Misinformation detection using multitask learning with mutual learning for novelty detection and emotion recognition

Abdul-Mageed, M., & Ungar, L. (2017). Emonet: Fine-grained emotion detection with gated recurrent neural networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 718–728). Akhtar, 2018, No, that never happened!! investigating rumors on Twitter, IEEE Intelligent Systems, 33, 8, 10.1109/MIS.2018.2877279 Allcott, 2017, Social media and fake news in the 2016 election, Journal of Economic Perspectives, 31, 211, 10.1257/jep.31.2.211 Amplayo, 2018, Network-based approach to detect novelty of scholarly literature, Information Sciences, 422, 542, 10.1016/j.ins.2017.09.037 An, 2020, Diversity and novelty in biomedical information retrieval, 369 Attardi, 2020, Transfer learning from transformers to fake news challenge stance detection (FNC-1) task Barr, 2019 Becker, 2017, Multilingual emotion classification using supervised learning: Comparative experiments, Information Processing & Management, 53, 684, 10.1016/j.ipm.2016.12.008 Bidgoly, 2020 Brady, 2017, Emotion shapes the diffusion of moralized content in social networks, Proceedings of the National Academy of Sciences, 114, 7313, 10.1073/pnas.1618923114 Breja, 2015 Chaudhry, 2017, Stance detection for the fake news challenge: identifying textual relationships with deep neural nets Cruz, J. C. B., Tan, J. A., & Cheng, C. (2020). Localization of Fake News Detection via Multitask Transfer Learning. In Proceedings of the 12th Language Resources and Evaluation Conference (pp. 2596–2604). Cuan-Baltazar, 2020, Misinformation of COVID-19 on the internet: infodemiology study, JMIR Public Health and Surveillance, 6, 10.2196/18444 Demszky, 2020 Devlin, 2019, BERT: Pre-training of deep bidirectional transformers for language understanding Ghanem, 2020, An emotional analysis of false information in social media and news articles, ACM Transactions on Internet Technology (TOIT), 20, 1, 10.1145/3381750 Ghosal, 2020, Is your document novel? Let attention guide you. An attention-based model for document-level novelty detection, Natural Language Engineering, 1 Ghosal, T., Edithal, V., Ekbal, A., Bhattacharyya, P., Tsatsaronis, G., & Chivukula, S. S. S. K. (2018). Novelty goes deep. A deep neural solution to document level novelty detection. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 2802–2813). Giachanou, A., Rosso, P., & Crestani, F. (2019). 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). Guo, 2019 Hanselowski, A., Avinesh, P., Schiller, B., Caspelherr, F., Chaudhuri, D., & Meyer, C. M., et al. (2018). A Retrospective Analysis of the Fake News Challenge Stance-Detection Task. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 1859–1874). He, 2020, Semantic matching efficiency of supply and demand texts on online technology trading platforms: Taking the electronic information of three platforms as an example, Information Processing & Management, 57, 10.1016/j.ipm.2020.102258 Hsu, 2019, A theory of misinformation spread on social networks, Available at SSRN 3391585 Imtiaz, 2020, Duplicate questions pair detection using siamese malstm, IEEE Access, 8, 21932, 10.1109/ACCESS.2020.2969041 Islam, 2020, COVID-19–related infodemic and its impact on public health: A global social media analysis, The American Journal of Tropical Medicine and Hygiene, 103, 1621, 10.4269/ajtmh.20-0812 Jain, 2019, Spam detection in social media using convolutional and long short term memory neural network, Annals of Mathematics and Artificial Intelligence, 85, 21, 10.1007/s10472-018-9612-z Jose, 2020, Smart monitoring based on novelty detection and artificial intelligence applied to the condition assessment of rotating machinery in the industry 4.0 Kerner, H. R., Wellington, D. F., Wagstaff, K. L., Bell, J. F., Kwan, C., & Amor, H. B. (2019). Novelty detection for multispectral images with application to planetary exploration. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 33 (pp. 9484–9491). Khuroo, 2020, Chloroquine and hydroxychloroquine in coronavirus disease 2019 (COVID-19). Facts, fiction & the hype. a critical appraisal, International journal of antimicrobial agents, 10.1016/j.ijantimicag.2020.106101 Klinger, R., et al. (2018). An analysis of annotated corpora for emotion classification in text. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 2104–2119). Kochkina, E., Liakata, M., & Zubiaga, A. (2018). All-in-one: Multi-task Learning for Rumour Verification. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 3402–3413). Kogan, 2019 Kouzy, 2020, Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter, Cureus, 12 Kumar, 2020, Semantic similarity and text summarization based novelty detection, SN Applied Sciences, 2, 332, 10.1007/s42452-020-2082-z Lee, S. (2015). Online sentence novelty scoring for topical document streams. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 567–572). Liew, J. S. Y., & Turtle, H. R. (2016). Exploring fine-grained emotion detection in tweets. In Proceedings of the NAACL Student Research Workshop (pp. 73–80). Liu, S., Liu, S., & Ren, L. (2019). Trust or Suspect? An Empirical Ensemble Framework for Fake News Classification. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Melbourne, Australia (pp. 11–15). Liu, 2019, Detection of satiric news on social media: analysis of the phenomenon with a french dataset, 1 MacCartney, 2014, Natural logic and natural language inference, 129 Mutlu, E. Ç., Oghaz, T., Jasser, J., Tütüncüler, E., Rajabi, A., & Tayebi, A., et al. (0000). Astance data set on polarized conversations on twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19. Mutlu, 2020, A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19, Data in Brief, 10.1016/j.dib.2020.106401 Pennycook, 2020, Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention, Psychological Science, 10.1177/0956797620939054 Pennycook, 2019, Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning, Cognition, 188, 39, 10.1016/j.cognition.2018.06.011 Pham, L. (2019). Transferring, transforming, ensembling: the novel formula of identifying fake news. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Melbourne, Australia (pp. 11–15). Qin, 2016 Rath, 2020, Automatic detection of fake news using textual entailment recognition, 1 Ren, 2019, Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews, Information Processing & Management, 56, 1425, 10.1016/j.ipm.2018.04.003 Rohit, 2018, Novelty detection in BBC sports news streams, International Journal of Scientific Research in Computer Science Applications and Management Studies Saikh, T., Ghosal, T., Ekbal, A., & Bhattacharyya, P. (2017). Document level novelty detection: textual entailment lends a helping hand. In Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017) (pp. 131–140). Scheufele, 2019, Science audiences, misinformation, and fake news, Proceedings of the National Academy of Sciences, 116, 7662, 10.1073/pnas.1805871115 Shu, 2019, Defend: Explainable fake news detection, 395 Shu, 2019 Slovikovskaya, V., & Attardi, G. (2020). Transfer Learning from Transformers to Fake News Challenge Stance Detection (FNC-1) Task. In Proceedings of the 12th Language Resources and Evaluation Conference (pp. 1211–1218). Tasnim, 2020 Vosoughi, 2018, The spread of true and false news online, Science, 359, 1146, 10.1126/science.aap9559 Wang, W. Y. (2017). Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 422–426). Wang, 2015, Detecting emotions in social media: A constrained optimization approach Wu, L., Rao, Y., Jin, H., Nazir, A., & Sun, L. (2019). Different Absorption from the Same Sharing: Sifted Multi-task Learning for Fake News Detection. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 4636–4645). Xiaoye, 2019 Xiong, 2020, DGI: Recognition of textual entailment via dynamic gate matching, Knowledge-Based Systems, 10.1016/j.knosys.2020.105544 Yang, 2019 Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., & Hovy, E. (2016). 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 (pp. 1480–1489). Yang, 2019 Zarrabian, 2020, Covid-19 pandemic and the importance of cognitive rehabilitation, Basic and Clinical Neuroscience, 189 Zhou, D., Zhang, X., Zhou, Y., Zhao, Q., & Geng, X. (2016). Emotion distribution learning from texts. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 638–647).