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Journal articles on the topic 'Online social data'

Author: Grafiati

Published: 10 January 2023

Last updated: 28 January 2023

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1

Phithakkitnukoon, Santi. "Sensing Urban Social Geography Using Online Social Networking Data." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 3 (August 3, 2021): 36–39. http://dx.doi.org/10.1609/icwsm.v5i3.14213.

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Growing pool of public-generated bits like online social networking data provides possibility to sense social dynamics in the urban space. In this position paper, we use a location-based online social networking data to sense geo-social activity and analyze the underlying social activity distribution of three different cities: London, Paris, and New York. We find a non-linear distribution of social activity, which follows the Power Law decay function. We perform inter-urban analysis based on social activity distribution and clustering. We believe that our study sheds new light on context-aware urban computing and social sensing.

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Wilk, Violetta, Geoffrey N. Soutar, and Paul Harrigan. "Tackling social media data analysis." Qualitative Market Research: An International Journal 22, no. 2 (April 8, 2019): 94–113. http://dx.doi.org/10.1108/qmr-01-2017-0021.

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PurposeThis paper aims to offer insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyze big, text-based, online data taken from consumer-to-consumer (C2C) social media communication.Design/methodology/approachThis study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an online open community and an online brand community that involved online brand advocacy (OBA). The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented.FindingsThis research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to OBA.Research limitations/implicationsQualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The “visual-first” analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.Practical implicationsThe paper provides practical recommendations for analysts considering which programs to use when exploring big, text-based, online data.Originality/valueThis paper answered a call to action for further research and demonstration of analytical programs of big, online data from social media C2C communication and makes strong suggestions about the need to examine such data in a number of ways.

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Giglietto, Fabio, and Luca Rossi. "Limiti e possibilità degli online social data." SOCIOLOGIA DELLA COMUNICAZIONE, no. 49 (September 2015): 9–18. http://dx.doi.org/10.3280/sc2015-049002.

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Binder, Jens F., Sarah L. Buglass, Lucy R. Betts, and Jean D. M. Underwood. "Online social network data as sociometric markers." American Psychologist 72, no. 7 (October 2017): 668–78. http://dx.doi.org/10.1037/amp0000052.

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Sathya, R., and A. Aruna devi. "Data Mining and Analysis of Online Social Networks." International Journal of Business Intelligents 004, no. 001 (June 15, 2015): 18–21. http://dx.doi.org/10.20894/ijbi.105.004.001.005.

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Alim, Sophia. "Automated Data Extraction from Online Social Network Profiles." International Journal of Virtual Communities and Social Networking 5, no. 4 (October 2013): 24–42. http://dx.doi.org/10.4018/ijvcsn.2013100102.

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As the use of online social networking (OSN) sites is increasing, data extraction from OSN profiles is providing researchers with a rich source of data. Data extraction is divided into non-automated and automated approaches. However, researchers face a variety of ethical challenges especially using automated data extraction approaches. In social networking, there has been a lack of research that looks into the unique ethical challenges of using automated data extraction compared to non-automated extraction. This article explores the history of social research ethics and the unique ethical challenges associated with using automated data extraction, as well as how these impact the researcher. The author's review has highlighted that researchers face challenges when designing an experiment involving automated extraction from OSN profiles due to issues such as extraction methods, the speed at which the field of social media is moving and a lack of information on how to deal with ethical challenges.

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ZHOU, Jingya, Jianxi FAN, and Jin WANG. "Data placement approach for scalable online social networks." SCIENTIA SINICA Informationis 48, no. 3 (February 13, 2018): 329–48. http://dx.doi.org/10.1360/n112017-00064.

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Li, Na, Nan Zhang, and Sajal Das. "Preserving Relation Privacy in Online Social Network Data." IEEE Internet Computing 15, no. 3 (May 2011): 35–42. http://dx.doi.org/10.1109/mic.2011.26.

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Hoque, Imranul, and Indranil Gupta. "Disk Layout Techniques for Online Social Network Data." IEEE Internet Computing 16, no. 3 (May 2012): 24–36. http://dx.doi.org/10.1109/mic.2012.40.

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Verbraken, Thomas, Frank Goethals, Wouter Verbeke, and Bart Baesens. "Predicting online channel acceptance with social network data." Decision Support Systems 63 (July 2014): 104–14. http://dx.doi.org/10.1016/j.dss.2013.08.011.

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Hawkins, C. Matthew. "Radiology’s Social Media Hashtag Ontology: Codifying Online Data." Journal of the American College of Radiology 13, no. 1 (January 2016): 111–13. http://dx.doi.org/10.1016/j.jacr.2015.08.028.

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Ovadia, Steven. "Finding Data Sets Online." Behavioral & Social Sciences Librarian 29, no. 1 (February 24, 2010): 81–85. http://dx.doi.org/10.1080/01639260903572342.

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Fernando de Assis Rodrigues and Ricardo Cesar Gonçalves Sant'Ana. "PRIVACY AND ONLINE SOCIAL NETWORK." Brazilian Journal of Information Science: research trends 17 (January 19, 2023): e0230005. http://dx.doi.org/10.36311/1981-1640.2023.v17.e0230005.

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The popularization of Information and Communication Technologies allowed new forms of interaction, including Online Social Network (OSN) services. These services could share personal data with third-party organizations through Application Programming Interfaces (API), making it a complex task to observe the data flow. This research proposes a model to identify levels and elements available in the flow of personal data via API between organizations holding OSN services and external agents. It converged the recurrent mechanisms into providing data sets from the OSN services to external agents from a systematized reading of the technical documentation to establish a database model to fetch unitedly the possibility of storing information about the levels in which API provided personal data to external agents, applying the Entity-Relationship Model. Subsequently, it presents a Data Mart as a proof of concept for the proposed database model, intending to compare the ways of accessing the personal data attributes stored and, following, shared by the OSN services to external agents. It showed that the model could clarify the elements inherent in the data flow, allowing a more structured analysis, including the possibility of monitoring changes in the API by organizations over time.

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Kumaran P. and Chitrakala S. "Social influence determination on big data streams in an online social network." Multimedia Tools and Applications 76, no. 21 (June 27, 2017): 22133–67. http://dx.doi.org/10.1007/s11042-017-4890-8.

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Korshunov, Ilya, and Natalia Shirkova. "Vocational Education with Social Distancing: Data from Online Platforms." Education and Self Development 17, no. 2 (June 30, 2022): 203–18. http://dx.doi.org/10.26907/esd.17.2.16.

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This is a study of how vocational education was transformed to deal with social distancing due to coronavirus infection in the Russian Federation. Data was taken from online delivery platforms such as Coursera, Timepad, and Edumarket. The demand for educational courses in Russia increased by 2.5 times in the first half of 2020 due to the spread of COVID-19. Despite this only 19% of learners are prepared to continue their learning after the quarantine measures are lifted. In the first half of 2020 programs on these platforms were focused on soft skills. There was an increase in programs for stress resistance, leadership, productivity, business, hobbies and art, parent-child relationships, and foreign language learning. The proportion of programs reliant on social communications decreased significantly. However, there was an increase courses in IT (blog / website development, machine learning), and medicine (ensuring safety in case of coronavirus infection, prevention of COVID-19, etc.). The study is of interest to the heads of regional educational departments, specialists of the continuing education in universities, professional educational organizations, non-governmental organizations of continuing education, labor exchange and those who are involved in continuing education and self-learning.

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Alim, Sophia, Ruqayya Abdulrahman, Daniel Neagu, and Mick Ridley. "Online social network profile data extraction for vulnerability analysis." International Journal of Internet Technology and Secured Transactions 3, no. 2 (2011): 194. http://dx.doi.org/10.1504/ijitst.2011.039778.

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Chang, Wei, and Jie Wu. "Privacy-preserved data publishing of evolving online social networks." Journal of Information Privacy and Security 12, no. 1 (January 2, 2016): 14–31. http://dx.doi.org/10.1080/15536548.2016.1143765.

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Shetty, Nisha P., Balachandra Muniyal, and Saleh Mowla. "Policy resolution of shared data in online social networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3767. http://dx.doi.org/10.11591/ijece.v10i4.pp3767-3776.

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Online social networks have practically a go-to source for information divulging, social exchanges and finding new friends. The popularity of such sites is so profound that they are widely used by people belonging to different age groups and various regions. Widespread use of such sites has given rise to privacy and security issues. This paper proposes a set of rules to be incorporated to safeguard the privacy policies of related users while sharing information and other forms of media online. The proposed access control network takes into account the content sensitivity and confidence level of the accessor to resolve the conflicting privacy policies of the co-owners.

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Zhang, Honggang, Benyuan Liu, Bin Nie, Zhiyong Xu, Xiayin Weng, and Chao Yu. "Leveraging online social friendship to improve data swarming performance." Computer Networks 71 (October 2014): 130–43. http://dx.doi.org/10.1016/j.comnet.2014.06.012.

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Al-Saggaf, Yeslam. "Understanding Online Radicalisation Using Data Science." International Journal of Cyber Warfare and Terrorism 6, no. 4 (October 2016): 13–27. http://dx.doi.org/10.4018/ijcwt.2016100102.

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What characterises social media radicals? And why some people become attracted to radicalisation? To explore answers to these questions, a number of tweets posted by a group of suspected radicals tweeting in Arabic were analysed using social network analysis and machine learning. The study revealed that these suspected radicals' networks showed significant interaction with others; but this interactivity is only significant quantitatively as the interaction is not reciprocated. With regards to why these suspected radicals became attracted to radicalisation, Topic Modelling revealed these suspected radicals' tweets underpinned a perceived injustice that they believed the Secret Police and the Government inflicted upon them. Overall, the study has shown that data science tools have the potential to inform our understanding of online radicalisation. It is hoped this exploratory study will be the basis for a future study in which the research questions will be answered using a larger sample.

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Maireder, Axel, Brian E. Weeks, Homero Gil de Zúñiga, and Stephan Schlögl. "Big Data and Political Social Networks." Social Science Computer Review 35, no. 1 (August 3, 2016): 126–41. http://dx.doi.org/10.1177/0894439315617262.

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Social media have changed the way citizens, journalists, institutions, and activists communicate about social and political issues. However, questions remain about how information is diffused through these networks and the degree to which each of these actors is influential in communicating information. In this study, we introduce two novel social network measures of connection and information diffusion that help shed light on patterns of political communication online. The Audience Diversity Score assesses the diversity of a particular actor’s followers and identifies which actors reach different publics with their messages. The Communication Connector Bridging Score highlights the most influential actors in the network who are potentially able to connect different spheres of communication through their information diffusion. We apply and discuss these measures using Twitter data from the discussion regarding the Transatlantic Trade Investment Partnership in Europe. Our results provide unique insights into the role various actors play in diffusing political information in online social networks.

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Gafni, Ruti, Tal Pavel, Raz Margolin, and Ben Weiss. "Strong password? Not with your social network data!" Online Journal of Applied Knowledge Management 5, no. 1 (May 4, 2017): 27–41. http://dx.doi.org/10.36965/ojakm.2017.5(1)27-41.

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Passwords are the standard means of registration and access to Websites, information systems, online services and various social networks. Databases are increasingly breached and social engineering is employed to obtain usernames and passwords for online fraud, therefore, there is a need to secure existing passwords, and to create ones that will be more crack-resistant. This study addresses the issue of personal data, which users enter on social networks, and incorporate in passwords, as well as how tracking and identifying this data assists hackers in cracking these passwords. The study focuses on Facebook, conducting an online anonymous questionnaire among 195 respondents, and an experiment among a voluntary response sample of 72 participants, in which passwords were tried to been deciphered by a custom dictionary attack. The findings confirm a link between the use of accessible online personal data and success rates of password deciphering. The findings underscore the grave threat to users’ information security - not only as a result of their voluntary exposure of personal data on social networks, but also due to the integration of this data into their passwords. The study argues the need to emphasize users' awareness to their password strength, with this vulnerability in mind.

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Parker, Heather J., and Stephen Flowerday. "Understanding the disclosure of personal data online." Information & Computer Security 29, no. 3 (May 21, 2021): 413–34. http://dx.doi.org/10.1108/ics-10-2020-0168.

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Purpose Social media has created a new level of interconnected communication. However, the use of online platforms brings about various ways in which a user’s personal data can be put at risk. This study aims to investigate what drives the disclosure of personal information online and whether an increase in awareness of the value of personal information motivates users to safeguard their information. Design/methodology/approach Fourteen university students participated in a mixed-methods experiment, where responses to Likert-type scale items were combined with responses to interview questions to provide insight into the cost–benefit analysis users conduct when disclosing information online. Findings Overall, the findings indicate that users are able to disregard their concerns due to a resigned and apathetic attitude towards privacy. Furthermore, subjective norms enhanced by fear of missing out (FOMO) further allows users to overlook potential risks to their information in order to avoid social isolation and sanction. Alternatively, an increased awareness of the personal value of information and having experienced a previous privacy violation encourage the protection of information and limited disclosure. Originality/value This study provides insight into privacy and information disclosure on social media in South Africa. To the knowledge of the researchers, this is the first study to include a combination of the theory of planned behaviour and the privacy calculus model, together with the antecedent factors of personal valuation of information, trust in the social media provider, FOMO.

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Sufa, Siska Armawati, Andry Alamsyah, Suwandi Sumartias, Feliza Zubair, and Susie Perbawasari. "Twitter and Online Prostitution: The Communication Network of Online Prostitution in Twitter." Webology 19, no. 1 (January 20, 2022): 2797–810. http://dx.doi.org/10.14704/web/v19i1/web19186.

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The information developments and communication technology also has a negative side. This study is aimed to determine the online prostitution communication network on Twitter and understanding of online prostitution on Twitter. This study uses SNA (Social Network Analysis). SNA views social relations as a theory of networks consisting of nodes and relationships (also called edges, links, or connections). Nodes are individual actors in the network and the relationships are between actors. The data set used in this study is tweet data from Twitter, with the keyword samples being “openBO”. The hashtags were chosen because they are the most widely used the hashtags for online prostitution. The data is taken with a period between January 2020 to December 2020. The data is taken and processed using NodeXL Basic, one of the plugins for Microsoft Excel that can be used to process data from social networks. This research found there are 3,673 nodes and 54,969 edges. The hashtags utilization is usually utilized by online whores to advance their administrations. A few prostitutes are independent, it is implying that they don’t connect with others in advancing business sex administrations. It is not reliant upon pimps since pimps mean to lessen their business chain.

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Silwal, Suman, and Dale W. Callahan. "Using Social Media Data as Research Data." International Journal for Innovation Education and Research 1, no. 3 (November 30, 2013): 49–55. http://dx.doi.org/10.31686/ijier.vol1.iss3.114.

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Social Media (SM) is becoming a normal part of everyday life. The information generated from Social Media (SM) data is becoming increasingly utilized as a communication channel for market trend, brand awareness, breaking news, and online social interaction between person to person. SM is also rapidly growing and maturing [1]. Further, SM is becoming a reliable tool for interdisciplinary industries like banks, travel, healthcare, biotech, software, sports etc.SM data can also be used as a research tool to apply in different areas of Humanities, Art, Science and Engineering. There are unlimited possibilities using Social Networking Site (SNS) to collect, process and evaluate data. This paper reviews the current state of Social Networking Sites and Text-based Language Processes, and how it can be used to generate valuable information.

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Al-Taie, Mohammed Zuhair, Seifedine Kadry, and Joel Pinho Lucas. "Online Data Preprocessing: A Case Study Approach." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (August 1, 2019): 2620. http://dx.doi.org/10.11591/ijece.v9i4.pp2620-2626.

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<span lang="EN-US">Besides the Internet search facility and e-mails, social networking is now one of the three best uses of the Internet. A tremendous number of volunteers every day write articles, share photos, videos and links at a scope and scale never imagined before. However, because social network data are huge and come from heterogeneous sources, the data are highly susceptible to inconsistency, redundancy, noise, and loss. For data scientists, preparing the data and getting it into a standard format is critical because the quality of data is going to directly affect the performance of mining algorithms that are going to be applied next. Low-quality data will certainly limit the analysis and lower the quality of mining results. To this end, the goal of this study is to provide an overview of the different phases involved in data preprocessing, with a focus on social network data. As a case study, we will show how we applied preprocessing to the data that we collected for the Malaysian Flight MH370 that disappeared in 2014.</span>

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Sung-bok Chang and 성열홍. "Online Opinion Analysis of Performing Arts through Social Big Data." Journal of Korea Design Forum ll, no. 57 (November 2017): 79–90. http://dx.doi.org/10.21326/ksdt.2017..57.007.

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Koohpar, Fariba Khazaei, Afsaneh Fatemi, and Fatemeh Raji. "Data availability improvement in peer-to-peer online social networks." IET Information Security 14, no. 3 (May 1, 2020): 332–41. http://dx.doi.org/10.1049/iet-ifs.2019.0363.

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Gibbons, John W., and Arvin Agah. "Modelling content lifespan in online social networks using data mining." International Journal of Web Based Communities 11, no. 3/4 (2015): 234. http://dx.doi.org/10.1504/ijwbc.2015.072131.

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Liu, Guoxin, Haiying Shen, and Harrison Chandler. "Selective Data Replication for Online Social Networks with Distributed Datacenters." IEEE Transactions on Parallel and Distributed Systems 27, no. 8 (August 1, 2016): 2377–93. http://dx.doi.org/10.1109/tpds.2015.2485266.

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AbdulKader, Hatem, Emad ElAbd, and Waleed Ead. "Protecting Online Social Networks Profiles by Hiding Sensitive Data Attributes." Procedia Computer Science 82 (2016): 20–27. http://dx.doi.org/10.1016/j.procs.2016.04.004.

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Alvarez-Jimenez, M., M. A. Alcazar-Corcoles, C. Gonzalez-Blanch, S. Bendall, P. D. McGorry, and J. F. Gleeson. "Online social media: New data, new horizons in psychosis treatment." Schizophrenia Research 166, no. 1-3 (August 2015): 345–46. http://dx.doi.org/10.1016/j.schres.2015.05.006.

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Park, Joon S., Kevin A. Kwiat, Charles A. Kamhoua, Jonathan White, and Sookyung Kim. "Trusted Online Social Network (OSN) services with optimal data management." Computers & Security 42 (May 2014): 116–36. http://dx.doi.org/10.1016/j.cose.2014.02.004.

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Zhou, Jingya, Jianxi Fan, Jin Wang, Baolei Cheng, and Juncheng Jia. "Towards traffic minimization for data placement in online social networks." Concurrency and Computation: Practice and Experience 29, no. 6 (May 19, 2016): e3869. http://dx.doi.org/10.1002/cpe.3869.

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Gangarde, Rupali, Deepshikha Shrivastava, Amit Sharma, Tanishka Tandon, Ambika Pawar, and Rachit Garg. "Data anonymization to balance privacy and utility of online social media network data." Journal of Discrete Mathematical Sciences and Cryptography 25, no. 3 (April 3, 2022): 829–38. http://dx.doi.org/10.1080/09720529.2021.2016225.

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Donath, Judith, Alex Dragulescu, Aaron Zinman, Fernanda Viégas, and Rebecca Xiong. "Data Portraits." Leonardo 43, no. 4 (August 2010): 375–83. http://dx.doi.org/10.1162/leon_a_00011.

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Data portraits depict their subjects' accumulated data rather than their faces. They can be visualizations of discussion contributions, browsing histories, social networks, travel patterns, etc. They are subjective renderings that mediate between the artist's vision, the subject's self-presentation, and the audience's interest. Designed to evocatively depict an individual, a data portrait can be a decorative object or be used as an avatar, one's information body for an online space. Data portraits raise questions about privacy, control, aesthetics, and social cognition. These questions become increasingly important as more of our interactions occur online, where we exist as data, not bodies.

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Taylor, Bradley. "Understanding Consumer Preferences from Social Media Data." NIM Marketing Intelligence Review 11, no. 2 (November 1, 2019): 48–53. http://dx.doi.org/10.2478/nimmir-2019-0016.

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AbstractConsumers produce enormous amounts of textual data of product reviews online. Artificial intelligence (AI) can help analyze this data and generate insights about consumer preferences and decision-making. A GfK research project tested how we can use AI to learn consumer preferences and predict choices from publicly available social media and review data. The common AI tool “Word Embeddings” was used and has shown to be a powerful way to analyze the words people use. It helped reveal consumers’ preferred brands, favorite features and main benefits. Language biases uncovered by the analysis can indicate preferences. Compared to actual sales data from GfK panels, they fit reasonably within various categories. Especially when data volumes were large, the method produced very accurate results. By using free, widespread online data it is completely passive, without affecting respondents or leading them into ranking or answering questions they would otherwise not even have thought of. The analysis is fast to run and no fancy processing power is needed.

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Kitto, Richard J., and John Barnett. "Analysis of Thin Online Interview Data." American Journal of Evaluation 28, no. 3 (September 2007): 356–68. http://dx.doi.org/10.1177/1098214007304536.

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Prof. Narinder Kaur and Lakshay Monga. "Social Network Mental Disorders Detection via Online Social Media Mining." January 2021 7, no. 01 (January 4, 2021): 27–30. http://dx.doi.org/10.46501/ijmtst0701006.

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Social Network Mental Disorder Detection” or “SNMD” is an approach to analyse data and retrieve sentiment that it embodies. Twitter SNMD analysis is an application of sentiment analysis on data from Twitter (tweets), in order to extract sentiments conveyed by the user. In this paper, we aim to review some papers regarding research in sentiment analysis on Twitter, describing the methodologies adopted and models applied, along with describing a generalized Python based approach. A prototype system is developed and tested.

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Dai, Chenyun, Fang-Yu Rao, Traian Marius Truta, and Elisa Bertino. "Privacy-Preserving Assessment of Social Network Data Trustworthiness." International Journal of Cooperative Information Systems 23, no. 02 (June 2014): 1441004. http://dx.doi.org/10.1142/s0218843014410044.

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Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for decision making of those companies. In this paper, our goal is to assess the trustworthiness of local social network data by referencing external social networks. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.

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Jacobsen, Stephanie, and Nora Ganim Barnes. "On Being Social: How Social Identity Impacts Social Commerce for the Millennial Shopper." INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION 3, no. 4 (2017): 38–45. http://dx.doi.org/10.18775/ijmsba.1849-5664-5419.2014.34.1005.

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Millennials are a technologically sophisticated generation, who have the purchasing power to change the face of retailing. A significant proportion of their shopping is done online and they utilize their social networks while engaging in the shopping process- a current area of interest termed “social commerce”. No single group is better positioned to take advantage of social commerce, and yet, it’s possible that Millennials are participating in social networks and online shopping in order to better define their social identities. This study summarizes data from three years of longitudinal research into the use of social media by Millennials on three platforms: Facebook, Twitter and Pinterest. The results show that Millennials prefer to utilize the identity shaping aspects of social media and commerce. We recommend that platforms allow more identity formation in order to increase the likelihood that Millennials not only use the platform, but also make purchases through them.

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Magesh, S., and S. Vijayalakshmi. "Purchaser's Optimistic Response to Social Media Advertisements—A Data Mining Approach." Journal of Computational and Theoretical Nanoscience 16, no. 2 (February 1, 2019): 664–68. http://dx.doi.org/10.1166/jctn.2019.7787.

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The paper aspires at discovering the most indispensable factors persuading customer reactions and purchasing commodities after observing online advertisements of social media and recognizing the distinctiveness of clusters of Purchaser having the optimistic reaction, over and above of buying customer clusters after analyzing online advertisement in social media. The selection of attribute and clustering techniques are incorporated in the analysis of data to find significant factors and target customer clusters correspondingly through data mining approach. It has been identifies that there is a strapping correlation between the advertisement being clicked on social media and the fulfillment with commodities, and amidst purchasing commodities online and saving information for supplementary deliberations. The findings also points out the characteristics of product and price Conscious clusters for Purchasers' reaction and procuring after seeing online social media advertisement.

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Liu, Zhi Han, Shao Teng Han, and Fang Chun Yang. "OSL: An Optimized Strategy of Data Replicas for Online Social Network." Applied Mechanics and Materials 263-266 (December 2012): 3230–33. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.3230.

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With the continued growth of social network users, nearly all the OSN (Online Social Network) providers replicate users’ data to different server nodes in order to ensure high reliability. Unlike traditional web applications, OSN represents a different class of data system that most of the data is based on friend relationship. In this paper, we propose OSL (Online Social Locality) algorithm, an online optimized strategy of data replicas based on social locality. This algorithm can decide the server node the replicas should be saved, and it improves system performance significantly.

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Miori, Virginia M., and Richard T. Herschel. "Exploring Big Data Footprints and Ethics." International Journal of Business Intelligence Research 10, no. 2 (July 2019): 1–15. http://dx.doi.org/10.4018/ijbir.2019070101.

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Big Data is collected via engagement in online activity and undergraduate students tend to be particularly heavy users of digital media. This article explores their online activity to assess their participation and usage patterns as well as their ethical perspectives. The research finds that these students have a somewhat substantial Big Data footprint since they actively engage in social media, use smart devices, shop online, use streaming services, and employ digital tools. Social connectedness necessitates the potential for their privacy being compromised and the findings suggest that introverts are more concerned about this issue then extroverts are. However, people of both types are concerned about conveying a positive image online. The majority of those surveyed primarily identified with the values expressed by the Utilitarian and Kantian ethical perspectives and less so with those expressed by Social Contract Theory and Virtue Ethics. However, study participants did not consistently ground their moral values in any one of these ethical theories.

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Corple, Danielle J., and Jasmine R. Linabary. "From data points to people: feminist situated ethics in online big data research." International Journal of Social Research Methodology 23, no. 2 (July 30, 2019): 155–68. http://dx.doi.org/10.1080/13645579.2019.1649832.

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Al-Obeidat, Feras, Anoud Bani-Hani, Oluwasegun Adedugbe, Munir Majdalawieh, and Elhadj Benkhelifa. "A microservices persistence technique for cloud-based online social data analysis." Cluster Computing 24, no. 3 (March 30, 2021): 2341–53. http://dx.doi.org/10.1007/s10586-021-03244-0.

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Stepanenko, Viktoriia, and Viacheslav Stepanenko. "Online Services and Data Processing Software for Pedagogical and Social Research." Bulletin of Luhansk Taras Shevchenko National University 2, no. 1 (349) (2022): 160–68. http://dx.doi.org/10.12958/2227-2844-2022-1(349)-2-160-168.

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In the presented article, an attempt was made to present a conditional set of services and programs that allow the collection, systematization, statistical processing of data and presentation of results, including visualization (graphs, tables of results, charts, etc.). It is noted that the main trend in the development of modern services is their complexity. As a rule, several operations can be performed using one service (data collection, their primary mathematical processing, visualization, etc.). Therefore, all classifications of such services are rather conditional. A review of certain online services and software packages for capturing, processing and storing data from pedagogical and social research was carried out: Google Sheets, Google Forms, Microsoft Office Excel, STATISTICA, SPSS, online calculators, etc. The advantages and disadvantages of using these services are outlined. It is noted that the use of ICT in pedagogical and social research improves their quality and compliance with modern requirements for the processing of experimental data.

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Ranjan, B. B., G. Srujana, N. Rupalin, and P. Kiranmai. "Identification and Detection of Unwanted Data in Online Promotion Social Networks." Indian Journal of Public Health Research & Development 9, no. 12 (2018): 1602. http://dx.doi.org/10.5958/0976-5506.2018.02087.9.

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An, Ying. "Medical Social Security System Considering Multisensor Data Fusion and Online Analysis." Mobile Information Systems 2022 (August 8, 2022): 1–12. http://dx.doi.org/10.1155/2022/2312333.

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At present, multi-sensor data fusion technology has been applied in many fields, and we have to understand the algorithm principle of multisensor data fusion technology, master the basic theory of multisensor data fusion technology, and analyze the application field of multisensor data fusion technology. The technical challenges of its application area should be understood. The discussion on its development direction has promoted the wide application of multisensor data fusion technology. By improving the query planning, query interpretation, and cache query optimization mechanism of different data organization models, a scalable and efficient distributed hybrid online analytical processing system is designed and implemented. With the opening of the medical system reform, the construction of the medical security system will be continuously improved. The in-depth reform of the medical security system will affect the medical treatment of more than one billion people, involving thousands of households, and it is a great cause for the welfare of the people. In this paper, exploration and research are carried out, combined with the basic algorithm and typical application of data fusion, and the construction of urban residents’ medical insurance is studied. The experimental results show that the Cavani index of medical expenses generally shows a downward trend in urban and rural areas, and the urban area has decreased from −0.1916 in 2012 to −0.2995 in 2020. This paper focuses on the in-depth study of the development process, actual situation, and existing problems of the social medical insurance system for urban residents, compares the development models of urban medical insurance in other places, and tries to put forward valuable and constructive countermeasures for the medical insurance system for urban residents.

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Torra, Vicenc, Termeh Shafie, and Julian Salas. "Data Protection for Online Social Networks and $ P$ -Stability for Graphs." IEEE Transactions on Emerging Topics in Computing 4, no. 3 (July 2016): 374–81. http://dx.doi.org/10.1109/tetc.2015.2433923.

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