm in their content creation process. The study aims to use algorithmic “folk” theory to examine what theories have been created in the community and how the theories affect the creators. The study also applies gatekeeping theory and social cognitive theory (SCT) to paint a clearer picture in how these creators view the algorithm. Seven interviews with cosplay content creators were conducted and with the help of a thematic analysis method the study found several themes in how the creators view and behave in relation to TikTok and its algorithm. The results of our study shows that there’s a definite present of “folk” theories created inside of the community. The most distinct behaviour relating to “folk” theory among the creators was that they can’t use the hashtag cosplay in the belief that the algorithm would suppress the content. This study concludes that the creators are more aware of the algorithm then they themself know and have different ways of working with and around it.
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Larsson, Matilda, and Nelly Nilsson. "Så styr algoritmer ditt flöde : En studie om personliga algoritmer inom sociala medier." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-44381.
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Sociala medier är ett fenomen som utvecklats drastiskt under de senaste 20 åren. Instagram och Facebook är två av de mest använda sociala medieplattformarna som har utvecklats markant med digitaliseringen. År 2016 ändrade Instagram sin algoritm, och 2018 gjorde Facebook samma sak. Algoritmerna syftar nu till att skräddarsy användarnas flöde för att visa det som är mest relevant till respektive konto och föra människor närmare varandra. Studien syftar till att undersöka hur Instagrams och Facebooks algoritmer fungerar och påverkar människor. Arbetet ska klargöra hur en algoritm uppfattas, hur plattformarna kan ha gått ifrån sina ursprungliga användningssyften och vilka etiska frågor som väckts till liv i processen. För att uppfylla studiens syfte skapas en undersökning utifrån tre generationer för att studera hur algoritmer påverkar människor och den roll som algoritmer spelar i samhället. Arbetet tar upp såväl positiva som negativa aspekter i relation till algoritmer. En kvalitativ undersökning med kvantifierbar data grundar strukturerade intervjuer i form av webbaserade frågeformulär. I ett första stadie ska respondenterna svara på ett frågeformulär. I nästa stadie ska respondenterna titta på dokumentärfilmen The Social Dilemma och sedan svara på efterföljande frågor i ett avslutande skede.
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Zhang, Ruiyang. "Cache Design for Massive Heterogeneous Data of Mobile Social Media." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175759.
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Since social media gains ever increasing popularity, Online Social Networks have become important repositories for information retrieval. The concept of social search, therefore, is gradually being recognized as the next breakthrough in this field, and it is expected to dominate topics in industry. However, retrieving information from OSNs with high Quality of Experience is non-trivial as a result of the prevalence of mobile applications for social networking services. For the sake of shortening user perceived latency Web caching was introduced and has been studied extensively for years. Nevertheless, the previous works seldom focus on the Web caching solutions for social search. In the context of this master’s thesis project, emphasis is given to the design of a Web caching system which is used to cache public data from social media with the objective of improving the user experience in terms of the freshness of data and the perceived service latency. To be more specific, a Web caching strategy named Staleness Bounded LRU algorithm is proposed to limit the term of validity of the cached data. In addition, a Two-Level Web Caching System that adopts the SB-LRU algorithm is proposed in order for shortening the user perceived latency. Results of trace-driven simulations and performance evaluations demonstrate that serving clients with stale data is avoided and the user perceived latencies are significantly shortened when the proposed Web caching system is used in the use case of unauthenticated social search. Besides, the design idea in this project is believed to be helpful to the design of a Web caching system for social search, which is capable of caching user specific data for different clients.
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Wright, Christopher M. "Using Statistical Methods to Determine Geolocation Via Twitter." TopSCHOLAR®, 2014. http://digitalcommons.wku.edu/theses/1372.
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With the ever expanding usage of social media websites such as Twitter, it is possible to use statistical inquires to form a geographic location of a person using solely the content of their tweets. According to a study done in 2010, Zhiyuan Cheng, was able to detect a location of a Twitter user within 100 miles of their actual location 51% of the time. While this may seem like an already significant find, this study was done while Twitter was still finding its ground to stand on. In 2010, Twitter had 75 million unique users registered, as of March 2013, Twitter has around 500 million unique users. In this thesis, my own dataset was collected and using Excel macros, a comparison of my results to that of Cheng’s will see if the results have changed over the three years since his study. If found to be that Cheng’s 51% can be shown more efficiently using a simpler methodology, this could have a significant impact on Homeland Security and cyber security measures.
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Hoteit, Tarek. "Effects of Investor Sentiment Using Social Media on Corporate Financial Distress." ScholarWorks, 2015. https://scholarworks.waldenu.edu/dissertations/464.
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The mainstream quantitative models in the finance literature have been ineffective in detecting possible bankruptcies during the 2007 to 2009 financial crisis. Coinciding with the same period, various researchers suggested that sentiments in social media can predict future events. The purpose of the study was to examine the relationship between investor sentiment within the social media and the financial distress of firms Grounded on the social amplification of risk framework that shows the media as an amplified channel for risk events, the central hypothesis of the study was that investor sentiments in the social media could predict t he level of financial distress of firms. Third quarter 2014 financial data and 66,038 public postings in the social media website Twitter were collected for 5,787 publicly held firms in the United States for this study. The Spearman rank correlation was applied using Altman Z-Score for measuring financial distress levels in corporate firms and Stanford natural language processing algorithm for detecting sentiment levels in the social media. The findings from the study suggested a non-significant relationship between investor sentiments in the social media and corporate financial distress, and, hence, did not support the research hypothesis. However, the model developed in this study for analyzing investor sentiments and corporate distress in firms is both original and extensible for future research and is also accessible as a low-cost solution for financial market sentiment analysis.
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Fahed, Nour. "The Dilemmal Socialization on Social Media Platforms : A Qualitative Study on the Experience of Online Socialization and the Infrastructure of Social Media Platforms." Thesis, Södertörns högskola, Medie- och kommunikationsvetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:sh:diva-46523.
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Social media effects may affect self-perception and the way media users live their offline lives. The purpose of this essay was to examine the phenomenon of social media saturation in order to understand the possible risks to the development of human identity during the adolescent period. Hence, these risks may be generated by being exposed to social comparison, cyberbullying, self-validation, and self-perception in a sensitive age when self-image is still fragile and being formed. The purpose of this essay is to examine the psychological tendencies of human beings while interacting with their peers on social media platforms. Hence, this will give us a clearer view of what would be achieved by conducting interviews. Moreover, a selection of theories will be applied to those interviews in order to associate those theories with what has been said by respondents. Hence, Meyrowitz’s theory will be used in relation to understanding the identity adaptation to online connection and linked to Goffman’s discussions of “onstage” and “backstage” (Meyrowitz, 1985: 5). After this, the essay will investigate how users’ self-perception and social comparison are enacted while socializing on social media platforms. Furthermore, this essay sheds the light on how identity is constructed online in the sense of belonging to a community on a social media platform as well as of gratification coming from peer validation in a virtual community. To be able to explain this, the “Social Identity Theory” will, therefore, be discussed (Teo, Matti, et al, 2017: 23). This will be discussed by mentioning theories like “Mediatization” (Couldry & Hepp, 2013). And lastly, the sociological concept of Habitus, minted by Pierre Bourdieu will demonstrate the process of adaptation towards unspoken social codes existing in virtual communities (Markham, 2017: 55). As found in the four qualitative semi-structured interviews with social media users, respondents are surrounding themselves with like-minded social groups which provide them with confidence about their own system of beliefs. Nevertheless, their perspectives are often marked by notable social pessimism and a lack of incentive to engage in conflictual interactions with others on social media. The results pointed out the perception among the interviewees that the impact of social media on identity formation is largely confined to adolescent users. Many users self-report significant daily screen time and are aware of the risks of social bubbles. Most of the respondents denied being subjected to cyberbullying, while they were surfing on social media, so the respondents’ physical lives were not affected by cyberbullying even for those who mentioned their exposure to cyberbullying. All the respondents expressed a sense of jealousy to some extent, even though some of them showed awareness of the thought that people post their lives from a perfect angle while hiding the flaws and not showing the imperfections of their lives on social media. Lastly, social comparison was an incentive feeling affected most of the respondents, and in their own experience, social media affected their character development and self-perception since they were exposed to social media at an adolescent age.
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Brudvik, Vigdis, and Agnes Törnerud. "Digital Musik och Algoritmer : En användarstudie om hur Spotify's algoritmer påverkar unga vuxnas musikkonsumtion." Thesis, Uppsala universitet, Institutionen för informatik och media, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446286.
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This study aims to examine how the generation of young adults who grew up in a digital society relate to algorithms that control them on Spotify, how they experience the personification on the platform and how the algorithms affect their flows. The purpose is also to investigate how young people experience and relate to the effects of Spotify's algorithms. The survey is based on six in-depth qualitative interviews conducted with young adults aged 20-30, that have been transcribed, discussed and analyzed using a thematic narrative analysis. The purpose of the study has been formulated according to the interview guide and the thematic narrative analysis. Results and analysis have been merged and there the respondents' intersubjective opinions and values are connected with the theoretical framework. The results show that users believe that algorithms mainly have had a positive impact on their music consumption. The majority of the respondents described that they prefer the custom-made playlists on Spotify (created by algorithms), and that they like to be presented with personalized content. However, few respondents stated that they notice or are aware that the algorithms control them, which means they are being guided towards certain music preferences without being aware of it. This will further be analyzed with the help of previous research and the theoretical framework.Denna studie ämnar undersöka hur den generation unga vuxna som vuxit upp i ett digitaliserat samhälle förhåller sig till de algoritmer som styr dem på Spotify, hur de upplever personifieringen på plattformen och hur algoritmerna påverkar deras flöden. Syftet är att undersöka och belysa hur unga vuxna upplever och förhåller sig till effekterna av Spotifys algoritmer i kontext till deras musikkonsumtion. Undersökningen baseras på sex stycken utförda kvalitativa djupintervjuer med unga vuxna i åldrarna 20-30 år som studerar vid Uppsala Universitet. Djupintervjuerna har transkriberats, kodats och därefter analyserats med hjälp av tematisk narrativ analys. Kapitlet för resultat och analys är hopslagna och där sammankopplas respondenternas narrativ beståendes av deras upplevelser och värderingar ihop med det teoretiska ramverket. Uppsatsen avslutas med ett avsnitt innehållandes slutdiskussion där även tidigare forskning vävs in. Resultatet visar att användare anser att algoritmer i huvudsak haft en positiv påverkan på deras musikkonsumtion. Majoriteten av respondenterna i denna studie beskriver att de föredrar Spotifys färdiggjorda spellistor (som skapats av algoritmer), och att de gärna tar del av ett personifierat innehåll. Problematiken är dock att få av respondenter utger sig för att vara fullt medvetna om algoritmerna och hur de styr dem, vilket innebär en risk att de lotsas mot vissa musikpreferenser utan att vara medvetna om det. Detta analyseras därför vidare med hjälp av tidigare forskning samt studiens teoretiska ramverk och nyckelbegrepp.
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Conzo, Naomi. "Privacy e "Social dilemma": aspetti etico-informatici legati al trattamento online dei dati personali sui social media." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021.
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Il fine di questo elaborato di tesi è analizzare per quali scopi e secondo quali metodologie le piattaforme online di Social Media trattano i nostri dati online. L'analisi sarà incentrata in primo luogo in luce delle regolamentazioni vigenti, quali tipi e come tutelano i dati in ambito privacy; seguirà un approfondimento delle informative sui dati (privacy policy) di alcuni importanti Social dal quale verranno estrapolati e riconosciuti concetti come la profilazione. Lo studio di che cosa è la profilazione avrà come scopo l'introduzioni di questioni molto importanti riguardo i sistemi utilizzati per profilare - sistemi che sono nel modo più assoluto automatizzati, composti da algoritmi sviluppati sulla base di "deep learning" e "machine learning" in modo da non richiedere l'intervento umano - le conseguenze sul piano etico-sociale e i problemi di sicurezza informatica che me derivano. Nella parte finale, in particolare, verrà fatto notare come l'atto dell'invio di contenuti personalizzati non sia stato seriamente considerato e come il GDPR tratta questa situazione.
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Sandberg, Sebastian. "Identifying Hateful Text on Social Media with Machine Learning Classifiers and Normalization Methods - Using Support Vector Machines and Naive Bayes Algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-155353.
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Hateful content on social media is a growing problem. In this thesis, machine learning algorithms and pre-processing methods have been combined in order to train classifiers in identifying hateful text on social media. The combinations have been compared in terms of performance, where the considered performance criteria have been F-score and accuracy in classification. Training are performed using Naive Bayes algorithm(NB) and Support Vector Machines (SVM). The pre-processing techniques that have been used are tokenization and normalization. Fortokenization, an open-source unigram tokenizer have been used while a normalization model that normalizes each tweet pre-classification have been developed in Java. Normalization include basic clean up methods such as removing stop words, URLs, and punctuation, as well as altering methods such as emoticon conversion and spell checking. Both binary and multi-class versions of the classifiers have been used on balanced and unbalanced data. Both machine learning algorithms perform on a reasonable level with accuracy between 76.70% and 93.55% and an F-score between 0.766 and 0.935. The results point towards the fact that the main purpose of normalization is to reduce noise, balancing data is necessary and that SVM seem to slightly outperform NB.
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Rambelli, Giorgio. "Classificazione di contenuti provenienti da Social Media tramite algoritmi di Machine Learning su architettura Big Data a supporto della Pubblica Amministrazione." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.
Find full textAbstract:
L’elaborato di tesi presenta un caso di studio nell’ambito del Data Mining tramite la realizzazione di un sistema per il riconoscimento di notizie non attendibili diffuse in rete.
Tale disciplina si pone l’obiettivo di identificare relazioni, pattern ricorrenti ed informazioni significative su grandi quantità di dati tramite l’applicazione di algoritmi e metodi statistici. Il processo di Data Mining può essere suddiviso in tre fasi principali: estrazione trasformazione e caricamento su una banca dati, effettiva analisi e rappresentazione grafica dei risultati ottenuti. Sfruttando strumenti e tecnologie sofisticati provenienti da altri campi quali Intelligenza Artificiale e Big Data, è possibile estrarre conoscenza approfondita, fornire predizioni sugli andamenti futuri e generare suggerimenti sulle base degli andamenti passati.
Il progetto in esame si colloca proprio in questo settore sfruttando algoritmi di Network Analysis e strumenti per la gestione di Big Data per valutare l’attendibilità del contenuto proveniente dai Social Media. Il prototipo è stato realizzato presso l’azienda Iconsulting S.p.A. su commissione del Corecom Emilia-Romagna (Comitato Regionale per le Comunicazioni) in seguito alle esigenze di un supporto informatico per contrastare il fenomeno delle Fake News, tema nei cui confronti la Pubblica Amministrazione dimostra particolare sensibilità.
L’obiettivo di realizzare un sistema per la valutazione automatica dell’attendibilità del contenuto digitale online può essere considerato raggiunto sotto ogni aspetto: il sistema è attualmente in funzione, il cliente ha valutato positivamente l’elaborato e l’azienda ospite sta valutando l’utilizzo di tale sistema per istituire un osservatorio tramite il quale organizzare incontri di discussione sul tema della comunicazione digitale.
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Bansal, Nilesh. "Online Analysis of High Volume Social Text Streams." Thesis, 2013. http://hdl.handle.net/1807/43485.
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Social media is one of the most disruptive developments of the past decade. The impact of this information revolution
has been fundamental on our society. Information dissemination has never been cheaper and users are increasingly
connected with each other. The line between content producers and consumers is blurred, leaving us with
abundance of data produced in real-time by users around the world on multitude of topics.
In this thesis we study techniques to aid an analyst in uncovering insights from this new media form which is modeled
as a high volume social text stream. The aim is to develop practical algorithms with focus on the ability to scale,
amenability to reliable operation, usability, and ease of implementation.
Our work lies at the intersection of building large scale real world systems and developing
theoretical foundation to support the same.
We identify three key predicates to enable online methods for analysis of social data, namely :
- Persistent Chatter Discovery to explore topics discussed over a period of time,
- Cross-referencing Media Sources to initiate analysis using a document as the query, and
- Contributor Understanding to create aggregate expertise and topic summaries of authors contributing online.
The thesis defines each of the predicates in detail and
covers proposed techniques, their practical applicability, and detailed experimental results to establish accuracy and
scalability for each of the three predicates.
We present BlogScope, the core data aggregation and management platform, developed as part of the thesis to enable
implementation of the key predicates in real world setting. The system provides a web based user interface for searching
social media conversations and analyzing the results in multitude of ways.
BlogScope, and its modified versions, index tens to hundreds
of billions of text documents while providing interactive query times. Specifically, BlogScope has been crawling 50 million
active blogs with 3.25 billion blog posts. Same techniques have also been successfully tested on a Twitter stream of data, adding
thousands of new Tweets every second and archiving over 30 billion documents.
The social graph part of our database consists of 26 million
Twitter user nodes with 17 billion follower edges. The BlogScope system has been used by over 10,000 unique visitors
a day, and the commercial version of the system is used by thousands of enterprise clients globally.
As social media continues to evolve at an exponential pace, there is a lot that still needs to be studied. The thesis concludes by
outlining some of possible future research directions.
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Bjernersjö, Mathilda, and Alicia Wikdahl. "Qualitative experiences of personalization algorithms : The strategies used by university students (to counteract ideological homogeneity) when navigating social media feeds and their experience with personalization algorithms." Thesis, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48214.
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Purpose – At the beginning of this year the number of social media user worldwide was recorded to be at 3.80 billion and is estimated to keep rising (Kemp, 2020). Along with this connectivity, new concepts have been acknowledged that have caused some controversy. These concepts have been named echo chambers and filter bubbles. This study investigates how social media users experience personalisation algorithms and the perceived existence of filter bubbles and echo chambers on various social media platforms and channels. Furthermore, it explores to what extent these users make use of strategies when navigating their social media feeds and if they believe that said strategies have any effect on personalisation algorithms, filter bubbles, and echo chambers. Method – Semi-structured interviews were conducted with 10 participants currently studying at Jönköping University. The interviews were then transcribed and analysed using a thematic approach. Findings – The perception of students at Jönköping University is that filter bubbles and echo chambers exist in a varying degree of visibility and commonality depending on the situation and medium. These are also experienced to be interconnected with personalisation algorithms. The experience of personalisation algorithms is mainly positive, as it allows the users to easily navigate the feed but negative implications are also mentioned. These are isolation, the negative strengthening and creation of filter bubbles or echo chambers. However, the algorithm is not believed to be the only thing responsible for the creation. The users believe that their own navigation and strategies can have such an effect. The strategies that users use are (1) Passive navigation, (2) Source evaluation, (3) Multiple source searching, (4) Responsibility taking, and the (5) Creation of filter bubbles. The effect these have on filter bubbles, personalisation algorithms and echo chambers is helping, to an extent, alleviate some of the negative effects that these are experienced to have. However, the passive navigation and creation of filter bubbles have a somewhat opposite effect as they help the creation or maintaining of filter bubbles and personalisation algorithms. Implications – The findings in this study build on existing evidence of echo chambers being more easily formed when the topic of discussion is of a political nature, as it is described by Barberá, Jost, Nagler, Tucker, and Bonneau (2015) in their research on how echo chambers form on Twitter. Furthermore, these findings could be considered to disagree with the research of Dubois and Blank (2018) who found that people who had a high political interest were less likely of ending up in an echo chamber. Although, due to their research taking all media into account, such as television, radio, newspaper, and so forth, while the current study is focused on social media alone the comparison is a bit more difficult to make. Finally, in the study made by Seargeant and Tagg (2019), it was concluded that the personalisation algorithms are not the sole contributor to filter bubbles forming on Facebook and that the users play a key role in how their online environment is shaped. The results of this study build on their research that the users do use strategies when navigating that affect what is being shown to them on their social media feeds. However, the participants of the current study claimed that the algorithms played a pretty large role too, which is not quite in line with the research conducted by Seargeant and Tagg (2019). Limitations – The generalisability of this study is limited due to the small sample size chosen to conduct this study, although instead, it provides deeper insight into the relationship between humans and their social media platforms and channels. Moreover, a bias that should be acknowledged is that when conducting interviews there is a risk of encountering response bias, which is when the participants assume the purpose of the study and adapt their answers to fit what they believe the researcher(s) want to hear. To avoid this precautions were taken when designing the questions to make sure that they would not lead the participants in any directions.
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"Breaking Hash-Tag Detection Algorithm for Social Media (Twitter)." Master's thesis, 2015. http://hdl.handle.net/2286/R.I.29838.
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abstract: In trading, volume is a measure of how much stock has been exchanged in a given period of time. Since every stock is distinctive and has an alternate measure of shares, volume can be contrasted with historical volume inside a stock to spot changes. It is likewise used to affirm value patterns, breakouts, and spot potential reversals. In my thesis, I hypothesize that the concept of trading volume can be extrapolated to social media (Twitter).
The ubiquity of social media, especially Twitter, in financial market has been overly resonant in the past couple of years. With the growth of its (Twitter) usage by news channels, financial experts and pandits, the global economy does seem to hinge on 140 characters. By analyzing the number of tweets hash tagged to a stock, a strong relation can be established between the number of people talking about it, to the trading volume of the stock.
In my work, I overt this relation and find a state of the breakout when the volume goes beyond a characterized support or resistance level.Dissertation/Thesis
Masters Thesis Computer Science 2015
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Fernandes, Mauricio Vidotti. "Grade of membership (GoM) por algoritmos genéticos." Master's thesis, 2015. http://hdl.handle.net/10362/15226.
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Muitas vezes é necessário trabalhar com variáveis categóricas, porem há um número restrito de análisesque as abordam. Uma boa técnica de segmentação é a grade of membership (GoM), muito utilizada na área médica, em psicologia e em sociologia. Essa metodologia possui uma interpretação interessante baseada em perfis extremos (segmentos) e grau de pertencimento. Porém o modelo possui grande complexidade de estimação dos parâmetros pormáxima verossimilhança. Assim, neste trabalho propõe-se o uso de algoritmos genéticos para diminuir a complexidade e o tempo de cálculo, e aumentar a acurácia. A técnica é nomeada de Genetics Algorithms grade of membership (GA-GoM). Para averiguar a efetividade, o modelo foi primeiramente abordado por simulação – foi executado um experimento fatorial levando em conta o número de segmentos e variáveis trabalhadas. Em seguida, foi abordado um caso prático de segmentação de engajamento em redes sociais. Os resultados são superiores para modelos de maior complexidade. Conclui-se, assim, que é útil a abordagem para grandes bases de dados que contenham dados categóricos.
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Magalhães, Renato Ferreira. "Validação de algoritmos de text mining em contexto de oncologia." Dissertação, 2020. https://hdl.handle.net/10216/129886.
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Magalhães, Renato Ferreira. "Validação de algoritmos de text mining em contexto de oncologia." Master's thesis, 2020. https://hdl.handle.net/10216/129886.
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Marques, Manuel Várzea Segurado. "Web push notifications como estratégia de angariação de público para a informação." Master's thesis, 2020. http://hdl.handle.net/10400.14/35441.
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A presente investigação realizada no âmbito do Mestrado de Ciências da Comunicação na
vertente de Internet e Novos Media tem como tema central os web push notifications,
enquanto estratégia de angariação de novos utilizadores para os websites que utilizam esta
nova ferramenta.
A parte teórica da dissertação refere a evolução constante do mundo digital, consequência
do aparecimento da Internet e a necessidade que existe das pessoas e empresas se adaptarem
a este novo processo, a digitalização. Como consequência do aparecimento da Internet o
mundo mudou, tal como a maneira de se fazer negócios, algo que introduz um novo modelo
de comunicação e um novo modelo de venda.
Também como consequência do aparecimento da Internet e modernização das empresas
surgem os novos modelos de negócio, tais como, os negócios através das redes sociais, mais
nomeadamente, os negócios feitos através do Facebook e Instagram. Porém, estes
apresentam um problema, o algoritmo, algo que se encontra em constante mutação, o que
torna o trabalho das empresas mais árduo no que diz respeito à angariação de tráfego para
os seus websites.
A componente empírica explora os novos modelos de angariação de tráfego utilizados pelas
empresas, tal como, os seus resultados e melhores práticas aquando da sua utilização.
As empresas necessitaram de arranjar uma solução às redes sociais visto que, estas tinham a
si associadas o carácter da imprevisibilidade, então, viraram-se para as notificações e assim,
surgiram os mobile notifications e os web push notifications como alternativas às redes
sociais, no que diz respeito à angariação de novos utilizadores/clientes.
A dissertação explora a novidade que os web push notifications introduziram nas empresas
e o seu papel neste novo modelo de fazer negócio. Para o efeito, foram analisados três casos
de estudos de três empresas digitais que utilizam estas notificações, o MSN, o jornal O
Público digital e o jornal O Sol digital, onde são analisadas as principais vantagens e
desvantagens destes web push notifications e mobile notifications e é também elaborado um
guia de “best practise” para utilizar esta nova ferramenta.This research carried out within the scope about Master of Science in Communication in the field of Internet and New Media has as its central theme web push notifications, as a strategy to attract new users to the websites that use this new tool. The theoretical part of the dissertation refers the constant evolution of the digital world, a consequence of the appearance of the Internet and the need that exists for people and companies to adapt to this new process, digitalization. As a consequence of the appearance of the Internet, the world has changed, as has the way of doing business, something that introduces a new model of communication and a new model of sales. Also, as a consequence of the emergence of the Internet and the modernization of the companies, new business models have emerged, such as business through social networks, more specifically, business done through Facebook and Instagram. However, they have a problem, the algorithm, something that is constantly changing, which makes the work of companies more arduous in terms of attracting traffic to their websites. The empirical component explores the new traffic-gathering models used by companies, as well as their results and best practices when using them. The companies needed to find a solution to social networks, since they had the character of unpredictability associated to them, so they turned to notifications and thus, mobile notifications and web push notifications emerged as alternatives to social networks, regarding to attract new users / customers. The dissertation explores the novelty that web push notifications introduced in companies and their role in this new model of doing business. For this purpose, three case studies of three digital companies that use these notifications were analyzed, MSN, the newspaper O Público digital and the newspaper O Sol digital, where the main advantages and disadvantages of these web push notifications and mobile notifications are analyzed and a best practices guide is also developed to use this new tool.
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HSIANG, CHUANG KAI, and 莊凱翔. "The prediction of trend toward stock price by text mining and sentiment analysis on social media: Using SVM and LDA Algorithm." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/mqp258.
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碩士國立高雄應用科技大學
資訊管理研究所碩士班
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In recent years, text mining is largely applied in the surrounding. this paper uses text mining to explore the social media content and uses the classification algorithm to predict future stock trends. In this paper, Using Latent Dirichlet Allocation and sentiment analysis and other text mining methods to analysis the social media context which is collected by the Internet, in addition, this study uses technical indicators to predict the stock price of Taiwan stock market, including Williams %R, Psychological Line and On Balance Volume…and many more, use the model to predict stock price movements. Through the LDA to establish the social media context topics and sentiment analysis of social media context to obtain the analysis results. then it through the support vector machine training to obtain the accuracy of social media context prediction, and compare influence of correct rate between sentiment analysis and topic vector.
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Hegedüš, Tomáš. "Algoritmizace sociálních sítí a její percepce očima mládeže." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-448314.
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The diploma thesis Algorithmization of social networks and its perception by adolescents deals with the issue of algorithms which lead to personalization of social networks. Their perception by children and adolescents is currently not sufficiently explored. In the theoretical part, I demonstrate how the current studies examine the perception of algorithms on a sample of the entire population, in the context of adolescents how they focus on social networks as a whole. Therefore, the ambition of this thesis is to research the intersection of these three spheres and its impact on adolescent's perception of algorithmisation of social networks. Through qualitative research using the method of semi-structured interviews, this thesis outlines trends that may prevail in this area. An analytical synthesis of knowledge also answers the questions of how adolescents are informed about the possibilities of influencing the selection and sorting of the content and whether they show interest in these possibilities. In the theoretical part, I introduce readers of this work to the issue of new media, how social media developed, including their convergence. Among the researched social networks are Facebook, Instagram and YouTube. To understand the comparison of reality and the statements of the respondents, in the...
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Simon, Alban Emmanuel Mazars. "The Wild in Live Project: A Human/Algorithm learning network to help citizen science in wildlife conservation." Master's thesis, 2019. http://hdl.handle.net/10316/88052.
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Dissertação de Mestrado em Ecologia apresentada à Faculdade de Ciências e Tecnologia************************************************************************************************************************************************************************************************Apesar dos recentes avanços, algumas das iniciativas de ciência cidadã estão ainda limitadas por múltiplos constrangimentos específicos à monitorização efectuada pelas pessoas que participam deste tipo de projectos e que podem impedir a qualidade dos dados recolhidos. No entanto, o advento de novas tecnologias está a revolucionar a recolha e o acesso a dados de conservação. O projeto Wild in Live teve como objetivo provar se tal tecnologia poderia ser usada em ciência cidadã como uma ferramenta para auxiliar na recolha de dados. Foi desnevolvido um algoritmo capaz de rastrear plataformas de redes sociais para categorizar e detectar tartarugas marinhas. O protótipo tem uma classificação com uma precisão de 95,53% e pode reconhecer tartarugas marinhas individuais dentro de um banco de dados considerável de 22 500 imagens. O resultado preliminar ofereceu uma boa visão do potencial inexplorado e da limitação da visão computacional para monitorar os ecossistemas. A recolha passiva de dados aumentará o escopo e a escala do monitorização de tartarugas marinhas, e o Wild in Live poderá tornar-se uma ferramenta para ajudar os pesquisadores a recolher mais informações, enquanto projetos de ciência cidadã poderiam usá-los para aumentar a conscientização para a conservação das espécies.***************************************************************************************************************************************************************************************
************************************************************************************************************************************************************************************************************************************************************************************************Despite the recent advances, citizen science is limited by multiple biases specific to human monitoring that can hamper the quality of the dataset. However, the advent of new technologies is revolutionising the generation and access to conservation data. The Wild in Live project aimed to prove if such technology could be used in citizen science as a tool to help the data collection. We, therefore, created an algorithm capable of scouting social media platforms to categorise and detect sea turtles. The prototype has a classification accuracy of 95.53% and can recognise individual sea turtles within a considerable database of 22 500 pictures. The preliminary result offered a good insight into the untapped potential and limitation of computer-vision to monitor ecosystems. The passive collection of data will increase the scope and scale of sea turtle monitoring, and Wild in Live project could become a tool to help researchers gather more information while citizen science projects could use it to raise awareness.**************************************************************************************************************************************************************************************************************************************************************************************
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"A paradigm shift of the trademark logo towards algorithmic justice: 邁向算法公義的商標圖案範式轉移." 2013. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291471.
Full textAbstract:
Unauthorised use of the trademark logo on social media on a massive scale has led to frequent legal conflicts between trademark holders, social media providers and internet users and legal uncertainty. The thesis concludes that trademark infringement and dilution laws in the U.S. and EU are inadequate to solve this problem. The trademark logo on social media should be protected against unauthorised use, even though it is not used to sell the good or service the trademark indicates, but is used in a commercial environment. Two premises for any solution are that first the enforcement should be made automatic, since litigation on a case-by-case basis is not scalable, and second that the safe harbour provisions for online service providers, that aggravate the problem, should be substituted for strict liability. The trademark logo can be seen as the personification of the trademark holder, and one can argue that the stability of the trademark logo is not only in the interest of the trademark holder but also of society at large. One can argue that trademark dilution already provides a kind of moral right of integrity for the trademark logo. However, this right is limited to trademark logos that are considered famous or have a reputation, and, moreover, that are used in a commercial way. This thesis argues that also the trademark logo that did not reach the requested level of fame or reputation and is used in a non-commercial way should also be protected against unauthorised use on social media. Therefore the moral right of integrity is proposed for the trademark logo. Until the law will be amended to include a moral right of integrity for the trademark logo, this thesis suggests to implement proactive solutions in the walled gardens of social media as a testing ground for potential legislation. This automated solution is scalable, makes intellectual property protection and enforcement not only effective but also more calibratable to social policy goals and will inevitable lead to an algorithmic justice.社交媒體上大規模未經授權使用的商標標誌為商標持有人、社交媒體提供商和互聯網用戶之間帶來法律衝突，更帶來法律上的不確定性。本文提出的結論是美國與歐盟有關商標被侵犯和稀釋的法律並不足以解決這個問題。縱使在社交媒體上的商標標誌未被用作銷售貨品或服務，但由於是在商業環境使用，所以法律應該保障其免被未經授權使用。任何解決方法都必須基於兩個前設。首先由於按個別情況興訟的可行性低，所以執法應該自動化。其二網絡服務供應商所依賴的避風港條款只會使問題更加嚴重，因此應該採用嚴格法律責任。商標標誌可被視為商標持有人的縮影，而商標標誌的穩定性除了對商標持有人有利之外，可謂對廣大社會也有裨益。法律所保障商標免被稀釋本身可說是一種對商標標誌完整性的精神權利的認可。但現時只有被認為著名，有聲譽，以及作商業用途的商標標誌才享有此權利。本文強調一般未達到著名或有聲譽，以及未作商業用途的商標標誌在社交媒體上應該受到同等免被未經授權使用的保障。就此，本文提出商標標誌應享有完整性的精神權利。在修法保障商標標誌完整性的精神權利前，本文建議在社交媒體這個空間裡採納積極的措施，以測試未來法律條文的可行性。此自動化的方案有可擴展性，使保障和執行知識產權既有效率，又可對準社會政策的目標，更必然會帶來算法公義。
Friedmann, Danny.
Thesis Ph.D. Chinese University of Hong Kong 2013.
Includes bibliographical references (leaves 354-380).
Abstracts also in Chinese.
Title from PDF title page (viewed on 05, October, 2016).
Friedmann, Danny.
Detailed summary in vernacular field only.
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Teixeira, António José dos Santos. "Sentiment analysis in retail: the case of Parfois facebook page." Master's thesis, 2017. http://hdl.handle.net/10071/15317.
Full textAbstract:
The way that consumers are interacting with brands is changing, and in Retail it is no
different. With the growth of internet usage and with all the social networks that we interact
with, social media is gaining more and more relevance and importance. This research extracted
1.845 posts, 8.256 comments and more than 500.000 reactions from Parfois Facebook page.
The comments were translated to English due to having comments made in several different
languages, modelled and finally made the sentiment analysis. This analysis was made
concerning the post dates, the reasons of the post and the products associated in the post.
It was used decision tree algorithms to predict sentiments, so it can be predicted the
sentiment when making a new post.
With the Sentiment Analysis from Social Media, Parfois can gain understanding about
their own brand, from the marketing department through to the buying or even design
departments. Using Social Media analysis together with Business Intelligence, can help Parfois
decision makers gain competitive advantage regarding their competitors or even improve their
products.A maneira como os consumidores interagem com as marcas está a mudar, e no retalho não é diferente. Com o aumento do uso da internet e com todas as redes sociais que interagimos, as redes sociais ganham mais relevância e importância. Esta pesquisa extraiu 1.845 posts, 8.256 comentários e mais de 500.000 reações da página de Facebook da Parfois. Os comentários foram traduzidos para o inglês devido ao fato de haver comentários feitos em várias línguas diferentes, modelados e finalmente feita a análise de sentimentos. Esta análise foi feita em relação às datas das publicações, os motivos do post, os produtos associados ao post. Foram utilizados algoritmos de árvores de decisão para prever sentimentos para que se possa prever o sentimento ao fazer um novo post. Com a analise de sentimentos das redes sociais, a Parfois pode entender melhor a sua própria marca, desde o departamento de marketing até ao departamento de compras ou mesmo o departamento de design. Usar a análise de sentimentos das redes sociais junto com o Business Intelligence organizacional, pode ajudar os decisores da Parfois a ganhar vantagem competitiva em relação aos concorrentes ou mesmo a melhorar seus produtos.