An Emotional Analysis of False Information in Social Media and News Articles

[EN] Fake news is risky since it has been created to manipulate the readers' opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news articles sources. Our experiments showed that false information has different emotional patterns in each of its types, and emotions play a key role in deceiving the reader. Based on that, we proposed a LSTM neural network model that is emotionally-infused to detect false news.The work of the second author was partially funded by the Spanish MICINN under the research project MISMISFAKEnHATE on Misinformation and Miscommunication in social media: FAKEnews and HATE speech (PGC2018-096212B-C31).Ghanem, BHH.; Rosso, P.; Rangel, F. (2020). An Emotional Analysis of False Information in Social Media and News Articles. ACM Transactions on Internet Technology. 20(2):1-18. https://doi.org/10.1145/3381750S118202Magda B. Arnold. 1960. Emotion and Personality. Columbia University Press. Magda B. Arnold. 1960. Emotion and Personality. Columbia University Press.Bhatt, G., Sharma, A., Sharma, S., Nagpal, A., Raman, B., & Mittal, A. (2018). Combining Neural, Statistical and External Features for Fake News Stance Identification. Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW ’18. doi:10.1145/3184558.3191577Castillo, C., Mendoza, M., & Poblete, B. (2011). Information credibility on twitter. Proceedings of the 20th international conference on World wide web - WWW ’11. doi:10.1145/1963405.1963500Chakraborty, A., Paranjape, B., Kakarla, S., & Ganguly, N. (2016). Stop Clickbait: Detecting and preventing clickbaits in online news media. 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). doi:10.1109/asonam.2016.7752207Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3-4), 169-200. doi:10.1080/02699939208411068Ghanem, B., Rosso, P., & Rangel, F. (2018). Stance Detection in Fake News A Combined Feature Representation. Proceedings of the First Workshop on Fact Extraction and VERification (FEVER). doi:10.18653/v1/w18-5510Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735-1780. doi:10.1162/neco.1997.9.8.1735Karadzhov, G., Nakov, P., Màrquez, L., Barrón-Cedeño, A., … Koychev, I. (2017). Fully Automated Fact Checking Using External Sources. RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning. doi:10.26615/978-954-452-049-6_046Kumar, S., West, R., & Leskovec, J. (2016). Disinformation on the Web. Proceedings of the 25th International Conference on World Wide Web. doi:10.1145/2872427.2883085Li, X., Meng, W., & Yu, C. (2011). T-verifier: Verifying truthfulness of fact statements. 2011 IEEE 27th International Conference on Data Engineering. doi:10.1109/icde.2011.5767859Nyhan, B., & Reifler, J. (2010). When Corrections Fail: The Persistence of Political Misperceptions. Political Behavior, 32(2), 303-330. doi:10.1007/s11109-010-9112-2Plutchik, R. (2001). The Nature of Emotions. American Scientist, 89(4), 344. doi:10.1511/2001.4.344Popat, K., Mukherjee, S., Strötgen, J., & Weikum, G. (2016). Credibility Assessment of Textual Claims on the Web. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. doi:10.1145/2983323.2983661Poria, S., Gelbukh, A., Hussain, A., Howard, N., Das, D., & Bandyopadhyay, S. (2013). Enhanced SenticNet with Affective Labels for Concept-Based Opinion Mining. IEEE Intelligent Systems, 28(2), 31-38. doi:10.1109/mis.2013.4Rangel, F., & Rosso, P. (2016). On the impact of emotions on author profiling. Information Processing & Management, 52(1), 73-92. doi:10.1016/j.ipm.2015.06.003Rashkin, H., Choi, E., Jang, J. Y., Volkova, S., & Choi, Y. (2017). Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. doi:10.18653/v1/d17-1317Ruchansky, N., Seo, S., & Liu, Y. (2017). CSI. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. doi:10.1145/3132847.3132877Tausczik, Y. R., & Pennebaker, J. W. (2009). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1), 24-54. doi:10.1177/0261927x09351676Volkova, S., Shaffer, K., Jang, J. Y., & Hodas, N. (2017). Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). doi:10.18653/v1/p17-2102Zhao, Z., Resnick, P., & Mei, Q. (2015). Enquiring Minds. Proceedings of the 24th International Conference on World Wide Web. doi:10.1145/2736277.274163

Multimodal Fake News Detection with Textual, Visual and Semantic Information

[EN] Recent years have seen a rapid growth in the number of fake news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to di erentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news.Anastasia Giachanou is supported by the SNSF Early Postdoc Mobility grant under the project Early Fake News Detection on Social Media, Switzerland (P2TIP2 181441). Guobiao Zhang is funded by China Scholarship Council (CSC) from the Ministry of Education of P.R. China. The work of Paolo Rosso is partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31)Giachanou, A.; Zhang, G.; Rosso, P. (2020). Multimodal Fake News Detection with Textual, Visual and Semantic Information. Springer. 30-38. https://doi.org/10.1007/978-3-030-58323-1_3S3038Boididou, C., et al.: Verifying multimedia use at MediaEval 2015. In: MediaEval 2015 Workshop, pp. 235–237 (2015)Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: WWW 2011, pp. 675–684 (2011)Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: CVPR 2017, pp. 1251–1258 (2017)Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. 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FacTweet: Profiling Fake News Twitter Accounts

[EN] We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet independently. We show the experimental benefits of modeling latent stylistic signatures of mixed fake and real news with a sequential model over a wide range of strong baselinesThe work of Paolo Rosso was partially funded by the Spanish MICINN under the research project MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31)Ghanem, BHH.; Ponzetto, SP.; Rosso, P. (2020). FacTweet: Profiling Fake News Twitter Accounts. 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In: Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval), pp. 1125–1131 (2019)Ghanem, B., Glavas, G., Giachanou, A., Ponzetto, S.P., Rosso, P., Pardo, F.M.R.: UPV-UMA at CheckThat! Lab: verifying Arabic claims using a cross lingual approach. In: Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum, Lugano, Switzerland, 9–12 September 2019 (2019)Ghanem, B., Rosso, P., Rangel, F.: An emotional analysis of false information in social media and news articles. ACM Trans. Internet Technol. (TOIT) 20(2), 1–18 (2020)Giachanou, A., Rosso, P., Crestani, F.: Leveraging emotional signals for credibility detection. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 877–880 (2019)Graham, J., Haidt, J., Nosek, B.A.: Liberals and conservatives rely on different sets of moral foundations. J. Pers. Soc. 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An Exploratory Study of COVID-19 Misinformation on Twitter

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1 500 tweets relating to 1 274 false and 276 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating (new tweets) or spreading (retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.Comment: 20 pages, nine figures, four tables. Submitted for peer review, revision

How Misinformation Spreads Through Twitter

While living in the age of information, an inherent drawback to such high exposure to content lends itself to the precarious rise of misinformation. Whether it is called “alternative facts,” “fake news,” or just incorrect information, because of its pervasiveness in nearly every political and policy discussion, the spread of misinformation is seen as one of the greatest challenges to overcome in the 21st century. As new technologies emerge, a major piece of both content creation and the perpetuation of misinformation are social media platforms like Twitter, Facebook, and YouTube. As news events emerge, whether be a pandemic, a mass shooting, or an election campaign, it is difficult to divulge the facts from fiction when so many different “facts” appear. This study looks at 14,545,945 tweets generated in the wake of the 1 October mass shooting and its second anniversary to identify how much of the public response is fogged by information pollution, to identify what kind of misinformation is spread and how it spreads on Twitter and news coverage

Identifying Fake News using Emotion Analysis

This paper presents research applying Emotional Analysis to “Fake News” and “Real News” articles to investigate whether or not there is a difference in the emotion used in these two types of news articles. The paper reports on a dataset for Fake and Real News that we created, and the natural language processing techniques employed to process the collected text. We use a lexicon that includes predefined words for eight emotions (anger, anticipation, disgust, fear, surprise, sadness, joy, trust) to measure the emotional impact in each of these eight dimensions. The results of the emotion analysis are used as features for machine learning algorithms contained in the Weka package to train a classifier. This classifier is then used to analyze a new document to predict/classify it to be “Fake” or “Real” News