On the Impact of Emotions on the Detection of False Information
Pages 277 - 282
Abstract
A great amount of fake news are propagated in online social media, with the aim, usually, to deceive users and formulate specific opinions. The threat is even greater when the purpose is political or ideological and they are used during electoral campaigns. Bots play a key role in disseminating these false claims. False information is intentionally written to trigger emotions to the readers in an attempt to be believed and be disseminated in social media. Therefore, in order to discriminate credible from non credible information, we believe that it is important to take into account these emotional signals. In this paper we describe the way that emotional features have been integrated in deep learning models in order to detect if and when emotions are evoked in fake news.
References
[1]
Chakraborty, B. Paranjape, S. Kakarla, and N. Ganguly. Stop clickbait: Detecting and preventing clickbaits in online news media. In Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 9–16. IEEE Press, 2016.
[2]
J. C. de Albornoz, L. Plaza, and P. Gerv´as. Sentisense: An easily scalable concept-based affective lexicon for sentiment analysis. In LREC, pages 3562–3567, 2012.
[3]
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, 2019.
[4]
E. Fast, B. Chen, and M. S. Bernstein. Empath: Understanding topic signals in large-scale text. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 4647–4657. ACM, 2016.
[5]
Ghanem, P. Rosso, and F. Rangel. An emotional analysis of false information in social media and news articles. ACM Transactions on Internet Technology (TOIT), 20(2):1–18, 2020.
[6]
A. Giachanou, P. Rosso, and F. Crestani. Leveraging emotional signals for credibility detection. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’19, page 877–880, New York, NY, USA, 2019. Association for Computing Machinery.
[7]
Giachanou, P. Rosso, I. Mele, and F. Crestani. Emotional influence prediction of news posts. In Proceedings of the 12th International AAAI Conference on Web and Social Media, ICWSM ’18, pages 592–595, 2018.
[8]
J. Graham, J. Haidt, and B. A. Nosek. Liberals and Conservatives Rely on Different Sets of Moral Foundations. Journal of personality and social psychology, 96(5):1029, 2009.
[9]
S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
[10]
D. Horne and S. Adali. This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News. In Eleventh International AAAI Conference on Web and Social Media, 2017.
[11]
A.Karduni, R. Wesslen, S. Santhanam, I. Cho, S. Volkova, D. Arendt, S. Shaikh, and W. Dou. Can you verifi this? studying uncertainty and decision-making about misinformation using visual analytics. 2018.
[12]
S. M. Mohammad. Word affect intensities. In Proceedings of the 11th International Conference on Language Resources and Evaluation, pages 174–183, 2018.
[13]
S. M. Mohammad and P. D. Turney. Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, pages 26–34. Association for Computational Linguistics, 2010.
[14]
S. M. Mohammad and P. D. Turney. Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3):436–465, 2013.
[15]
J. Pennington, R. Socher, and C. Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language, EMNLP ’14, pages 1532–1543, 2014.
[16]
V. P´erez-Rosas, B. Kleinberg, A. Lefevre, and R. Mihalcea. Automatic Detection of Fake News. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3391–3401, Santa Fe, New Mexico, USA, Aug. 2018. Association for Computational Linguistics.
[17]
K. Popat, S. Mukherjee, A. Yates, and G. Weikum. Declare: Debunking fake news and false claims using evidence-aware deep learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language, EMNLP ’18, pages 22–32, 2018.
[18]
S. Poria, A. Gelbukh, A. Hussain, N. Howard, D. Das, and S. Bandyopadhyay. Enhanced senticnet with affective labels for concept-based opinion mining. IEEE Intelligent Systems, 28(2):31–38, 2013.
[19]
F. Rangel, A. Giachanou, B. Ghanem, and P. Rosso. Overview of the 8th Author Profiling Task at PAN 2020: Profiling Fake News Spreaders on Twitter (In Press). In L. Cappellato, C. Eickhoff, N. Ferro, and A. N´ev´eol, editors, CLEF 2020 Labs and Workshops, Notebook Papers. CEUR-WS.org, Sept. 2020.
[20]
F. Rangel and P. Rosso. On the impact of emotions on author profiling. Information processing & management, 52(1):73–92, 2016.
[21]
F. Rangel and P. Rosso. Overview of the 7th Author Profiling Task at PAN 2019: Bots and Gender Profiling. In CLEF 2019 labs and workshops, notebook papers. CEUR Workshop Proceedings. CEUR-WS.org., volume 2380, 2019.
[22]
F. Rangel, P. Rosso, and M. Franco-Salvador. A Low Dimensionality Representation for Language Variety Identification. In In 17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing’16. Springer-Verlag, LNCS(9624), pages 156–169, 2018.
[23]
H. Rashkin, E. Choi, J. Y. Jang, S. Volkova, and Y. Choi. Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2931–2937, 2017.
[24]
G. Sidorov, S. Miranda-Jim´enez, F. Viveros-Jim´enez, A. Gelbukh, N. Castro-S´anchez, F. Vel´asquez, I. Dıaz-Rangel, S. Su´arez-Guerra, A. Trevino, and J. Gordon. Empirical study of opinion mining in spanish tweets. micai 2012. Lect Notes Comput Sci, 7629:1–14, 2012.
[25]
Y. R. Tausczik and J. W. Pennebaker. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of language and social psychology, 29(1):24–54, 2010.
[26]
S. Volkova, K. Shaffer, J. Y. Jang, and N. Hodas. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), volume 2, pages 647–653, 2017.
[27]
S. Vosoughi, D. Roy, and S. Aral. The Spread of True and False News Online. Science, 359(6380):1146–1151, 2018.
[28]
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy. Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pages 1480–1489, 2016.
[29]
S. Zannettou, M. Sirivianos, J. Blackburn, and N. Kourtellis. The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans. arXiv preprint arXiv:1804.03461, 2018.
Index Terms
- On the Impact of Emotions on the Detection of False Information
Index terms have been assigned to the content through auto-classification.
Recommendations
Using Attack Information to Reduce False Positives in Network IDS
ISCC '06: Proceedings of the 11th IEEE Symposium on Computers and CommunicationsReducing the rate of false positives is of vital importance in enhancing the usefulness of signature-based network intrusion detection systems (NIDSs). To reduce false positives, a network administrator must throughly investigate a lengthy list of ...
Enhancing the Accuracy of Intrusion Detection Systems by Reducing the Rates of False Positives and False Negatives Through Multi-objective Optimization
Intrusion detection systems (IDSs) are the fundamental parts of any network security infrastructure given their role as layers of defense against hackers. However, IDSs generate frequent instances of false alerts and miss a lot of real attacks that ...
Recognizing Induced Emotions of Movie Audiences from Multimodal Information
Recognizing emotional reactions of movie audiences to affective movie content is a challenging task in affective computing. Previous research on induced emotion recognition has mainly focused on using audio-visual movie content. Nevertheless, the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
February 2021
644 pages
ISBN:9781450389839
DOI:10.1145/3459104
Copyright © 2021 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 July 2021
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ISEEIE 2021
ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering
February 19 - 21, 2021
Seoul, Republic of Korea
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 81Total Downloads
- Downloads (Last 12 months)19
- Downloads (Last 6 weeks)1
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format