Media-Rich Fake News Detection: A Survey
Abstract
Fake News has been around for decades and with the advent of social media and modern day journalism at its peak, detection of media-rich fake news has been a popular topic in the research community. Given the challenges associated with detecting fake news research problem, researchers around the globe are trying to understand the basic characteristics of the problem statement. This paper aims to present an insight on characterization of news story in the modern diaspora combined with the differential content types of news story and its impact on readers. Subsequently, we dive into existing fake news detection approaches that are heavily based on text-based analysis, and also describe popular fake news data-sets. We conclude the paper by identifying 4 key open research challenges that can guide future research.
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Other researchers focus on content to categorize fake news by distinguishing different formats and content types of data in the news.
“However, other researchers (Parikh and Atrey 2018; Fraga-Lamas and Fernández-Caramés 2020; Hasan and Salah 2019; Masciari et al. 2020; Bakdash et al. 2018; Elhadad et al. 2019; Yang et al. 2019b) focus on the content to categorize types of fake news through distinguishing the different formats and content types of data in the news (e.g., text and/or multimedia).”
From Fake news, disinformation and misinformation in social media: a review (2023) · cites this paper as Parikh and Atrey 2018
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Sorted by citations
Fake news, disinformation and misinformation in social media: a review
Aïmeur, E., Amri, S., & Brassard, G. (2023) · Social Network Analysis and Mining · doi:10.1007/s13278-023-01028-5 · Cited by 772 · see citing works
- Authors
- Parikh, S. B., & Atrey, P. K.
- Affiliations
- State University of New York (US)
- ORCID
- Atrey, Pradeep K.
- Venue
- 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 436-441
- OpenAlex
- W2810806782
- Cited by
- 237
- References
- 0
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 35 · 2023: 44 · 2022: 48 · 2021: 39 · 2020: 24 · 2019: 22 · 2018: 1 · 2013: 1
- FWCI
- 65.81
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR) (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- fake news detection, social media, fake news datasets, open problems
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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