Detecting and combating fake news on web 2.0 technology in the 2019 political season Indonesia

Samuel Anderson, Universitas Diponegoro, Semarang, Indonesia
Hapsari Dwiningtyas Sulistyani, Universitas Diponegoro, Semarang, Indonesia

Abstract


The digital age has come with lots of misinformation on the internet (web 2.0). The difference between real and fake news is unclear. This paper therefore scientifically employs algorithms and the evolution tree to help in the detection of fake news. Social bots in the spread of fake news are also detected by BotOrNot.  The research employs an in-depth qualitative but informal interview with 102 participants who are internet and social media-active as well as prospective Indonesian electorates to investigate the spread and believe in fake news. The result indicates that about 91 of the informants experience the spread of fake news on daily basis, out of which 67 succumb to the truthfulness of the news. This article therefore develops a trend of battling fake news with the application of the Inoculation theory and citizen journalism as tools to eradicate fake news that may emerge before and during the 2019 election.  ‘Ohmynews’ and ‘ABC blogs’ in the South Korean 2002 general elections and the Australian 2007 Federal elections respectively will be used as models of citizen journalism to deal with fake news that may trend on the Web 2.0 (where social media application are enabled) in the 2019 Indonesian polls.

 


Keywords


Citizen Journalism, Inoculation Theory, Fake News, Detection, Elections

Full Text:

PDF

References


Akoglu, L., McGlohon, M., & Faloutsos, C. (2010, June). Oddball: Spotting anomalies in weighted graphs. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 410-421). Springer, Berlin, Heidelberg.

Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-36.

Atton, C. & Hamilton, J.F. (2015) Alternative journalism, London: Sage Publications.

Atton, C. (2009). Why alternative journalism matters. Journalism, 10(3), 283-285.

Banas, J. A., & Rains, S. A. (2010). A meta-analysis of research on inoculation theory. Communication Monographs, 77(3), 281-311.

Bessi, A., & Ferrara, E. (2016). Social bots distort the 2016 US Presidential election online discussion. First Monday, 21(11-7).

Bruns, A. (2008). Blogs, Wikipedia, Second Life, and beyond: From production to produsage (Vol. 45). Peter Lang.

Compton, J., Jackson, B., & Dimmock, J. A. (2016). Persuading others to avoid persuasion: Inoculation theory and resistant health attitudes. Frontiers in psychology, 7, 122.

Detiknews, “Saya Joko Widodo”, Hentikan Penyebaran Berita Bohong (2017, June 08.

Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104.

Gillmor, D. (2006). We the media: Grassroots journalism by the people, for the people. " O'Reilly Media, Inc.".

Gupta, A., Lamba, H., Kumaraguru, P., & Joshi, A. (2013, May). Faking sandy: characterizing and identifying fake images on twitter during hurricane sandy. In Proceedings of the 22nd international conference on World Wide Web (pp. 729-736).

Horne, B. D., & Adali, S. (2017). This just in: Fake news packs a lot in title, uses simpler, repetitive content in text body, more similar to satire than real news. arXiv preprint arXiv:1703.09398.

Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2014, May). Inferring strange behavior from connectivity pattern in social networks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 126-138). Springer, Cham.

Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2014, May). Inferring strange behavior from connectivity pattern in social networks. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 126-138). Springer, Cham.

Jin, F., Dougherty, E., Saraf, P., Cao, Y., & Ramakrishnan, N. (2013, August). Epidemiological modeling of news and rumors on twitter. In Proceedings of the 7th workshop on social network mining and analysis (pp. 1-9).

Keyes, R. (2004). The post-truth era: Dishonesty and deception in contemporary life. Macmillan.

Leiserowitz, A., Rosenthal, S., & Maibach, E. (2017). Inoculating the Public against Misinformation about Climate Change. Global Challenges, 1(2).https://doi.org/10.1002/gch 2.201600008.

Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates, 71(2001), 2001.

Pérez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2017). Automatic detection of fake news. arXiv preprint arXiv:1708.07104.

Qazvinian, V., Rosengren, E., Radev, D., & Mei, Q. (2011, July). Rumor has it: Identifying misinformation in microblogs. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (pp. 1589-1599).

Shen, L., & Bigsby, E. (2012). The SAGE handbook of persuasion developments in theory and practice.

Tambuscio, M., Ruffo, G., Flammini, A., & Menczer, F. (2015, May). Fact-checking effect on viral hoaxes: A model of misinformation spread in social networks. In Proceedings of the 24th international conference on World Wide Web (pp. 977-982).

Van der Linden, S., Leiserowitz, A., Rosenthal, S., & Maibach, E. (2017). Inoculating the public against misinformation about climate change. Global Challenges, 1(2), 1600008.

Van der Linden, S., Leiserowitz, A., Rosenthal, S., & Maibach, E. (2017). Inoculating the public against misinformation about climate change. Global Challenges, 1(2), 1600008.




DOI: https://doi.org/10.21831/jss.v15i2.25233

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Samuel Anderson, Hapsari Dwiningtyas Sulistyani

Supervised by

RJI Main logo


Our Journal has been Indexed by

           


Creative Commons License
Journal of Social Studies (JSS) by http://journal.uny.ac.id/index.php/jss is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

View My Stats