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Analyzing and distinguishing fake and real news to mitigate the problem of disinformation

  • S.I. : SBP-BRiMS 2019
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Abstract

Identifying fake news has become an important issue. Increasing usage of social media has led to an increase in the number of people who can be influenced, thus the spread of fake news can potentially impact important events. Fake news has become a major societal issue and a technical challenge for social media companies to identify and has led many to extreme measures, such as WhatsApp deleting two million of its users every month to prevent the spread of fake news. The current problem of fake news is rooted in the historical problem of disinformation, which is false information intentionally, and usually clandestinely, disseminated to manipulate public opinion or obfuscate the truth. Our work addresses the problem of identifying fake news by (i) detecting and analyzing fake news features (ii) identifying the textual and sociocultural characteristics fake news features.

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Notes

  1. https://github.com/KaiDMML/FakeNewsNet.

References

  • Allcott H, Gentzkow M (2017) Social media and fake news in the 2016 election. J Econ Perspect 31(2):211–36

    Article  Google Scholar 

  • Bisgin H, Arslan H, Korkmaz Y (2019) Analyzing the Dabiq magazine: the language and the propaganda structure of ISIS. In: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation. Springer, Berlin, pp 1–11

  • Bluemle SR (2018) Post-facts: information literacy and authority after the 2016 election. Portal Libr Acad 18(2):265–282

    Article  Google Scholar 

  • Carillo EC (2019) Navigating this perfect storm: teaching critical reading in the face of the common core state standards, fake news, and google. Pedag Crit Approaches Teach Lit Lang Compos Cult 19(1):135–159

    Google Scholar 

  • Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint. arXiv:1406.1078

  • Dumais S, Chen H (2000) Hierarchical classification of web content. In Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval. ACM, New York, pp 256–263

  • Fash LG (2017) Information literacy in the American literature classroom. Transformations 27(2):195–201

    Article  Google Scholar 

  • Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford 1(12)

  • Goldstein DE (2018) Never remember: fake news turning points and vernacular critiques of bad faith communication. J Am Folklore 131(522):471–481

    Article  Google Scholar 

  • Grinberg N, Joseph K, Friedland L, Swire-Thompson B, Lazer D (2019) Fake news on twitter during the 2016 us presidential election. Science 363(6425):374–378

    Article  Google Scholar 

  • Hall S (2001) Encoding/decoding in culture, media, language. In: Reprinted from Media and cultural studies. Blackwell, Malden, pp 166–176

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  • Jones K (2018) ‘A man who revels in his own ignorance, racism and misogyny’: identifiable referents trump indefinite grammar. Funct Linguist 5(1):11

    Article  Google Scholar 

  • Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv preprint. arXiv:1607.01759

  • Khaldarova I, Pantti M (2016) Fake news: the narrative battle over the ukrainian conflict. Journal Pract 10(7):891–901

    Google Scholar 

  • Lai S, Xu L, Liu K, Zhao J (2015) Recurrent convolutional neural networks for text classification. In: Twenty-ninth AAAI conference on artificial intelligence

  • Lee JY, Dernoncourt F (2016) Sequential short-text classification with recurrent and convolutional neural networks. arXiv preprint. arXiv:1603.03827

  • Lenker M (2017) Developmentalism: learning as the basis for evaluating information. Portal Libr Acad 17(4):721–737

    Article  Google Scholar 

  • Li S, Kulkarni G, Berg TL, Berg AC, Choi Y (2011) Composing simple image descriptions using web-scale n-grams. In: Proceedings of the fifteenth conference on computational natural language learning. Association for Computational Linguistics, Portland, pp 220–228

  • Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong KF, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks. In: IJCAI, pp 3818–3824

  • Mikolov T, Karafiát M, Burget L, Černockỳ J, Khudanpur S (2010) Recurrent neural network based language model. In: Eleventh annual conference of the international speech communication association

  • Miller TP, Leon A (2017) Introduction to special issue on literacy, democracy, and fake news: making it right in the era of fast and slow literacies. Lit Compos Stud 5(2):10–23

    Google Scholar 

  • Nelson JL, Taneja H (2018) The small, disloyal fake news audience: the role of audience availability in fake news consumption. New Media Soc 20(10):3720–3737

    Article  Google Scholar 

  • Olah C (2015) Understanding LSTM networks. http://colah.github.io/posts/2015-08-Understanding-LSTMs/. Accessed 19 Mar 2020

  • Popat K, Mukherjee S, Yates A, Weikum G (2018) Declare: debunking fake news and false claims using evidence-aware deep learning. arXiv preprint. arXiv:1809.06416

  • Rashkin H, Choi E, Jang JY, Volkova S, Choi Y (2017) Truth of varying shades: analyzing language in fake news and political fact-checking. In: Proceedings of the 2017 conference on empirical methods in natural language processing, pp 2931–2937

  • Ruchansky N, Seo S, Liu Y (2017) Csi: A hybrid deep model for fake news detection. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 797–806

  • Sapkota U, Bethard S, Montes M, Solorio T (2015) Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 93–102

  • Shu K, Sliva A, Wang S, Tang J, Liu H (2017a) Fake news detection on social media: A data mining perspective. ACM SIGKDD Explor Newslett 19(1):22–36

    Article  Google Scholar 

  • Shu K, Wang S, Liu H (2017b) Exploiting tri-relationship for fake news detection. arXiv preprint. arXiv:1809.01286

  • Shu K, Mahudeswaran D, Wang S, Lee D, Liu H (2018) FakeNewsNet: a data repository with news content, social context and dynamic information for studying fake news on social media. arXiv preprint. arXiv:1809.01286

  • Wang AH (2010) Don’t follow me: spam detection in Twitter. In: 2010 international conference on security and cryptography (SECRYPT). IEEE, pp 1–10

  • Wang X, McCallum A, Wei X (2007) Topical n-grams: phrase and topic discovery, with an application to information retrieval. In: Seventh IEEE international conference on data mining (ICDM 2007). IEEE, pp 697–702

  • Wang X, Wei F, Liu X, Zhou M, Zhang M (2011) Topic sentiment analysis in Twitter: a graph-based hashtag sentiment classification approach. In: Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, New York, , pp 1031–1040

  • Wang Y, Yang W, Ma F, Xu J, Zhong B, Deng Q, Gao J (2019) Weak supervision for fake news detection via reinforcement learning. arXiv preprint. arXiv:1912.12520

  • Wu L, Morstatter F, Carley KM, Liu H (2019) Misinformation in social media: definition, manipulation, and detection. ACM SIGKDD Explor Newslett 21(2):80–90

    Article  Google Scholar 

  • Yao L, Mao C, Luo Y (2019) Graph convolutional networks for text classification. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 7370–7377

  • Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. In: Advances in neural information processing systems, pp 649–657

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Acknowledgements

We want to thank Kathleen M. Carley and Nitin Agarwal for organizing the Disinformation Challenge as part of the International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation, 2019, Washington, DC, USA. Our winning work for the challenge became the basis for this paper.

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Correspondence to Alina Vereshchaka.

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Vereshchaka, A., Cosimini, S. & Dong, W. Analyzing and distinguishing fake and real news to mitigate the problem of disinformation. Comput Math Organ Theory 26, 350–364 (2020). https://doi.org/10.1007/s10588-020-09307-8

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