Automated Fake News Detection in the Age of Digital Libraries
The transformation of printed media into digital environment and the extensive use of social media have changed the concept of media literacy and people’s habit of consuming news. While this faster, easier, and comparatively cheaper opportunity offers convenience in terms of people's access to information, it comes with a certain significant problem: Fake News. Due to the free production and consumption of large amounts of data, fact-checking systems powered by human efforts are not enough to question the credibility of the information provided, or to prevent its rapid dissemination like a virus. Libraries, known as sources of trusted information for ages, are facing with the problem because of this difficulty. Considering that libraries are undergoing digitisation processes all over the world and providing digital media to their users, it is very likely that unchecked digital content will be served by world’s libraries. The solution is to develop automated mechanisms that can check the credibility of digital content served in libraries without manual validation. For this purpose, we developed an automated fake news detection system based on the Turkish digital news content. Our approach can be modified for any other language if there is labelled training material. The developed model can be integrated into libraries’ digital systems to label served news content as potentially fake whenever necessary, preventing uncontrolled falsehood dissemination via libraries.
Alexandre Bovet and Hernán A. Makse, “Influence of Fake News in Twitter During the 2016 US Presidential Election,” Nature Communications 10, no. 7 (2019): 1–14, https://doi.org/10.1038/s41467-018-07761-2.
American Library Association, “Resolution on Access to Accurate Information,” 2018.
Andrew Guess, Brendan Nyhan, and Jason Reifler, “Selective Exposure to Misinformation: Evidence from the Consumption of Fake News during the 2016 US Presidential Campaign,” European Research Council 9, no. 3 (2018): 4.
Angeleen Neely–Sardon, and Mia Tignor, “Focus on the Facts: A News and Information Literacy Instructional Program,” The Reference Librarian 59, no. 3 (2018): 108–21, https://doi.org /10.1080/02763877.2018.1468849.
Burak Bezirci, and Asım Egemen Yilmaz, “Metinlerin Okunabilirliğinin Ölçülmesi Üzerine Bir Yazilim Kütüphanesi Ve Türkçe Için Yeni Bir Okunabilirlik Ölçütü,” Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi 12, no. 3 (2010): 49–62, https://dergipark.org.tr/en/pub/deumffmd/issue/40831/492667.
Christian Janze, and Marten Risius, “Automatic Detection of Fake News on Social Media Platforms,” (paper, Pasific Asia Conference on Information Systems (PACIS), 2017).
Claire Wardle and Hossein Derakhshan, “Information Disorder: Toward an Interdisciplinary Framework for Research and Policy Making,” Council of Europe report 27 (2017).
David M. Markowitz, and Jeffrey T. Hancock, “Linguistic Traces of a Scientific Fraud: The Case of Diederik Stapel,” PloS one 9, no. 8 (2014): e105937, https://doi.org/10.1371/journal.pone.0105937.
“Definition of 'News',” available at: https://www.collinsdictionary.com/dictionary/english/news
Dominic DiFranzo and Kristine Gloria-Garcia, “Filter Bubbles and Fake News,” XRDS: Crossroads, The ACM Magazine for Students 23, no. 3 (April 2017): 32–35, https://doi.org/10.1145/3055153.
Ender Ateşman, “Türkçede Okunabilirliğin Ölçülmesi,” Dil Dergisi 58, no. 71–74 (1997).
Eni Mustafaraj and P. Takis Metaxas, “The Fake News Spreading Plague: Was It Preventable?” Proceedings of the 2017 ACM on Web Science Conference, (June 2017): 235–39, https://doi.org/10.1145/3091478.3091523.
Eugenio Tacchini et al., “Some Like It Hoax: Automated Fake News Detection in Social Networks,” arXiv preprint arXiv:1704.07506 (2017).
“Facebook, Twitter May Face More Scrutiny in 2019 to Check Fake News, Hate Speech,” accessed May 17, 2020, available: https://www.huffingtonpost.in/entry/facebook-twitter-may-face-more-scrutiny-in-2019-to-check-fake-news-hate-speech_in_5c29c589e4b05c88b701d72e.
Francesco Barbieri, Francesco Ronzano, and Horacio Saggion, “Is This Tweet Satirical? A Computational Approach for Satire Detection in Spanish,” Procesamiento del Lenguaje Natural, no. 55 (2015): 135-42.
Hannah Rashkin et al., “Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking,” Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, (2017): 2931–37.
IFLA, “How to Spot Fake News,” 2017.
Jacob L. Nelson and Harsh Taneja, “The Small, Disloyal Fake News Audience: The Role of Audience Availability in Fake News Consumption,” New Media & Society 20, no. 10 (2018): 3720–37, https://doi.org/10.1177/1461444818758715.
James W. Pennebaker, Martha E. Francis, and Roger J. Booth, “Linguistic Inquiry and Word Count: LIWC 2001”, Mahway: Lawrence Erlbaum Associates 71, no. 2001 (2001).
Jana Laura Egelhofer and Sophie Lecheler, “Fake News as a Two-Dimensional Phenomenon: A Framework and Research Agenda,” Annals of the International Communication Association 43, no. 2 (2019): 97–116, https://doi.org/10.1080/23808985.2019.1602782.
Jane Mandalios, “Radar: An Approach for Helping Students Evaluate Internet Sources,” Journal of Information Science 39, no. 4 (2013): 470–78, https://doi.org/10.1177/0165551513478889.
Jing Ma et al., “Detecting Rumors from Microblogs with Recurrent Neural Networks,” (paper, Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), (2016): 3818–24), https://ink.library.smu.edu.sg/sis_research/4630.
Julia B. Hirschberg et al., “Distinguishing Deceptive from Non-Deceptive Speech,” (2005), https://doi.org/10.7916/D8697C06.
Julio C.S. Reis et al., “Supervised Learning for Fake News Detection,” IEEE Intelligent Systems 34, no. 2 (2019): 76–81, https://doi.org10.1109/MIS.2019.2899143.
Justin P. Friesen, Troy H. Campbell, and Aaron C. Kay, “The Psychological Advantage of Unfalsifiability: The Appeal of Untestable Religious and Political Ideologies,” Journal of Personality and Social Psychology 108, no. 3 (2015): 515–29, https://doi.org/10.1037/pspp0000018.
Kai Shu et al., “Fake News Detection on Social Media: A Data Mining Perspective,” ACM SIGKDD Explorations Newsletter 19, no. 1 (2017): 22–36, https://doi.org/10.1145/3137597.3137600.
Lei Guo and Chris Vargo, “’Fake News’ and Emerging Online Media Ecosystem: An Integrated Intermedia Agenda-Setting Analysis of the 2016 Us Presidential Election,” Communication Research 47, no. 2 (2020): 178–200, https://doi.org/10.1177/0093650218777177.
Lina Zhou et al., “Automating Linguistics-Based Cues for Detecting Deception in Text-Based Asynchronous Computer-Mediated Communications,” Group Decision and Negotiation 13, no. 1 (2004): 81–106, https://doi.org/10.1023/B:GRUP.0000011944.62889.6f.
Linda Jacobson, “The Smell Test: In the Era of Fake News, Librarians Are Our Best Hope,” School Library Journal 63, no. 1 (2017): 24–29.
Lynn Silipigni Connaway et al., “Digital Literacy in the Era of Fake News: Key Roles for Information Professionals,” Proceedings of the Association for Information Science and Technology 54, no. 1 (2017): 554–55, https://doi.org/10.1002/pra2.2017.14505401070.
M. Connor Sullivan, “Why Librarians Can’t Fight Fake News,” Journal of Librarianship and Information Science 51, no. 4 (December 2019): 1146–56, https://doi.org/10.1177/0961000618764258.
Matthew C. Sullivan, “Libraries and Fake News: What’s the Problem? What’s the Plan?,” Communications in Information Literacy 13, no. 1 (2019): 91–113, https://doi.org/10.15760/comminfolit.2019.13.1.7.
Myle Ott et al., “Finding Deceptive Opinion Spam by Any Stretch of the Imagination,” arXiv preprint arXiv:1107.4557 (2011).
Natali Ruchansky, Sungyong Seo, and Yan Liu, “CSI: A Hybrid Deep Model for Fake News Detection,” Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, (November 2017): 797–806, https://doi.org/10.1145/3132847.3132877.
Nick Rochlin, “Fake News: Belief in Post-Truth,” Library Hi Tech 35, no. 3 (2017): 386–92, https://doi.org/10.1108/LHT-03-2017-0062.
Nitesh V. Chawla et al., “Smote: Synthetic Minority Over-Sampling Technique,” Journal of Artificial Intelligence Research 16, (2002): 321–57, https://doi.org/10.1613/jair.953.
Philip N. Howard et al., “Social Media, News and Political Information During the US Election: Was Polarizing Content Concentrated in Swing States?,” arXiv preprint arXiv:1802.03573 (2018).
Rada Mihalcea and Carlo Strapparava, “The Lie Detector: Explorations in the Automatic Recognition of Deceptive Language,” (paper, Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, (2009): Association for Computational Linguistics, 309–12).
Robert Gunning, “The technique of clear writing,” Revised Edition, New York: McGraw Hill, 1968.
S. F. Kattimani, Praveenkumar Kumbargoudar, and D. S. Gobbur, “Training of the Library Professionals in Digital Era: Key Issues” (2006), https://ir.inflibnet.ac.in:8443/ir/handle/1944/1234.
Sarah Blakeslee, “The CRAAP test,” LOEX Quarterly 3, no. 3 (2004):4.
Soroush Vosoughi, Deb Roy, and Sinan Aral, “The Spread of True and False News Online,” Science 359, no. 6380 (2018): 1146–51, https://doi.org/10.1126/science.aap9559.
Soujanya Poria et al., “A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks,” arXiv preprint arXiv:1610.08815 (2016).
Tanja Pavleska et al., “Performance Analysis of Fact-Checking Organizations and Initiatives in Europe: A Critical Overview of Online Platforms Fighting Fake News,” Social Media and Convergence 29 (2018).
Torstein Granskogen, “Automatic Detection of Fake News in Social Media Using Contextual Information” (master’s thesis, Norwegian University of Science and Technology (NTNU), 2018).
Uğur Mertoğlu and Burkay Genç, “Lexicon Generation for Detecting Fake News,” arXiv preprint arXiv:2010.11089 (2020).
Verónica Pérez-Rosas et al., “Automatic Detection of Fake News,” arXiv preprint arXiv:1708.07104 (2017).
Victoria L. Rubin et al., “Fake News or Truth? Using Satirical Cues to Detect Potentially Misleading News,” (paper, Proceedings of the Second Workshop on Computational Approaches to Deception Detection, (2016): 7–17).
Victoria L. Rubin and Niall Conroy, “Discerning Truth from Deception: Human Judgments and Automation Efforts,” First Monday 17, no. 5 (2012), https://doi.org/10.5210/fm.v17i3.3933.
Victoria L. Rubin, Yimin Chen, and Nadia K. Conroy, “Deception Detection for News: Three Types of Fakes,” Proceedings of the Association for Information Science and Technology 52, no. 1 (2015): 1–4, https://doi.org/10.1002/pra2.2015.145052010083.
Wayne Finley, Beth McGowan, and Joanna Kluever, “Fake News: An Opportunity for Real Librarianship,” ILA reporter 35, no. 3 (2017): 8–12.
Xinyi Zhou and Reza Zafarani, “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities,” ACM Computing Surveys (CSUR) 53, no. 5 (2020): 1–40, https://doi.org/10.1145/3395046.
Yaqing Wang et al., “EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection,” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (2018): 849–57, https://doi.org/10.1145/3219819.3219903.
Yasmine Lahlou, Sanaa El Fkihi, and Rdouan Faizi, “Automatic Detection of Fake News on Online Platforms: A Survey,” (paper, 2019 1st International Conference on Smart Systems and Data Science (ICSSD), Rabat, Morocco, 2019), https://doi.org/10.1109/ICSSD47982.2019.9002823.
Copyright (c) 2020 Uğur Mertoğlu and Burkay Genç
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.