Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
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TLDR
Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.Abstract:
Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of \fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ine ective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.read more
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Book ChapterDOI
Textual Characteristics of News Title and Body to Detect Fake News: A Reproducibility Study
Anu Shrestha,Francesca Spezzano +1 more
TL;DR: In this article, the authors presented their attempt to reproduce the same results to validate their findings, and showed which of their findings can be generalized to larger political and gossip news datasets.
Journal ArticleDOI
Detecting malicious behavior in social platforms via hybrid knowledge- and data-driven systems
TL;DR: In this article, the authors implement and carry out an empirical evaluation of a version of the recently-proposed NetDER architecture for hybrid AI decision-support systems with the capability of leveraging the availability of machine learning modules, logical reasoning about unknown objects, and forecasts based on diffusion processes.
Journal ArticleDOI
NetDER: An Architecture for Reasoning About Malicious Behavior
Jose N. Paredes,Gerardo I. Simari,Gerardo I. Simari,Maria Vanina Martinez,Marcelo Alejandro Falappa +4 more
TL;DR: The working hypothesis is that these problems – and many others – can be effectively tackled by combining multiple data sources that are constantly being updated, and maintaining a knowledge base using logic-based formalisms capable of value invention to support generating hypotheses based on available data.
Journal ArticleDOI
A deep dive into COVID-19-related messages on WhatsApp in Pakistan
R Tallal Javed,Muhammad Usama,Waleed Iqbal,Junaid Qadir,Gareth Tyson,Ignacio Castro,Kiran Garimella +6 more
TL;DR: In this paper, an extended overview of how Pakistan's population used public WhatsApp groups for sharing information related to the COVID-19 pandemic is given. But, the work is based on a major effort to annotate thousands of text and image-based messages.
Book ChapterDOI
Brazilian Presidential Elections in the Era of Misinformation: A Machine Learning Approach to Analyse Fake News
TL;DR: Examination of a sample database of the 2018 Brazilian election articles shared by Brazilians over social media platforms finds that the developments in deep learning could potentially benefit fake news research.
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