6TH International Congress on Technology - Engineering - Kuala Lumpur3 - Malaysia (2018-07-19)

Post Cambridge Analytica Fallout: Observing Social Media User Awareness Regarding The Use Of Their Data

Social media has become one of the most significant platforms in recent years. The ability to connect to other people, communicate through text and multimedia content has become a necessity in modern lifestyle. Moreover, users and consumers are increasingly dependent on social media to find proper information in order to make decisions. This fact motivates researchers to find dyadic relations between individual’s activity in social media and the decision made related to the activity. The terms Social Network Analysis (1) and Social Set Analysis (2) are two of the most common concept used to find the relation of users’ profile and their activity in social media and real life. Furthermore, other studies have shown that this concept can be implemented to support business activity (3–5) or predict opinion or political tendency of a user (6) or a group of users (7,8). Since then, social media activity data has become a valuable asset. However, harvesting the data efficiently and effectively without breaching user’s privacy is another significant problem. In the first quarter of 2018, Facebook has been shaken by the leaked information regarding their users data have been used for Donald Trump’s 2016 Precidency campaign (9). The data was harvested by a UK based political consultant Cambridge Analytica. These data harvested by using a Facebook based quiz application that exploit Facebook API capability to access not only the user data, but also the user’s friends data. Further investigation showed that Indonesia Facebook users data were also part of the harvested data (10). While the role of Cmabridge Analytica in Indonesia is still unclear, it is important for Indonesian users to be aware of their data considering the upcoming presidential election in near future. The objective of this study is to measure Indonesian user awareness regarding their data; how it was stored, shared, and used either by the social media platform or by third party. We split the survey into three part: the behaviour of social media activity, the awareness regarding the event, and the understanding of how their data is used. We collected the result from 312 responders which are active social media users. The result shows that while the majority of the users have heard about the event, only 10% were able to describe the event correctly. While most of the responders aware how their data is commercially used to enhance advertisement targeting, only 13% understand how their social media activity can be used to build psychographic profile of the user. Another interesting fact is 26% of our responders were unaware that Facebook based application, such as a simple quiz app, gives third party developer an access to user’s data and this method is commonly used by third party developer to harvest user’s data without their consent. These results shows that most of the responders in our survey have a very low awareness regarding the event and its impact in social media activity. They were unaware how the data can be harvested unethically and can be used to perform user profiling. We conclude that the need for users education regarding the importance of digital data is critical to avoid further abuse. References: 1. Borgatti SP, Mehra A, Brass DJ, Labianca G. Network Analysis in the Social Sciences. Science. 2009 Feb 13;323(5916):892–5. 2. Vatrapu R, Mukkamala RR, Hussain A, Flesch B. Social Set Analysis: A Set Theoretical Approach to Big Data Analytics. IEEE Access. 2016;4:2542–71. 3. Hu H-W, Cheng C-H, Chung Y-C, Lee C-Y. Ticket-purchase behavior under the effects of marketing campaigns on facebook fan pages. In IEEE; 2017 [cited 2018 May 31]. p. 2746–51. Available from: http://ieeexplore.ieee.org/document/8258239/ 4. Boldt LC, Vinayagamoorthy V, Winder F, Schnittger M, Ekran M, Mukkamala RR, et al. Forecasting Nike’s sales using Facebook data. In IEEE; 2016 [cited 2018 May 23]. p. 2447–56. Available from: http://ieeexplore.ieee.org/document/7840881/ 5. Goncalves J, Kostakos V, Venkatanathan J. Narrowcasting in social media: effects and perceptions. In ACM Press; 2013 [cited 2018 May 31]. p. 502–9. Available from: http://dl.acm.org/citation.cfm?doid=2492517.2492570 6. Chiu S-I, Hsu K-W. Predicting Political Tendency of Posts on Facebook. In ACM Press; 2018 [cited 2018 May 31]. p. 110–4. Available from: http://dl.acm.org/citation.cfm?doid=3185089.3185094 7. Flesch B, Vatrapu R, Mukkamala RR. A big social media data study of the 2017 german federal election based on social set analysis of political party Facebook pages with SoSeVi. In IEEE; 2017 [cited 2018 May 23]. p. 2720–9. Available from: http://ieeexplore.ieee.org/document/8258236/ 8. Vicario MD, Gaito S, Quattrociocchi W, Zignani M, Zollo F. News Consumption during the Italian Referendum: A Cross-Platform Analysis on Facebook and Twitter. In IEEE; 2017 [cited 2018 May 31]. p. 648–57. Available from: http://ieeexplore.ieee.org/document/8259827/ 9. Chang A. The Facebook and Cambridge Analytica scandal, explained with a simple diagram - Vox [Internet]. The Facebook and Cambridge Analytica scandal, explained with a simple diagram | Vox. 2018 [cited 2018 Jun 10]. Available from: https://www.vox.com/policy-and-politics/2018/3/23/17151916/facebook-cambridge-analytica-trump-diagram 10. BBC Indonesia. Facebook: Bagaimana mengetahui data saya disalahgunakan oleh Cambridge Analytica? 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Jos Timanta Tarigan, Elviawaty M. Zamzami