False Information on Web and Social Media: A Survey

By Srijan Kumar, Neil Shah. arXiv.org, 2018

396 citations recorded by the source corpus.

False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characterizing how false information proliferates on social platforms and why it succeeds in deceiving readers are critical to develop efficient detection algorithms and tools for early detection. A recent surge of research in this area has aimed to address the key issues using methods based on feature engineering, graph mining, and information modeling. Majority of the research has primarily focused on two broad categories of false information: opinion-based (e.g., fake reviews), and fact-based (e.g., false news and hoaxes). Therefore, in this work, we present a comprehensive survey spanning diverse aspects of false information, namely (i) the actors involved in spreading false information, (ii) rationale behind successfully deceiving readers, (iii) quantifying the impact of false information, (iv) measuring its characteristics across different dimensions, and finally, (iv) algorithms developed to detect false information. In doing so, we create a unified framework to describe these recent methods and highlight a number of important directions for future research.

Subfields: Information Systems, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1bb23fd4252a76b55ff77ed982fc78c3e06d1452

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Citations in the corpus

Fake news, disinformation and misinformation in social media: a review (2023, neutral): “6 Hence, fake news can have a significant impact on society as manipulated and false content is easier to generate and harder to detect (Kumar and Shah 2018) and as disinfor-mation actors change their tactics (Kumar and Shah 2018; Micallef et al. 2020).”
Knowledge Conflicts for LLMs: A Survey (2024, background): “Previous research has shown that LMs can be significantly influenced by misinformation or outdated information within a specific context (Zhang and Choi, 2021; Du et al., 2022b).”
FakeBERT: Fake news detection in social media with a BERT-based deep learning approach (2021, background): “provide a quick platform for information sharing [10, 21].”
REV2: Fraudulent User Prediction in Rating Platforms (2018, background): “Bitcoins with, there is a huge monetary incentive for fraudulent users to give fake ratings [11, 13, 15, 34].”
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media (2018, background): “For example, some individuals and organizations spread fake news in social media for financial and political gains.(2,3) It is also reported that fake news has an influence on the 2016 U.”
Combating Fake News (2019, background): “Specifically, adversaries create new accounts and throw-away accounts to promote fake news and avoid detection [54], as can be seen in an instance of the US election when potentially large number of fake accounts were created to influence the election and were found to initiate coordinated attacks w”
All You Need is "Love": Evading Hate Speech Detection (2018, background): “While the ability of ordinary people to reach thousands of others instantenously undoubtedly has positive effects, downsides like polarization via echo chambers [13] have become apparent.”
Community Interaction and Conflict on the Web (2018, background): “forms has focused primarily on controversies [3, 13, 20, 21, 45, 52] and anti-social behavior, in the form of trolling [11, 12, 42], sockpuppetry [39], harassment and cyberbullying [8, 23, 32, 35, 55, 66, 79], vandalism [43], hate speech [9, 15, 51, 56, 72], and others [17, 24, 40, 41, 44, 46, 54, 6”