The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index.
On Sentiment of Online Fake News
Abstract
The presence of disinformation and fake news on the Internet and especially social media has become a major concern. Prime examples of such fake news surged in the 2016 U.S. presidential election cycle and the COVID-19 pandemic. We quantify sentiment differences between true and fake news on social media using a diverse body of datasets from the literature that contains about 100K previously labeled true and fake news. We also experiment with a variety of sentiment analysis tools. We model the association between sentiment and veracity as conditional probability and also leverage statistical hypothesis testing to uncover the relationship between sentiment and veracity. With a significance level of 99.999%, we observe a statistically significant relationship between negative sentiment and fake news and between positive sentiment and true news. The degree of association, as measured by Goodman and Kruskal's gamma, ranges between. 037 to. 475. Finally, we make our data and code publicly available to support reproducibility. Our results assist in the development of automatic fake news detectors.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“For example, Zaeem et al. observed a statistically significant relationship between negative sentiment and fake news (Zaeem et al., 2020)”
From The Psycho-Emotive Effects of AI-Generated Fake News on Social Media Users (2026) · cites this paper as Zaeem et al., 2020
Citations
Cites
Social Media and Fake News in the 2016 Election
Allcott, H., & Gentzkow, M. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.211 · Cited by 6,767
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
Tandoc, E. C., Lim, Z. W., & Ling, R. S. (2017) · Digital Journalism · doi:10.1080/21670811.2017.1360143 · Cited by 2,745
Information credibility on twitter
Castillo, C., Mendoza, M., & Poblete, B. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963500 · Cited by 2,593
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Wang, W. Y. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2067 · Cited by 2,010
Accuracy of Deception Judgments
Bond, C. F., & DePaulo, B. M. (2006) · Personality and Social Psychology Review · doi:10.1207/s15327957pspr1003_2 · Cited by 1,953
Nonverbal Leakage and Clues to Deception †
Ekman, P., & Friesen, W. V. (1969) · Psychiatry · doi:10.1080/00332747.1969.11023575 · Cited by 1,854
Lying Words: Predicting Deception from Linguistic Styles
Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003) · Personality and Social Psychology Bulletin · doi:10.1177/0146167203029005010 · Cited by 1,489
Shu, K., Mahudeswaran, D., Wang, S., Lee, D., & Liu, H. (2018) · Big Data · doi:10.1089/big.2020.0062 · Cited by 1,362
Automatic deception detection: Methods for finding fake news
Conroy, N. K., Rubin, V. L., & Chen, Y. (2015) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2015.145052010082 · Cited by 1,066
Flagged first, then by citations
1 of the 25 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Emotion detection for misinformation: A review
Liu, Z., Zhang, T., Yang, K., Thompson, P., Yu, Z., & Ananiadou, S. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102300 · Cited by 101
Uppada, S. K., Manasa, K., Vidhathri, B., Harini, R., & Sivaselvan, B. (2022) · Social Network Analysis and Mining · doi:10.1007/s13278-022-00878-9 · Cited by 47
Deceptive reviews and sentiment polarity: Effective link by exploiting BERT
Catelli, R., Fujita, H., Pietro, G. D., & Esposito, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118290 · Cited by 43
Ahammad, T. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100053 · Cited by 34
Bhardwaj, A., Bharany, S., & Kim, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36049 · Cited by 33
Metadata
- Authors
- Zaeem, R. N., Li, C., & Barber, K. S.
- Affiliations
- The University of Texas at Austin (US)
- Venue
- 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
- Publisher
- IEEE
- Published
- 2020-12-07
- Pages
- 760-767
- OpenAlex
- W3142258528
- Cited by
- 61
- References
- 25
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 14 · 2023: 28 · 2022: 6 · 2021: 3
- FWCI
- 19.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (publishedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- fake news, disinformation, sentiment analysis, social media, 2016 US presidential election, COVID-19 misinformation
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
