A Brief Overview on the Strategies to Fight Back the Spread of False Information

  • Álvaro Figueira CRACS-INESCTEC and University of Porto, Porto, Portugal
  • Nuno Guimaraes CRACS-INESCTEC and University of Porto, Porto, Portugal
  • Luis Torgo Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
Keywords: false information, social networks

Abstract

The proliferation of false information on social networks is one of the hardest challenges in today’s society, with implications capable of changing users perception on what is a fact or rumor. Due to its complexity, there has been an overwhelming number of contributions from the research community like the analysis of specific events where rumors are spread, analysis of the propagation of false content on the network, or machine learning algorithms to distinguish what is a fact and what is “fake news”. In this paper, we identify and summarize some of the most prevalent works on the different categories studied. Finally, we also discuss the methods applied to deceive users and what are the next main challenges of this area.

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Author Biographies

Álvaro Figueira, CRACS-INESCTEC and University of Porto, Porto, Portugal

Álvaro Figueira graduated in “Mathematics Applied to Computer Science” from Faculty of Sciences (UP) in 1995. He got his MSc in “Foundations of Advanced Information Technology” from Imperial College, London, in 1997, and his PhD in Computer Science from UP, in 2004. Prof. Figueira is currently an Assistant Professor with tenure at Faculty of Sciences in University of Porto. His research interests are in the areas of web mining, community detection, web-based learning and social media automated analysis. He is a researcher in the CRACS/INESCTEC research unit where he has been leading international projects involving University of Texas at Austin, University of Porto, University of Coimbra and University of Aveiro, regarding the automatic detection of relevance in social networks.

Nuno Guimaraes, CRACS-INESCTEC and University of Porto, Porto, Portugal

Nuno Guimaraes is currently a PhD student in Computer Science at the Faculty of Sciences University of Porto and a researcher at the Center for Research in Advanced Computing Systems (CRACS – INESCTEC). His PhD is focused on the analysis and detection of unreliable information on social media. He had previously worked as a researcher in REMINDS project whose goal was to detect journalistically relevant information on Social Media. Nuno completed his master’s and bachelor’s degree in Computer Science at the Faculty of Sciences of the University of Porto. In his master’s thesis, he developed a novel way to create time and domain dependent sentiment lexicons in an unsupervised fashion.

Luis Torgo, Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

Luis Torgo is a Canada Research Chair (Tier 1) on Spatiotemporal Ocean Data Analytics and a Professor of Computer Science at the Faculty of Computer Science of the Dalhousie University, Canada. He also holds appointments as an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto, Portugal, and as an invited professor of the Stern Business School of the New York University where he has been teaching in recent years at the Master of Science in Business Analytics. Dr. Torgo has been doing research in the area of Data Mining and Machine Learning since 1990, and has published over 100 papers in several forums of these areas. Dr. Torgo is the author of the widely acclaimed Data Mining with R book published by CRC Press in 2010 with a strongly revised second edition that appeared in 2017. Dr. Torgo is also the CEO and one of the founding partners of KNOYDA a company devoted to training and consulting within data science.

References

Rasha A Abdulla, Bruce Garrison, Michael Salwen, Paul Driscoll, Denise Casey, Coral Gables, and Society Division. The credibility of newspapers, television news, and online news. 2002.

Hunt Allcot and Matthew Gentzkow. SOCIAL MEDIA AND FAKE NEWS IN THE 2016 ELECTION. 2017.

Amazon. Amazon comprehend. https://aws.amazon.com/ comprehend/. Accessed: 2018-03-12.

Qinglin Chen Mark Craft Anant Goel, Nabanita De. Fib – lets stop living a lie. https://devpost.com/software/fib, 2017. Accessed: 2018-06-18.

Sotirios Antoniadis, Iouliana Litou, and Vana Kalogeraki. A Model for Identifying Misinformation in Online Social Networks. 9415:473–482, 2015.

Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, James Glass, and Preslav Nakov. Predicting factuality of reporting and bias of news media sources. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP ’18, Brussels, Belgium, 2018.

F Benevenuto, G Magno, T Rodrigues, and V Almeida. Detecting spammers on twitter. Collaboration, electronic messaging, antiabuse and spam conference (CEAS), 6:12, 2010.

Alexandre Bovet and Hernan A. Makse. Influence of fake news in Twitter during the 2016 US presidential election. pages 1–23, 2018.

Carlos Castillo, Marcelo Mendoza, and Barbara Poblete. Information Credibility on Twitter. 2011.

Rogerio Chaves. Fake news detector. https://fakenewsdetector. org/en, 2018. Accessed: 2018-06-18.

Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. Detecting automation of Twitter accounts: Are you a human, bot, or cyborg? IEEE Transactions on Dependable and Secure Computing, 9(6): 811–824, 2012.

Giovanni Luca Ciampaglia, Prashant Shiralkar, Luis M. Rocha, Johan Bollen, Filippo Menczer, and Alessandro Flammini. Computational fact checking from knowledge networks. PLoS ONE, 10(6):1–13, 2015.

CNN. What we know about the boston bombing and its after math. https://edition.cnn.com/2013/04/18/us/boston-marathon-things-we-know, 2013. Accessed: 2018-06-12.

Sarah Cohen, Chengkai Li, JunYang, and CongYu. Computational Journalism: a call to arms to database researchers. Proceedings of the 5th Biennial Conference on Innovative Data Systems Research (CIDR 2011) Asilomar, California, USA., (January):148–151, 2011.

David Conn. How the sun’s ‘truth’ about hillsborough unravelled. https://www.theguardian.com/football/2016/apr/26/how-the-suns-truth-about-hillsborough-unravelled, 2016. Accessed: 2018-06-07.

John P. Dickerson, Vadim Kagan, and V. S. Subrahmanian. Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? ASONAM 2014 – Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (Asonam):620–627, 2014.

Buket Ers¸ahin, Özlem AktaÅŸ, Deniz Kilmç, and Ceyhun Akyol. Twitter fake account detection. 2nd International Conference on Computer Science and Engineering, UBMK 2017, pages 388–392, 2017.

FakeNewsChallenge.org. Stance detection dataset for fnc-1. https://github.com/FakeNewsChallenge/fnc-1, 2017. Accessed: 2018-04-12.

Emilio Ferrara, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. The rise of social bots. Commun. ACM, 59(7):96–104, June 2016.

William Ferreira and Andreas Vlachos. Emergent: a novel data-set for stance classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1163–1168. Association for Computational Linguistics, 2016.

Álvaro Figueira and Luciana Oliveira. The current state of fake news: challenges and opportunities. Procedia Computer Science, 121(December):817–825, 2017.

Zafar Gilani, Ekaterina Kochmar, and Jon Crowcroft. Classification of TwitterAccounts intoAutomatedAgents and Human Users. Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 –

Google. Cloud natural language. https://cloud.google.com/natural-language/. Accessed: 2018-03-12.

B Y Jeffrey Gottfried and Elisa Shearer. News Use Across Social Media Platforms 2017. Pew Research Center, Sept 2017(News Use Across Social Media Platforms 2017):17, 2017.

Aditi Gupta. Twitter Explodes with Activity in Mumbai Blasts! A Lifeline or an Unmonitored Daemon in the Lurking? Precog.Iiitd.Edu.in, (September 2017):1–17, 2011.

Aditi Gupta, Hemank Lamba, and Ponnurangam Kumaraguru. $1.00 per RT #BostonMarathon #PrayForBoston: Analyzing fake content on twitter. eCrime Researchers Summit, eCrime, 2013.

Twitter Help. About verified accounts. https://help.twitter.com/en/managing-your-account/about-twitter-verified-accounts, 2018. Accessed: 2018-05-14.

Alex Hern. Google acts against fake news on search engine. https://www.theguardian.com/technology/2017/apr/25/google-launches-major-offensive-against-fake-news, 2017. Accessed: 2018-04-13.

Alex Hern. New facebook controls aim to regulate political ads and fight fake news. https://www.theguardian.com/technology/2018/apr/06/facebook-launches-controls-regulate-ads-publishers, 2018. Accessed: 2018-04-13.

IBM. Ibm cloud docs natural language understanding. https://console.bluemix.net/docs/services/natural-language-understanding/getting-started. html. Accessed: 2018-03-12.

Hamid Karimi, Courtland VanDam, Liyang Ye, and Jiliang Tang. End-to-end compromised account detection. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 314–321. IEEE, 2018.

Johannes Kiesel, Maria Mestre, Rishabh Shukla, Emmanuel Vincent, David Corney, Payam Adineh, Benno Stein, and Martin Potthast. Data for PAN at SemEval 2019 Task 4: Hyperpartisan News Detection, November 2018.

Bence Kollanyi, Philip N. Howard, and Samuel C. Woolley. Bots and Automation over Twitter during the First U.S. Election. Data Memo, (4):1–5, 2016.

Sejeong Kwon, Meeyoung Cha, Kyomin Jung, Wei Chen, and Yajun Wang. Prominent features of rumor propagation in online social media. In 2013 IEEE 13th International Conference on Data Mining, pages 1103–1108. IEEE, 2013.

Iouliana Litou, Vana Kalogeraki, Ioannis Katakis, and Dimitrios Gunopulos. Real-time and cost-effective limitation of misinformation propagation. Proceedings – IEEE International Conference on Mobile Data Management, 2016-July:158–163, 2016.

Microsoft. Text analytics api documentation. https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/. Accessed: 2018-03-12.

Tim Miller. Explanation in artificial intelligence: Insights from the social sciences. CoRR, abs/1706.07269, 2017.

Damian Mrowca and Elias Wang. Stance Detection for Fake News Identification. pages 1–12, 2017.

Richard Norton-Taylor. Zinoviev letter was dirty trick by mi6. https://www.theguardian.com/politics/1999/feb/04/uk.political_news6, 1999. Accessed: 2018-06-07.

OpenSources. Opensources -professionally curated lists of online sources, available free for public use. http://www.opensources.co/, 2018. Accessed: 2018-05-03.

PAN. Hyperpartisan news detection. “https://pan.webis.de/seme val19/semeval19-web/”. [Accessed: 2019-03-14].

Verónica Pérez-Rosas, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. Automatic Detection of Fake News. 2017.

Stephen Pfohl, Oskar Triebe, and Ferdinand Legros. Stance Detection for the Fake News Challenge with Attention and Conditional Encoding. pages 1–14, 2016.

Ben Popken. Twitter deleted 200,000 russian troll tweets. read them here., 2018. [Online; accessed 13-March-2019].

Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. A Stylometric Inquiry into Hyperpartisan and Fake News. 2017.

Martin Potthast, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. Buzzfeed-webis fake news corpus 2016, February 2018.

Jacob Ratkiewicz, Michael D Conover, Mark Meiss, Bruno Gonçalves, Alessandro Flammini, and Filippo Menczer Menczer. Detecting and tracking political abuse in social media. In Fifth international AAAI conference on weblogs and social media, 2011.

Megan Risdal. Getting real about fake news. https://www.kaggle.com/mrisdal/fake-news, 2016. Accessed: 2019-03-14.

Chengcheng Shao, Giovanni Luca Ciampaglia, Alessandro Flammini, and Filippo Menczer. Hoaxy: A Platform for Tracking Online Misinformation. pages 745–750, 2016.

Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Alessandro Flammini, and Filippo Menczer. The spread of fake news by social bots. arXiv preprint arXiv:1707.07592, pages 96–104, 2017.

Chengcheng Shao, Giovanni Luca Ciampaglia, Onur Varol, Kaicheng Yang, Alessandro Flammini, and Filippo Menczer. The spread of low-credibility content by social bots. 2017.

Prashant Shiralkar, Alessandro Flammini, Filippo Menczer, and Giovanni Luca Ciampaglia. Finding Streams in Knowledge Graphs to Support Fact Checking. 2017.

John Merriman Sholar, Shahil Chopra, and Saachi Jain. Towards Automatic Identification of Fake News : Headline-Article Stance Detection with LSTM Attention Models. 1:1–15, 2017.

Kai Shu, Xinyi Zhou, Suhang Wang, Reza Zafarani, and Huan Liu. The Role of User Profiles for Fake News Detection.

Craig Silverman, Jane Lytvynenko, Lam Vo, and Jeremy Singer-Vine. Inside the partisan fight for your news feed, 2017. [Online; accessed 13-March-2019].

Snopes. Fact-check: Comet ping pong pizzeria home to child abuse ring led by hillary clinton. https://www.snopes.com/fact-check/pizzagate-conspiracy/, 2016. Accessed: 2018-04-13.

Kate Starbird, Jim Maddock, Mania Orand, Peg Achterman, and Robert M Mason. Rumors, False Flags, and Digital Vigilantes: Misinformation on Twitter after the 2013 Boston Marathon Bombing. iConference 2014 Proceedings, 2014.

Maciej Szpakowski. Fake news corpus. https://github.com/several27/FakeNewsCorpus, 2018.

Eugenio Tacchini, Gabriele Ballarin, Marco L. Della Vedova, Stefano Moret, and Luca deAlfaro. Some Like it Hoax: Automated Fake News Detection in Social Networks. pages 1–12, 2017.

Marcella Tambuscio, Giancarlo Ruffo, Alessandro Flammini, and Filippo Menczer. Fact-checking Effect on Viral Hoaxes: A Model of Misinformation Spread in Social Networks. pages 977–982, 2015.

TextRazor. Text razor – extract meaning from your text. https://www.textrazor.com/. Accessed: 2018-03-12.

LLC. The Self Agency. B.s. detector – a browser extension that alerts users to unreliable news sources. http://bsdetector.tech/, 2016. Accessed: 2018-06-18.

Robert Thomson, Naoya Ito, Hinako Suda, Fangyu Lin, Yafei Liu, Ryo Hayasaka, Ryuzo Isochi, and Zian Wang. Trusting Tweets : The Fukushima Disaster and Information Source Credibility on Twitter. Iscram, (April):1–10, 2012.

Courtland VanDam and Pang-Ning Tan. Detecting hashtag hijacking from twitter. In Proceedings of the 8th ACM Conference on Web Science, pages 370–371. ACM, 2016.

Chris J Vargo, Lei Guo, and Michelle A Amazeen. The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016. New Media & Society, page 146144481771208, 2017.

Soroush Vosoughi, Deb Roy, and Sinan Aral. The spread of true and false news online. Science, 359(6380):1146–1151, 2018.

William Yang Wang. “liar, liar pants on fire”: A new benchmark dataset for fake news detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 422–426. Association for Computational Linguistics, 2017.

Arkaitz Zubiaga, Geraldine Wong Sak Hoi, Maria Liakata, Rob Procter, and Peter Tolmie. Analysing how people orient to and spread rumours in social media by looking at conversational threads. In PloS one, 2016.

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