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Exploring digital fake news phenomenon in indonesia cpr south_short_pdf

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A presentation at CPR South 2018, Maputo, Mozambique

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Exploring digital fake news phenomenon in indonesia cpr south_short_pdf

  1. 1. (Research In Progress) Exploring Digital Fake News Phenomenon in Indonesia Prepared By : Riri Kusumarani 1 CPRSouth 2018, Maputo, Mozambique Department Business & Technology Management KAIST, South Korea Image Source : Depositphotos.com Riri Kusumarani, HangJung Zo
  2. 2. Overview Digital Fake News in Indonesia
  3. 3. 3 Contents Overview Background . Research Motivation, Research Question, Research Objective Research Methodology Findings & Analysis Policy Recommendation Conclusion Proposed Policies
  4. 4. Overview The Trend : Digital Fake News  Intentional disinformation (invention or falsification of facts) for political and or com mercial purposes, presented as real news (McNair,2017)  False news stories that are intentionally packaged and published as if they were ge nuine (Allcott & Gentzkow,2017; DiFranzo & Gloria-Garcia,2017)
  5. 5. 130 millions Facebook subscribers ; annual growth of 23% *. * Source: wearesocial.com ; report on Digital … ** Source : Mastel.or.id. Survey done by MASTEL(2017)**  61.5% said they encountered a minimum of 1 fake news / day.  > 50% can spot hoax based on clarificati on  < 14.40% understands what actually hap pens Overview Digital Fake News in Indonesia
  6. 6. 6 What How Types & Channels of Digital Fake News are Commonly Found in Indonesia? Digital Fake News Spread in Indonesia 1st 2nd Explore Overview Research Questions & Objectives Explore Existing Situation lead to the Spread
  7. 7. Research Methodology  518 Fake news that are acquired from MAFINDO*  July 2015 ~ April 2018  Classification of Fake News  MCIT  MASTEL (Telecommunication Society)  Indonesian Internet Service Providers Association * MAFINDO : Independent organization aims to fight hoax and fake news
  8. 8. Source : Wardle (2017)
  9. 9. Findings & Analysis Findings POL 34% SARA 17% SOSBUD 17% TEK 5% KES 9% KRIM 3% LING 4% INTL 8% EKON 3% FAKE NEWS BY TOPIC Pol : Politics ; SARA : Ethnic,Religion, Race; Sosbud :SocioCultural ; Tek:Technology;Kes:Health ; Krim:Crime;Ling:Environment;Ekon:Economy Most Encountered Topic(s)
  10. 10. * Classification is based on Wardle(2017) Satire 1% False Connection 2% Misleading Content 19% False Content 24% Imposter 12% Manipulated Content 14% Fabricated Content 27% NA 1% FAKE NEWS BY TYPE* Findings & Analysis Findings Type & Channel of Fake News Most Common Writing Picture Source : Mastel.or.id Printed Media TV Web Chatting Application Channels
  11. 11. Findings & Analysis PSTeL Analysis What is Actually Happening There?
  12. 12.  Since 2004 , Indonesian people are more engaged in politics, one of them is due to dire ct voting mechanism (Tolbert, McNeal et al. 2003)  2012 marked as A turning point in Indonesians Political sphere(Lake, 2014) Fig: Ballot Paper for 2004 Presidential Election + Rise of The Buzzer Team “When everyone is talking about the same thing you mi ght think that maybe it’s true, maybe there is some mer it to it. That is where the impact lies.” ,Said Rasidi , Transpar ency International in Indonesia’s researcher.
  13. 13. SocCul >>>
  14. 14. Source : UNESCO, WorldBank, APJII , Internet User 2017 Fig: Internet Penetration Based on Age Group Fig:InternetAccessforLifestyle
  15. 15. Debunking Hoax Digital Literacy Digital Literacy
  16. 16.  Online Censorship : An internet based c rawling system supported by AI to fight negative contents in Indonesia starts to operate in 2018 (derived from EIT Law).  Not automatically censored.
  17. 17. * http://www.thejakartapost.com/academia/2017/11/28/editorial-countering-hate-speech.html  Country’s Criminal Code  slander and insults, to filing a false written or oral report to authorities that could harm the reputation of others*  The Electronic Information and Transactions (ITE)  target critics of the government*, Defamation  Anti-corruption activists, whistleblower, journalists(Safenet)  Establishment of Cyber Agency (2017) Source:Tempo.co 241 40% 7 Cases Court Years Served
  18. 18. Conclusion
  19. 19. Conclusion #1 Political related fake news are the most common topic in Indonesia’s Fake News Situation The number is expected to rise toward next presidential election (TheJakartaPost) #2 The role of Buzzer Team is clear in the spread of fake news The law enforcement might have arrested people related with this issue, but many are questioning the effectiveness of the act(Vice.com). #3 Fabricated & False Contents are The most common Categories These categories might be difficult to distinguish between the truth.
  20. 20. Policy Recommendations #2 Focus on Fake News Existing socio-technical movements are available. These social movements looks like independent from one another. Source : Vice.com #1 Integration of Independent Hoax Self-Reporting & Mitigation System
  21. 21. Digital Literacy Integration in School Curriculum#3 “Solutions are found through improving Indonesia’s mainstream media credibility, internet access and digital literacy,” Tapsell* says. “The ri se of ‘hoax news’ is thus a reflection of longer-term failures in these areas, rather than something that can be fixed immediately or easily.” *Ross Tapsell, an expert in Indonesian media at the Australian National University “The government’s [current] approach is short-term as it does not en- gage directly with the anti-hoax movement.”, Sasmito, Co-founder of MAFINDO 65% of 130 millions of Indonesia’s Internet users trust the news with out doing fact-checking (MCIT).
  22. 22. References Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211-236. Anderson, J. (2018). Even social media-savvy teens can’t spot a fake news story. Retrieved from https://qz.com/927543/even-social-media-savvy-t eens-cant-spot-a-fake-news-story/ Brandtzaeg, P. B., & Følstad, A. (2017). Trust and distrust in online fact-checking services. Communications of the ACM, 60(9), 65-71. Connolly, K., Chrisafis, A., McPherson, P., Kirchgaessner, S., Haas, B., Phillips, D., . . . Safi, M. (2016). Fake news: an insidious trend that's fast becom ing a global problem. Retrieved from https://www.theguardian.com/media/2016/dec/02/fake-news-facebook-us-election-around-the-w orld Dewey, C. (2014). This is not an interview with Banksy. The Washington Post. Retrieved from https://www.washingtonpost.com/news/the-intersec t/wp/2014/10/21/this-is-not-an-interview-with-banksy/?utm_term=.5ef84e836cd8 DiFranzo, D., & Gloria-Garcia, K. (2017). Filter bubbles and fake news. XRDS: Crossroads, The ACM Magazine for Students, 23(3), 32-35. European Commission. (2018). Summary report of the public consultation on fake news and online disinformation. Retrieved from https://ec.europ a.eu/digital-single-market/en/news/summary-report-public-consultation-fake-news-and-online-disinformation Figueira, Á., & Oliveira, L. (2017). The current state of fake news: challenges and opportunities. Procedia Computer Science, 121, 817-825. Molaei, H. (2017). Social Media and Politics: Examining Indonesians’ Political Knowledge on Facebook. Journal of Cyberspace Policy Studies Volume, 1(1), 119-139. Naveed, N., Gottron, T., Kunegis, J., & Alhadi, A. C. (2011). Bad news travel fast: A content-based analysis of interestingness on twitter. Paper prese nted at the Proceedings of the 3rd International Web Science Conference. Nelson, J. L., & Taneja, H. (2018). The small, disloyal fake news audience: The role of audience availability in fake news consumption. New Media & Society, 1461444818758715. Newman, N., Fletcher, R., Kalogeropoulos, A., Levy, D. A., & Nielsen, R. K. (2017). Reuters institute digital news report 2017. Newscred.com. (2018). Data From 10,000 Articles Prove That Content Marketing Really Does Work. Retrieved from https://insights.newscred.co m/data-from-10000-articles-prove-that-content-marketing-really-does-work/ Pearl, L., & Steyvers, M. (2010). Identifying emotions, intentions, and attitudes in text using a game with a purpose. Paper presented at the Proceed ings of the naacl hlt 2010 workshop on computational approaches to analysis and generation of emotion in text. Roets, A. (2017). ‘Fake news’: Incorrect, but hard to correct. The role of cognitive ability on the impact of false information on social impressions. I ntelligence, 65, 107-110. Silverman, C. (2015). Lies, damn lies and viral content. Tandoc Jr, E. C., Lim, Z. W., & Ling, R. (2018). Defining “Fake News” A typology of scholarly definitions. Digital Journalism, 6(2), 137-153. Tolbert, C. J., McNeal, R. S., & Smith, D. A. (2003). Enhancing civic engagement: The effect of direct democracy on political participation and knowl edge. State Politics & Policy Quarterly, 3(1), 23-41. Waldrop, M. M. (2017). News Feature: The genuine problem of fake news. Proceedings of the National Academy of Sciences, 114(48), 12631-1263 4. Ward, D. (2005). An overview of strategy development models and the Ward-Rivani model. Economics Working Papers, June, 1-24. Wardle, C. (2017). Fake news. It’s complicated. First Draft.
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A presentation at CPR South 2018, Maputo, Mozambique

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