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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.

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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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Authors and Affiliations

  1. Department of Computer Science and Engineering, University at Buffalo, Buffalo, USA

    Alina Vereshchaka & Wen Dong

  2. Department of English, University of Nevada, Reno, Reno, USA

    Seth Cosimini

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  1. Alina Vereshchaka
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  2. Seth Cosimini
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  3. Wen Dong
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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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