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Academic literature on the topic 'Social Networks - Data Processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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Journal articles on the topic "Social Networks - Data Processing"

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KRYVENCHUK, Yurii, and Mykhailo-Yurii KHANAS. "ALGORITHM OF DATA MINING AND PROCESSING OF RELATED DATA IN SOCIAL NETWORKS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 115–18. http://dx.doi.org/10.31891/2307-5732-2022-311-4-115-118.

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We live in a time of rapid growth of information technology, which is firmly entrenched in our daily lives. It is simply impossible to imagine a modern person without social networks, because they perform a communicative and informational function, namely: communication, information retrieval, news exchange, etc. Five hundred million tweets are posted daily, making Twitter a major social media platform from which topical information on events can be extracted. So, there is a lot of information available to the user, which is difficult to identify something specific and necessary in the usual way viewing. Accordingly, there is a need for technologies that can quickly process large amounts of data and highlight only the information that is useful to a particular user. This technology called recommender systems. It automatically suggest items to users that might be interesting for them. Due to the desire to unite people with common interests, it is relevant to develop a recommendation system based on social networks that help in personification of the user and compilation of his psychotype using his profile. The paper has description and results of the creation of recommendation system. The basis of this work is one of the algorithms used in recommendation systems – the recommendation system is based on content filtering. It analyzes users’ Twitter posts and calculates their interests. If we consider all the words, our model will not have good results and do not pay attention to what is important to use. Therefore, the most important step is always filtering data, so the number one task is to speed up the time of filtering text and retrieving data from the social network for further processing. The feature of this system is that this algorithm uses parallel calculations and frequency analysis of the text.

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Yang, Qing, Tigang Jiang, Wenjia Li, Guangchi Liu, Danda B. Rawat, and Jun Wu. "Editorial: Multimedia and Social Data Processing in Vehicular Networks." Mobile Networks and Applications 25, no. 2 (December 14, 2019): 620–22. http://dx.doi.org/10.1007/s11036-019-01432-2.

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MELNYK, K. "Processing and protection of personal data in social networks." INFORMATION AND LAW, no. 3(12) (December 23, 2014): 64–69. http://dx.doi.org/10.37750/2616-6798.2014.3(12).272566.

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Zadeh, Lotfi A., Ali M. Abbasov, and Shahnaz N. Shahbazova. "Fuzzy-Based Techniques in Human-Like Processing of Social Network Data." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 23, Suppl. 1 (December 2015): 1–14. http://dx.doi.org/10.1142/s0218488515400012.

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Social networks have gained a lot attention. They are perceived as a vast source of information about their users. Variety of different methods and techniques has been proposed to analyze these networks in order to extract valuable information about the users – things they do and like/dislike. A lot of effort is put into improvement of analytical methods in order to grasp a more accurate and detailed image of users. Such information would have an impact on many aspects of everyday life of people – from politics, via professional life, to shopping and entertainment. The theory of fuzzy sets and systems, introduced in 1965, has the ability to handle imprecise and ambiguous information, and to cope with linguistic terms. The theory has evolved into such areas like possibility theory and computing with words. It is very suitable for processing data in a human-like way, and providing the results in a human-oriented manner. The paper presents a short survey of works that use fuzzy-based technologies for analysis of social networks. We pose an idea that fuzzy-based techniques allow for introduction of humancentric and human-like data analysis processes. We include here detailed descriptions of a few target areas of social network analysis that could benefit from applications of fuzzy sets and systems methods.

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D’Ulizia, Arianna, Patrizia Grifoni, and Fernando Ferri. "Query Processing of Geosocial Data in Location-Based Social Networks." ISPRS International Journal of Geo-Information 11, no. 1 (December 30, 2021): 19. http://dx.doi.org/10.3390/ijgi11010019.

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The increasing use of social media and the recent advances in geo-positioning technologies have produced a great amount of geosocial data, consisting of spatial, textual, and social information, to be managed and queried. In this paper, we focus on the issue of query processing by providing a systematic literature review of geosocial data representations, query processing methods, and evaluation approaches published over the last two decades (2000–2020). The result of our analysis shows the categories of geosocial queries proposed by the surveyed studies, the query primitives and the kind of access method used to retrieve the result of the queries, the common evaluation metrics and datasets used to evaluate the performance of the query processing methods, and the main open challenges that should be faced in the near future. Due to the ongoing interest in this research topic, the results of this survey are valuable to many researchers and practitioners by gaining an in-depth understanding of the geosocial querying process and its applications and possible future perspectives.

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Persico, Valerio, Antonio Pescapé, Antonio Picariello, and Giancarlo Sperlí. "Benchmarking big data architectures for social networks data processing using public cloud platforms." Future Generation Computer Systems 89 (December 2018): 98–109. http://dx.doi.org/10.1016/j.future.2018.05.068.

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Bartolini, Ilaria, and Marco Patella. "Real-Time Stream Processing in Social Networks with RAM3S." Future Internet 11, no. 12 (November 29, 2019): 249. http://dx.doi.org/10.3390/fi11120249.

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The avalanche of (both user- and device-generated) multimedia data published in online social networks poses serious challenges to researchers seeking to analyze such data for many different tasks, like recommendation, event recognition, and so on. For some such tasks, the classical “batch” approach of big data analysis is not suitable, due to constraints of real-time or near-real-time processing. This led to the rise of stream processing big data platforms, like Storm and Flink, that are able to process data with a very low latency. However, this complicates the task of data analysis since any implementation has to deal with the technicalities of such platforms, like distributed processing, synchronization, node faults, etc. In this paper, we show how the RAM 3 S framework could be profitably used to easily implement a variety of applications (such as clothing recommendations, job suggestions, and alert generation for dangerous events), being independent of the particular stream processing big data platforms used. Indeed, by using RAM 3 S, researchers can concentrate on the development of their data analysis application, completely ignoring the details of the underlying platform.

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JI, CHANGQING, YU LI, WENMING QIU, YINGWEI JIN, YUJIE XU, UCHECHUKWU AWADA, KEQIU LI, and WENYU QU. "BIG DATA PROCESSING: BIG CHALLENGES AND OPPORTUNITIES." Journal of Interconnection Networks 13, no. 03n04 (September 2012): 1250009. http://dx.doi.org/10.1142/s0219265912500090.

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With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.

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Zhang, Qian, Jingyao Li, Hongyao Zhao, Quanqing Xu, Wei Lu, Jinliang Xiao, Fusheng Han, Chuanhui Yang, and Xiaoyong Du. "Efficient Distributed Transaction Processing in Heterogeneous Networks." Proceedings of the VLDB Endowment 16, no. 6 (February 2023): 1372–85. http://dx.doi.org/10.14778/3583140.3583153.

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Countrywide and worldwide business, like gaming and social networks, drives the popularity of inter-data-center transactions. To support inter-data-center transaction processing and data center fault tolerance simultaneously, existing protocols suffer from significant performance degradation due to high-latency and unstable networks. In this paper, we propose RedT, a novel distributed transaction processing protocol that works in heterogeneous networks. In detail, nodes within a data center are inter-connected via the RDMA-capable network and nodes across data centers are inter-connected via TCP/IP networks. RedT extends two-phase commit (2PC) by decomposing transactions into sub-transactions in terms of the data center granularity, and proposing a pre-write-log mechanism that is able to reduce the number of inter-data-center round-trips from a maximal of 6 to 2. Extensive evaluation against state-of-the-art protocols shows that RedT can achieve up to 1.57× higher throughputs and 0.56× lower latency.

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Pronichev, A. P. "Architecture of a distributed system for processing heterogeneous data from social networks." Informatization and communication 4 (November 2020): 97–100. http://dx.doi.org/10.34219/2078-8320-2020-11-4-97-100.

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The article discusses the architecture of a system for collecting and analyzing heterogeneous data from social networks. This architecture is a distributed system of subsystem modules, each of which is responsible for a separate task. The system also allows you to use external systems for data analysis, providing the necessary interface abstraction for connection. This allows for more flexible customization of the data analysis process and reduces development, implementation and support costs.

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Dissertations / Theses on the topic "Social Networks - Data Processing"

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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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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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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.

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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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Yelasani, kailash kumar yadav. "ECONOMIZED SENSOR DATA PROCESSING WITH VEHICLE PLATOONING." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2305.

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We present platooning as a special case of crowd-sensing framework. After offering a policy that governs platooning, we review common scenarios and components surrounding platooning. We present a prototype that illustrates efficiency of road usage and vehicle travel time derived from platooning. We have argued that beyond the commonly reported benefits of platooning, there are substantial savings in acquisition and processing of sensory data sharing the road. Our results show that data transmission can be reduced to low of 3% compared to normal data transmission using a platoon formation with sensor sharing.

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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.

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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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Li, Yafei. "Efficient group queries in location-based social networks." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/203.

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Nowadays, with the rapid development of GPS-equipped mobile devices, location-based social networks have been emerging to bridge the gap between the physical world and online social networking services. Various types of data, such as personal locations, check-ins, microblogs and social relations, have been available in location-based social networks. Efficiently managing and analyzing such data to meet users' daily query requirements become a challenging task. Among all the existing works in location-based social networks, group query is one of the most important research topics. In this thesis, we investigate query techniques for location-based services in social networking applications. Specifically, considering a location-based social network, we study spatial-aware interest group queries, geo-social {dollar}k{dollar}-cover group queries, and social-aware ridesharing group queries. Firstly, we study the spatial-aware interest group queries in location-based social networks. Recently, most of the location-based social networks release check-in services that allow users to share their visiting locations with their friends. These locations, considered as spatial objects, are usually associated with a few tags that describe the features of those locations. Utilizing such information, we propose a new type of \emph{Spatial-aware Interest Group} (SIG) query that retrieves a user group of size {dollar}k{dollar} where each user is interested in the query keywords and the users are close to each other in the Euclidean space. We prove this query problem is NP-complete, and develop two efficient algorithms IOAIR and DOAIR based on the IR-tree for the processing of SIG queries. We also validate the performance efficiency of the proposed query processing algorithms by empirical evaluation. Secondly, we study the problem of geo-social {dollar}k{dollar}-cover group queries for collaborative spatial computing. In this problem, we propose a novel type of geo-social queries, called \emph{Geo-Social K-Cover Group} (GSKCG) query, which is based on spatial containment and a new modeling of social relationships. Intuitively, given a set of spatial query points and an underlying social network, a GSKCG query finds a minimum user group in which the members satisfy certain social relationship and their associated regions can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets. Thirdly, we study the problem of social-aware ridesharing group queries. With the deep penetration of smartphones and geo-locating devices, ridesharing is envisioned as a promising solution to transportation-related problems such as congestion and air pollution for metropolitan cities. Despite the potential to provide significant societal and environmental benefits, ridesharing has not so far been as popular as expected. Notable barriers include the social discomfort and safety concerns when traveling with strangers. To overcome these barriers, in this thesis, we propose a new type of \emph{Social-aware Ridesharing Group} (SaRG) query which retrieves a group of riders by taking into account their social connections besides traditional spatial proximities. Because the SaRG query problem is NP-hard, we design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. We also present several incremental strategies to accelerate the search speed by reducing the repeated computations. Moreover, we propose a novel index tailored to the proposed problem to further speed up the query processing. Experimental results on real datasets show that our proposed algorithms achieve desirable performance. The works of this thesis show that the group query processing techniques are effective, which would facilitate the wider deployment of such query services in real applications

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Wang, Hao, and 王皓. "Advanced rank-aware queries and recommendation with novel types of data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206672.

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Nowadays we are living in an era of rich data, not only in the sense of the amount of data, but also in the sense of various sources and content of data. Efficient search, management, and exploitation of data have, over decades, been a major direction of database research. In this thesis, three challenging problems are proposed and studied, targeting (i) time series data, (ii) user preference data, and (iii) location-based social network data, respectively, providing efficient solutions to corresponding real-life applications. First, durability queries are studied in historical time series databases, which identify objects that have durable quality over time. For example, a sociologist may be interested in the top 10 web search terms during the period of some historical events; the police may seek for vehicles that move close to a suspect 70% of the time during a certain time, etc. Such durable top-k (DTop-k) and durable k-nearest neighbor (DkNN) queries can be viewed as natural extensions of the standard snapshot top-k and NN queries to timestamped sequences of values or locations. Although their snapshot counterparts have been studied extensively, there is little prior work that addresses this new class of durability queries. Efficient and scalable algorithms are proposed based on novel indexing techniques. Next, an efficient solution to k-nearest neighbor search over top-m lists is investigated. A top-m list is a ranking of m items, typically representing some user’s preference over these items. For example, a user may have a list of her 10 most favourite books; the result from a search engine is typically a list of webpages ranked according to their relevance to some keywords. The search problem aims at extracting k top-m lists from the database that are the “closest” to some query list where the closeness is evaluated using commonly used measures such as the Fagin’s intersection metric, Spearman’s footrule, Kendall’s tau, etc. Despite of the importance of such queries, there’s little prior work suggesting any efficient solution. In this thesis, a unified framework is proposed to answer such queries efficiently. Finally, the problem of top-N venue recommendation in location-based social networks (LBSNs) is studied, which recommends new venues to users. As an increasingly larger number of users partake in LBSNs, the recommendation problem in this setting has attracted significant attention in research and in practical applications. The detailed information about past user behavior that is traced by the LBSN differentiates the problem significantly from its traditional settings. The spatial nature in the past user behavior and also the information about the user social interaction with other users, provide a richer background to build a more accurate and expressive recommendation model. Although there have been extensive studies on recommender systems working with user-item ratings, GPS trajectories, and other types of data, there are very few approaches that exploit the unique properties of the LBSN user check-in data. In this thesis, effective and efficient algorithms that create recommendations are proposed based on such properties.
published_or_final_version
Computer Science
Doctoral
Doctor of Philosophy

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Zhao, Xiaoyun. "Road network and GPS tracking with data processing and quality assessment." Licentiate thesis, Högskolan Dalarna, Mikrodataanalys, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:du-17354.

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GPS technology has been embedded into portable, low-cost electronic devices nowadays to track the movements of mobile objects. This implication has greatly impacted the transportation field by creating a novel and rich source of traffic data on the road network. Although the promise offered by GPS devices to overcome problems like underreporting, respondent fatigue, inaccuracies and other human errors in data collection is significant; the technology is still relatively new that it raises many issues for potential users. These issues tend to revolve around the following areas: reliability, data processing and the related application. This thesis aims to study the GPS tracking form the methodological, technical and practical aspects. It first evaluates the reliability of GPS based traffic data based on data from an experiment containing three different traffic modes (car, bike and bus) traveling along the road network. It then outline the general procedure for processing GPS tracking data and discuss related issues that are uncovered by using real-world GPS tracking data of 316 cars. Thirdly, it investigates the influence of road network density in finding optimal location for enhancing travel efficiency and decreasing travel cost. The results show that the geographical positioning is reliable. Velocity is slightly underestimated, whereas altitude measurements are unreliable.Post processing techniques with auxiliary information is found necessary and important when solving the inaccuracy of GPS data. The densities of the road network influence the finding of optimal locations. The influence will stabilize at a certain level and do not deteriorate when the node density is higher.

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Schlenkrich, Lara. "An investigation of social computing." Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1006194.

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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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Igboayaka, Jane-Vivian Chinelo Ezinne. "Using Social Media Networks for Measuring Consumer Confidence: Problems, Issues and Prospects." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32341.

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This research examines the confluence of consumers’ use of social media to share information with the ever-present need for innovative research that yields insight into consumers’ economic decisions. Social media networks have become ubiquitous in the new millennium. These networks, including, among others: Facebook, Twitter, Blog, and Reddit, are brimming with conversations on an expansive array of topics between people, private and public organizations, governments and global institutions. Preliminary findings from initial research confirms the existence of online conversations and posts related to matters of personal finance and consumers’ economic outlook. Meanwhile, the Consumer Confidence Index (CCI) continues to make headline news. The issue of consumer confidence (or sentiment) in anticipating future economic activity generates significant interest from major players in the news media industry, who scrutinize its every detail and report its implications for key players in the economy. Though the CCI originated in the United States in 1946, variants of the survey are now used to track and measure consumer confidence in nations worldwide. In light of the fact that the CCI is a quantified representation of consumer sentiments, it is possible that the level of confidence consumers have in the economy could be deduced by tracking the sentiments or opinions they express in social media posts. Systematic study of these posts could then be transformed into insights that could improve the accuracy of an index like the CCI. Herein lies the focus of the current research—to analyze the attributes of data from social media posts, in order to assess their capacity to generate insights that are novel and/or complementary to traditional CCI methods. The link between data gained from social media and the survey-based CCI is perhaps not an obvious one. But our research will use a data extraction tool called NetBase Insight Workbench to mine data from the social media networks and then apply natural language processing to analyze the social media content. Also, KH Coder software will be used to perform a set of statistical analyses on samples of social media posts to examine the co-occurrence and clustering of words. The findings will be used to expose the strengths and weaknesses of the data and to assess the validity and cohesion of the NetBase data extraction tool and its suitability for future research. In conclusion, our research findings support the analysis of opinions expressed in social media posts as a complement to traditional survey-based CCI approaches. Our findings also identified a key weakness with regards to the degree of ‘noisiness’ of the data. Although this could be attributed to the ‘modeling’ error of the data mining tool, there is room for improvement in the area of association—of discerning the context and intention of posts in online conversations.

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Books on the topic "Social Networks - Data Processing"

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Ting, I.-Hsien, Hui-Ju Wu, and Tien-Hwa Ho. Mining and analyzing social networks. Berlin: Springer-Verlag, 2010.

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service), SpringerLink (Online, ed. Social Network Data Analytics. Boston, MA: Springer Science+Business Media, LLC, 2011.

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Garson, G. David. Neural networks: An introductory guide for social scientists. London: Sage, 1998.

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Abraham, Ajith. Computational Social Networks: Mining and Visualization. London: Springer London, 2012.

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Makar, Joos Irene, and Wolf Debra M, eds. Social media for nurses: Educating practitioners and patients in a networked world. New York, NY: Springer Pub. Co., 2013.

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Writers.net: Every writer's essential guide to online resources and opportunities. Rocklin, Calif: Prima Pub., 1997.

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Gach, Gary. Writers.net: Every writer's essential guide to online resources and opportunities. Rocklin, Calif: Prima Pub, 1996.

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Political networks: The structural perspective. Cambridge [England]: Cambridge University Press, 1990.

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Lazakidou, Athina A. Virtual Communities, Social Networks and Collaboration. New York, NY: Springer New York, 2012.

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Bernal, Joey. Web 2.0 and social networking for the enterprise: Guidelines and examples for implementation and management within your organization. Indianapolis, Ind: IBM Press, 2009.

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Book chapters on the topic "Social Networks - Data Processing"

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Du, Hongbin George, and Yanke Hu. "SqueezeBioBERT: BioBERT Distillation for Healthcare Natural Language Processing." In Computational Data and Social Networks, 193–201. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8_16.

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Nguyen, Bich-Ngan T., Phuong N. H. Pham, Loi H. Tran, Canh V. Pham, and Václav Snášel. "Fairness Budget Distribution for Influence Maximization in Online Social Networks." In Artificial Intelligence in Data and Big Data Processing, 225–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97610-1_19.

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Bank, Mathias, and Juergen Franke. "Social Networks as Data Source for Recommendation Systems." In Lecture Notes in Business Information Processing, 49–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15208-5_5.

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Kazienko, Przemysław, Wojciech Indyk, Tomasz Kajdanowicz, and Roman Bartusiak. "Distributed Processing of Networked Data." In Encyclopedia of Social Network Analysis and Mining, 1–7. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_258-1.

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Kazienko, Przemysław, Wojciech Indyk, and Tomasz Kajdanowicz. "Distributed Processing of Networked Data." In Encyclopedia of Social Network Analysis and Mining, 400–403. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_258.

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Kazienko, Przemysław, Wojciech Indyk, Tomasz Kajdanowicz, and Roman Bartusiak. "Distributed Processing of Networked Data." In Encyclopedia of Social Network Analysis and Mining, 662–68. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_258.

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Pereira, Fabíola S. F., Shazia Tabassum, João Gama, Sandra de Amo, and Gina M. B. Oliveira. "Processing Evolving Social Networks for Change Detection Based on Centrality Measures." In Studies in Big Data, 155–76. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89803-2_7.

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Mukhina, Ksenia, Alexander Visheratin, and Denis Nasonov. "Spatiotemporal Filtering Pipeline for Efficient Social Networks Data Processing Algorithms." In Lecture Notes in Computer Science, 86–99. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50433-5_7.

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Rustum, Rubeena, J. Kavitha, P. V. R. D. Prasada Rao, Jajjara Bhargav, and G. Charles Babu. "Customer Engagement Through Social Media and Big Data Pipeline." In Third International Conference on Image Processing and Capsule Networks, 599–608. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12413-6_47.

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Ninggal, Mohd Izuan Hafez, and Jemal Abawajy. "Privacy Threat Analysis of Social Network Data." In Algorithms and Architectures for Parallel Processing, 165–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24669-2_16.

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Conference papers on the topic "Social Networks - Data Processing"

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Kurbatov, Yuriy, Igor Rytsarev, and Alexander Kupriyanov. "Research Of Text Data Processing Algorithms In Social Networks." In 2020 International Conference on Information Technology and Nanotechnology (ITNT). IEEE, 2020. http://dx.doi.org/10.1109/itnt49337.2020.9253271.

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Ameen, Aso Khaleel, and Buket Kaya. "Spam detection in online social networks by deep learning." In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620910.

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Wang, Feng, Wenjun Jiang, Guojun Wang, and Dongqing Xie. "Data-Driven Influence Learning in Social Networks." In 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC). IEEE, 2017. http://dx.doi.org/10.1109/ispa/iucc.2017.00177.

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"International Workshop on Social Networks and Data Processing (SNDP 2012) [committees]." In 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2012. http://dx.doi.org/10.1109/wi-iat.2012.306.

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Al-Baghdadi, Ahmed, Gokarna Sharma, and Xiang Lian. "Efficient Processing of Group Planning Queries Over Spatial-Social Networks (Extended Abstract)." In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2023. http://dx.doi.org/10.1109/icde55515.2023.00320.

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Jinha Kim, Seung-Keol Kim, and Hwanjo Yu. "Scalable and parallelizable processing of influence maximization for large-scale social networks?" In 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544831.

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Mungen, Ahmet Anil, and Mehmet Kaya. "Influence analysis of posts in social networks by using quad-motifs." In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2017. http://dx.doi.org/10.1109/idap.2017.8090218.

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Dogan, Emre, and Buket Kaya. "Deep Learning Based Sentiment Analysis and Text Summarization in Social Networks." In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2019. http://dx.doi.org/10.1109/idap.2019.8875879.

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Liu, Liu, Jiangtao Yin, and Lixin Gao. "Efficient social network data query processing on MapReduce." In the 5th ACM workshop. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2491159.2491169.

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Chang, Yafei, Hejiao Huang, Qin Liu, and Xiaohua Jia. "Scalable and Parallel Processing of Influence Maximization for Large-Scale Social Networks." In 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM). IEEE, 2017. http://dx.doi.org/10.1109/bigcom.2017.28.

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Reports on the topic "Social Networks - Data Processing"

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Kouri, Tina. Exploiting Social Media Sensor Networks through Novel Data Fusion Techniques. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1431510.

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de Paula, Áureo, Pedro CL Souza, and Imran Rasul. Recovering social networks from panel data: identification, simulations and an application. The IFS, March 2018. http://dx.doi.org/10.1920/wp.cem.2018.1718.

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Rasul, Imran, Áureo de Paula, and Pedro CL Souza. Recovering social networks from panel data: identification, simulations and an application. The IFS, October 2018. http://dx.doi.org/10.1920/wp.cem.2018.5818.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Fort Belvoir, VA: Defense Technical Information Center, June 1992. http://dx.doi.org/10.21236/ada251567.

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Beer, Randall D. Neural Networks for Real-Time Sensory Data Processing and Sensorimotor Control. Fort Belvoir, VA: Defense Technical Information Center, December 1992. http://dx.doi.org/10.21236/ada259120.

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Malde, Bansi, and Arun Advani. Empirical methods for networks data: social effects, network formation and measurement error. IFS, December 2014. http://dx.doi.org/10.1920/wp.ifs.2014.1434.

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Wicker, Steven B. Self-Configuring Wireless Transmission and Decentralized Data Processing for Generic Sensor Networks. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada425425.

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Levitan, Herbert. Microcomputer-Based Data Acquisition, Analysis and Control of Information Processing by Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, November 1986. http://dx.doi.org/10.21236/ada177170.

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Oppel, Annalena. Beyond Informal Social Protection – Personal Networks of Economic Support in Namibia. Institute of Development Studies (IDS), November 2020. http://dx.doi.org/10.19088/ids.2020.002.

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Abstract:

This paper poses a different lens on informal social protection (ISP). ISP is generally understood as practices of livelihood support among individuals. While studies have explored the social dynamics of such, they rarely do so beyond the conceptual space of informalities and poverty. For instance, they discuss aspects of inclusion, incentives and disincentives, efficiency and adequacy. This provides important insights on whether and to what extent these practices provide livelihood support and for whom. However, doing so in part disregards the socio-political context within which support practices take place. This paper therefore introduces the lens of between-group inequality through the Black Tax narrative. It draws on unique mixed method data of 205 personal support networks of Namibian adults. The results show how understanding these practices beyond the lens of informal social protection can provide important insights on how economic inequality resonates in support relationships, which in turn can play a part in reproducing the inequalities to which they respond.

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Dannemann Dugick, Fransiska, Sarah Albert, Gil Averbuch, and Stephen Arrowsmith. Utilizing the Dynamic Networks Data Processing and Analysis Experiment (DNE18) to Establish Methodologies for the Comparison of Automatic Infrasonic Signal Detectors. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1832306.

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