Benutzer:Social2020/Social Data
Social Data
Social Data is a Data Source which Social Media provide for identifying Trends and Topics of Interest based on User Activity. The Data comes in form of Videos, Posts and other Items shared on Social Media[1]. Social Data is stored in Social Data Storage Systems.[2]
History
in the middle of 2000s Social Media created opportunities to study Social Processes and Dynamics in new ways. This was the Emergence of the „Web 2.0“. Social Network Sites such as Facebook and YouTube are the Foundation of the development of Web 2.0[3]. Nowdays the Internet is used as a Source of Information about the World by user-generated Content and Data sharing[4]. The Images and Videos People share and comment on and their Posts and Tweets all these Data that where collected to study Users behavior is Social Data[5]. Because only social media Companies had Access to large Social Datasets there where many Discussions[6]. Social Network Sites like Twitter or Amazon offers nowadays API’s to make Social Data available for any User[7]. The public APIs provided by social media and social network companies do not give all data that these companies themselves are capturing about the users[8]
Simpsons Paradox in Social Data
Simpsons Paradox was a big Problem that affects the Analysis of Trends in Social Data because Real-World Data is heterogeneous, i.e. composed of Subgroups that vary widely in Size and Behavior[9]. That means that the Trends that where observed in the Data that has been aggregated could be different from those of the underlying Subgroups[9]. This Failure led to wrong Conclusions. To solve this Problem Scientists Developed an Algorithm that is able to recognize the Simpsons Paradox[9].
Einzelnachweise
- ↑ Omar Alonso, Thibault Sellam: Quantitative Information Extraction From Social Data. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM, Ann Arbor MI USA 2018, ISBN 978-1-4503-5657-2, S. 1005–1008, doi:10.1145/3209978.3210133 (acm.org [abgerufen am 26. Januar 2021]).
- ↑ Nicolas Ruflin, Helmar Burkhart, Sven Rizzotti: Social-data storage-systems. In: Databases and Social Networks (= DBSocial '11). Association for Computing Machinery, Athens, Greece 2011, ISBN 978-1-4503-0650-8, S. 7–12, doi:10.1145/1996413.1996415 (doi.org [abgerufen am 26. Januar 2021]).
- ↑ Christian Fuchs: Web 2.0, Prosumption, and Surveillance. In: Surveillance & Society. Band 8, Nr. 3, 2011, ISSN 1477-7487, S. 288–309, doi:10.24908/ss.v8i3.4165 (queensu.ca [abgerufen am 26. Januar 2021]).
- ↑ Alexandra Olteanu, Carlos Castillo, Fernando Diaz, Emre Kıcıman: Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. In: Frontiers in Big Data. Band 2, 2019, ISSN 2624-909X, doi:10.3389/fdata.2019.00013 (frontiersin.org [abgerufen am 26. Januar 2021]).
- ↑ Jeon-Hyung Kang, Kristina Lerman: Scalable mining of social data using stochastic gradient fisher scoring. In: Proceedings of the 2103 workshop on Data-driven user behavioral modelling and mining from social media - DUBMOD '13. ACM Press, San Francisco, California, USA 2013, ISBN 978-1-4503-2417-5, S. 21–24, doi:10.1145/2513577.2513582 (acm.org [abgerufen am 26. Januar 2021]).
- ↑ Alexandra Olteanu, Carlos Castillo, Fernando Diaz, Emre Kıcıman: Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries. In: Frontiers in Big Data. Band 2, 2019, ISSN 2624-909X, doi:10.3389/fdata.2019.00013 (frontiersin.org [abgerufen am 26. Januar 2021]).
- ↑ Download Limit Exceeded. Abgerufen am 26. Januar 2021.
- ↑ Lev Manovich. Abgerufen am 26. Januar 2021 (englisch).
- ↑ a b c Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman: Can you Trust the Trend?: Discovering Simpson's Paradoxes in Social Data. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM, Marina Del Rey CA USA 2018, ISBN 978-1-4503-5581-0, S. 19–27, doi:10.1145/3159652.3159684 (acm.org [abgerufen am 26. Januar 2021]).
