Preprint Article Version 2 Preserved in Portico This version is not peer-reviewed

Understanding the Strategies of Creating Fake News in Social Media

Version 1 : Received: 11 November 2020 / Approved: 13 November 2020 / Online: 13 November 2020 (10:05:52 CET)
Version 2 : Received: 27 November 2020 / Approved: 27 November 2020 / Online: 27 November 2020 (16:43:48 CET)

How to cite: Vatsalan, D.; Arachchilage, N.A. Understanding the Strategies of Creating Fake News in Social Media. Preprints 2020, 2020110369 (doi: 10.20944/preprints202011.0369.v2). Vatsalan, D.; Arachchilage, N.A. Understanding the Strategies of Creating Fake News in Social Media. Preprints 2020, 2020110369 (doi: 10.20944/preprints202011.0369.v2).

Abstract

Social media giants like Facebook are struggling to keep up with fake news, in the light of the fact that disinformation diffuses at lightning speed. For example, the COVID-19 (i.e. Coronavirus) pandemic is testing the citizens' ability to distinguish real news from falsifying facts (i.e. disinformation). Cyber-criminals take advantage of the inability to cope with fake news diffusion on social media platforms. Fake news, created as a means to manipulate readers to perform various malicious IT activities such as clicking on fraudulent links associated with the fake news/posts. However, no previous study has investigated the strategies used to create fake news on social media. Therefore, we have analysed five data-sets using Machine Learning (ML) that contain online news articles (i.e. both fake and legitimate news) to investigate strategies of creating fake news on social media platforms. Our study findings revealed a threat model understanding strategies of crafting fake news which may highly likely diffuse on social media platforms.

Subject Areas

usable security; fake news; emotions; sentiment analysis; machine learning

Comments (1)

Comment 1
Received: 27 November 2020
Commenter: Nalin Asanka Gamagedara Arachchilage
Commenter's Conflict of Interests: Author
Comment: Based on the feedback from our lab/group members, we have done numerous amendments to abstract, methodology and data analysis sections reflecting the fact that offering fake news content analysis using matching leaning view rather than merely a cognitive science-based approach (i.e. user-study) into the problem of fake news detection. In addition, after having a discussion with the main/first/leading author, we have made an amendment to the authorship reflecting the Australian Research Council (ARC) guidelines. 
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