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Recent Advancements in Misinformation Detection

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Dive into Misinformation Detection

Part of the book series: The Information Retrieval Series ((INRE,volume 30))

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Abstract

The second chapter presents a brief literature survey and the recent advancements in topics related to misinformation detection. The literature survey in this chapter majorly concentrates on prior misinformation detection works carried out by exploring news content, news context, linguistic style, and propagation. The first part of the chapter talks about the gradual advancements of the misinformation datasets. It briefly explains the popular unimodal, multimodal, and multilingual datasets. Later, this chapter concentrates on the algorithms and frameworks developed for misinformation detection in recent years from various perspectives.

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Notes

  1. 1.

    https://www.channel4.com/.

  2. 2.

    https://www.politifact.com/.

  3. 3.

    https://www.snopes.com/.

  4. 4.

    https://www.reuters.com/.

  5. 5.

    https://www.kaggle.com/mrisdal/fake-news.

  6. 6.

    https://fiskkit.com/.

References

  • Abdul-Mageed, Muhammad, and Lyle Ungar. 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), 718–728.

    Google Scholar 

  • Ahuja, Nishtha, and Shailender Kumar. 2023. Mul-fad: attention based detection of multilingual fake news. Journal of Ambient Intelligence and Humanized Computing 14: 1–11.

    Article  Google Scholar 

  • Ajao, Oluwaseun, Deepayan Bhowmik, and Shahrzad Zargari. 2019. Sentiment aware fake news detection on online social networks. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2507–2511. IEEE.

    Google Scholar 

  • Akhtar, Md. Shad, Dushyant Singh Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, and Pushpak Bhattacharyya. 2019. Multi-task learning for multi-modal emotion recognition and sentiment analysis. In NAACL-HLT (1).

    Google Scholar 

  • Ameur, Mohamed Seghir Hadj, and Hassina Aliane. 2021. Aracovid19-mfh: Arabic COVID-19 multi-label fake news & hate speech detection dataset. Procedia Computer Science 189: 232–241.

    Google Scholar 

  • Amplayo, Reinald Kim, SuLyn Hong, and Min Song. 2018. Network-based approach to detect novelty of scholarly literature. Information Sciences 422: 542–557.

    Article  Google Scholar 

  • An, Xiangdong, Jimmy Xiangji Huang, and Yuqi Wang. 2020. Diversity and novelty in biomedical information retrieval. In Biomedical Information Technology, 369–396. Amsterdam: Elsevier.

    Chapter  Google Scholar 

  • Antol, Stanislaw, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C. Lawrence Zitnick, and Devi Parikh. 2015. VQA: Visual question answering. In Proceedings of the IEEE International Conference on Computer Vision, 2425–2433.

    Google Scholar 

  • Bappy, Jawadul, Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Arjuna Flenner, Shivkumar Chandrasekaran, Amit Roy-Chowdhury, Jason HBSK Lawrence Peterson, et al. 2017. Detection and localization of image forgeries using resampling features and deep learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 69–77.

    Google Scholar 

  • Becker, Karin, Viviane P. Moreira, and Aline G.L. dos Santos. 2017. Multilingual emotion classification using supervised learning: Comparative experiments. Information Processing & Management 53 (3): 684–704.

    Article  Google Scholar 

  • Bhutani, Bhavika, Neha Rastogi, Priyanshu Sehgal, and Archana Purwar. 2019. Fake news detection using sentiment analysis. In 2019 Twelfth International Conference on Contemporary Computing (IC3), 1–5. IEEE.

    Google Scholar 

  • Breja, Manvi. 2015. A novel approach for novelty detection of web documents. International Journal of Computer Science and Information Technologies 6: 4257–4262.

    Google Scholar 

  • Castelo, Sonia, Thais Almeida, Anas Elghafari, Aécio Santos, Kien Pham, Eduardo Nakamura, and Juliana Freire. 2019. A topic-agnostic approach for identifying fake news pages. In Companion Proceedings of The 2019 World Wide Web Conference, 975–980.

    Google Scholar 

  • Castillo, Carlos, Marcelo Mendoza, and Barbara Poblete. 2011. Information credibility on twitter. In Proceedings of the 20th International Conference on World Wide Web, 675–684. ACM.

    Google Scholar 

  • Chaudhry, Ali K., Darren Baker, and Philipp Thun-Hohenstein. 2017. Stance detection for the fake news challenge: identifying textual relationships with deep neural nets. CS224n: Natural Language Processing with Deep Learning.

    Google Scholar 

  • Chauhan, Dushyant Singh, Md. Shad Akhtar, Asif Ekbal, and Pushpak Bhattacharyya. 2019a. Context-aware interactive attention for multi-modal sentiment and emotion analysis. 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 2019, Hong Kong, China, November 3–7, 2019, 5646–5656.

    Google Scholar 

  • Chauhan, Dushyant Singh, Md. Shad Akhtar, Asif Ekbal, and Pushpak Bhattacharyya. 2019b. Context-aware interactive attention for multi-modal sentiment and emotion analysis. 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), 5647–5657.

    Google Scholar 

  • Chauhan, Hardik, Mauajama Firdaus, Asif Ekbal, and Pushpak Bhattacharyya. 2019c. Ordinal and attribute aware response generation in a multimodal dialogue system. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 5437–5447.

    Google Scholar 

  • Chauhan, Dushyant Singh, S.R. Dhanush, Asif Ekbal, and Pushpak Bhattacharyya. 2020. Sentiment and emotion help sarcasm? A multi-task learning framework for multi-modal sarcasm, sentiment and emotion analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 4351–4360.

    Google Scholar 

  • Chi, Haixiao, and Beishui Liao. 2022. A quantitative argumentation-based automated explainable decision system for fake news detection on social media. Knowledge-Based Systems 242: 108378.

    Article  Google Scholar 

  • Chiang, David, and Min Zhang. 2021. Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing: Tutorial abstracts. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts.

    Google Scholar 

  • Choi, Hyewon, and Youngjoong Ko. 2021. Using topic modeling and adversarial neural networks for fake news video detection. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2950–2954.

    Google Scholar 

  • Choi, Hyewon, and Youngjoong Ko. 2022. Effective fake news video detection using domain knowledge and multimodal data fusion on YouTube. Pattern Recognition Letters 154: 44–52.

    Article  Google Scholar 

  • Cruz, Jan Christian Blaise, Julianne Agatha Tan, and Charibeth Cheng. 2020. Localization of fake news detection via multitask transfer learning. In ed. Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asunción Moreno, Jan Odijk, and Stelios Piperidis, Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11–16, 2020, 2596–2604. European Language Resources Association. https://aclanthology.org/2020.lrec-1.316/

  • Cuan-Baltazar, Jose Yunam, Maria José Muñoz-Perez, Carolina Robledo-Vega, Maria Fernanda Pérez-Zepeda, and Elena Soto-Vega. 2020. Misinformation of COVID-19 on the internet: infodemiology study. JMIR Public Health and Surveillance 6 (2): e18444.

    Article  Google Scholar 

  • Dhawan, Mudit, Shakshi Sharma, Aditya Kadam, Rajesh Sharma, and Ponnurangam Kumaraguru. 2022. Game-on: Graph attention network based multimodal fusion for fake news detection. arXiv preprint arXiv:2202.12478.

    Google Scholar 

  • Etzioni, Oren, Michele Banko, Stephen Soderland, and Daniel S. Weld. 2008. Open information extraction from the web. Communications of the ACM 51 (12): 68–74.

    Article  Google Scholar 

  • Faustini, Pedro Henrique Arruda, and Thiago Ferreira Covoes. 2020. Fake news detection in multiple platforms and languages. Expert Systems with Applications 158: 113503.

    Google Scholar 

  • Firdaus, Mauajama, Hardik Chauhan, Asif Ekbal, and Pushpak Bhattacharyya. 2020. MEISD: A multimodal multi-label emotion, intensity and sentiment dialogue dataset for emotion recognition and sentiment analysis in conversations. In Proceedings of the 28th International Conference on Computational Linguistics, 4441–4453.

    Google Scholar 

  • Fukui, Akira, Dong Huk Park, Daylen Yang, Anna Rohrbach, Trevor Darrell, and Marcus Rohrbach. 2016. Multimodal compact bilinear pooling for visual question answering and visual grounding. In Conference on Empirical Methods in Natural Language Processing, 457–468. ACL.

    Google Scholar 

  • Ghadiri, Zahra, Milad Ranjbar, Fakhteh Ghanbarnejad, and Sadegh Raeisi. 2022. Automated fake news detection using cross-checking with reliable sources. CoRR abs/2201.00083. https://arxiv.org/abs/2201.00083

  • Ghanem, Bilal, Paolo Rosso, and Francisco Rangel. 2020. An emotional analysis of false information in social media and news articles. ACM Transactions on Internet Technology (TOIT) 20 (2): 1–18.

    Article  Google Scholar 

  • Ghanem, Bilal, Simone Paolo Ponzetto, Paolo Rosso, and Francisco Rangel. 2021. FakeFlow: Fake news detection by modeling the flow of affective information. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 679–689.

    Google Scholar 

  • Ghosal, Deepanway, Md. Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, and Pushpak Bhattacharyya. 2018a. Contextual inter-modal attention for multi-modal sentiment analysis. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 3454–3466.

    Google Scholar 

  • Ghosal, Deepanway, Md. Shad Akhtar, Dushyant Singh Chauhan, Soujanya Poria, Asif Ekbal, and Pushpak Bhattacharyya. 2018b. Contextual inter-modal attention for multi-modal sentiment analysis. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31–November 4, 2018, 3454–3466.

    Google Scholar 

  • Ghosal, Tirthankar, Vignesh Edithal, Asif Ekbal, Pushpak Bhattacharyya, Srinivasa Chivukula, and George Tsatsaronis. 2020. Is your document novel? Let attention guide you. an attention-based model for document-level novelty detection. Natural Language Engineering 1–28. https://doi.org/10.1017/S1351324920000194

  • Giachanou, Anastasia, Paolo Rosso, and Fabio Crestani. 2019. Leveraging emotional signals for credibility detection. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 877–880.

    Google Scholar 

  • Giachanou, Anastasia, Paolo Rosso, and Fabio Crestani. 2021. The impact of emotional signals on credibility assessment. Journal of the Association for Information Science and Technology 72: 1117–1132.

    Article  Google Scholar 

  • Gilda, Shlok. 2017. Evaluating machine learning algorithms for fake news detection. In 2017 IEEE 15th Student Conference on Research and Development (SCOReD), 110–115. IEEE.

    Google Scholar 

  • Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems, 2672–2680.

    Google Scholar 

  • Gravanis, Georgios, Athena Vakali, Konstantinos Diamantaras, and Panagiotis Karadais. 2019. Behind the cues: A benchmarking study for fake news detection. Expert Systems with Applications 128: 201–213.

    Article  Google Scholar 

  • Guha, Sreya. 2017. Related fact checks: A tool for combating fake news. arXiv preprint arXiv:1711.00715.

    Google Scholar 

  • Guo, Chuan, Juan Cao, Xueyao Zhang, Kai Shu, and Miao Yu. 2019. Exploiting emotions for fake news detection on social media. CoRR abs/1903.01728. http://arxiv.org/abs/1903.01728

  • Hanselowski, Andreas, P.V.S. Avinesh, Benjamin Schiller, Felix Caspelherr, Debanjan Chaudhuri, Christian M. Meyer, and Iryna Gurevych. 2018. A retrospective analysis of the fake news challenge stance-detection task. In Proceedings of the 27th International Conference on Computational Linguistics, 1859–1874.

    Google Scholar 

  • Hossain, Md. Zobaer, Md. Ashraful Rahman, Md. Saiful Islam, and Sudipta Kar. 2020. BanFakeNews: A dataset for detecting fake news in bangla. In ed. Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asunción Moreno, Jan Odijk, and Stelios Piperidis, Proceedings of The 12th Language Resources and Evaluation Conference, LREC 2020, Marseille, France, May 11–16, 2020, 2862–2871. European Language Resources Association. https://aclanthology.org/2020.lrec-1.349/

  • Huang, Yin-Fu, and Po-Hong Chen. 2020. Fake news detection using an ensemble learning model based on self-adaptive harmony search algorithms. Expert Systems with Applications 159: 113584.

    Article  Google Scholar 

  • Huang, Shujun, Nianguang Cai, Pedro Penzuti Pacheco, Shavira Narrandes, Yang Wang, and Wayne Xu. 2018. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics-Proteomics 15 (1): 41–51.

    Google Scholar 

  • Jin, Zhiwei, Juan Cao, Yu-Gang Jiang, and Yongdong Zhang. 2014. News credibility evaluation on microblog with a hierarchical propagation model. In 2014 IEEE International Conference on Data Mining, 230–239. IEEE.

    Google Scholar 

  • Jin, Zhiwei, Juan Cao, Yongdong Zhang, and Jiebo Luo. 2016a. News verification by exploiting conflicting social viewpoints in microblogs. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30.

    Google Scholar 

  • Jin, Zhiwei, Juan Cao, Yongdong Zhang, Jianshe Zhou, and Qi Tian. 2016b. Novel visual and statistical image features for microblogs news verification. IEEE Transactions on Multimedia 19 (3): 598–608.

    Article  Google Scholar 

  • Jin, Zhiwei, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo. 2017. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In Proceedings of the 25th ACM International Conference on Multimedia, 795–816.

    Chapter  Google Scholar 

  • Jose, Saucedo-Dorantes Juan, Jaen-Cuellar Arturo Yosimar, and Elvira-Ortiz David Alejandro. 2020. Smart monitoring based on novelty detection and artificial intelligence applied to the condition assessment of rotating machinery in the industry 4.0. In New Trends in the Use of Artificial Intelligence for the Industry 4.0. IntechOpen.

    Google Scholar 

  • Kaelbling, Leslie Pack, Michael L. Littman, and Andrew W. Moore. 1996. Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4: 237–285.

    Article  Google Scholar 

  • Kajava, Kaisla, Emily Öhman, Piao Hui, Jörg Tiedemann, et al. 2020. Emotion preservation in translation: Evaluating datasets for annotation projection. In Proceedings of Digital Humanities in Nordic Countries (DHN 2020).

    Google Scholar 

  • Karimi, Hamid, Proteek Roy, Sari Saba-Sadiya, and Jiliang Tang. 2018. Multi-source multi-class fake news detection. In Proceedings of the 27th International Conference on Computational Linguistics, 1546–1557.

    Google Scholar 

  • Karpathy, Andrej, and Li Fei-Fei. 2015. Deep visual-semantic alignments for generating image descriptions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3128–3137.

    Google Scholar 

  • Kerner, Hannah R., Danika F. Wellington, Kiri L. Wagstaff, James F. Bell, Chiman Kwan, and Heni Ben Amor. 2019. Novelty detection for multispectral images with application to planetary exploration. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 9484–9491.

    Google Scholar 

  • Khattar, Dhruv, Jaipal Singh Goud, Manish Gupta, and Vasudeva Varma. 2019. MVAE: Multimodal variational autoencoder for fake news detection. In The World Wide Web Conference, 2915–2921. ACM.

    Google Scholar 

  • Kim, Dong-Hyun, and Hae-Yeoun Lee. 2017. Image manipulation detection using convolutional neural network. International Journal of Applied Engineering Research 12 (21): 11640–11646.

    Google Scholar 

  • Kim, Jin-Hwa, Kyoung Woon On, Woosang Lim, Jeonghee Kim, JungWoo Ha, and Byoung-Tak Zhang. 2016. Hadamard product for low-rank bilinear pooling. CoRR abs/1610.04325. http://arxiv.org/abs/1610.04325

  • Kouzy, Ramez, Joseph Abi Jaoude, Afif Kraitem, Molly B. El Alam, Basil Karam, Elio Adib, Jabra Zarka, Cindy Traboulsi, Elie W. Akl, and Khalil Baddour. 2020. Coronavirus goes viral: Quantifying the COVID-19 misinformation epidemic on twitter. Cureus 12 (3): e7255.

    Google Scholar 

  • Kula, Sebastian, Michał Choraś, Rafał Kozik, Paweł Ksieniewicz, and Michał Woźniak. 2020. Sentiment analysis for fake news detection by means of neural networks. In International Conference on Computational Science, 653–666. Springer.

    Google Scholar 

  • Kumar, Sushil, and Komal Kumar Bhatia. 2020. Semantic similarity and text summarization based novelty detection. SN Applied Sciences 2 (3): 332.

    Article  Google Scholar 

  • Kwon, Sejeong, Meeyoung Cha, Kyomin Jung, Wei Chen, and Yajun Wang. 2013. Prominent features of rumor propagation in online social media. In 2013 IEEE 13th International Conference on Data Mining, 1103–1108. IEEE.

    Google Scholar 

  • Lee, Sungjin. 2015. Online sentence novelty scoring for topical document streams. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 567–572.

    Google Scholar 

  • Li, Yichuan, Bohan Jiang, Kai Shu, and Huan Liu. 2020b. MM-COVID: A multilingual and multimodal data repository for combating COVID-19 disinformation. arXiv preprint arXiv:2011.04088.

    Google Scholar 

  • Liew, Jasy Suet Yan, and Howard R. Turtle. 2016. Exploring fine-grained emotion detection in tweets. In Proceedings of the NAACL Student Research Workshop, 73–80.

    Google Scholar 

  • Liu, Shuaipeng, Shuo Liu, and Lei Ren. 2019a 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, 11–15.

    Google Scholar 

  • Liu, Yahui, Xiaolong Jin, and Huawei Shen. 2019b. Towards early identification of online rumors based on long short-term memory networks. Information Processing & Management 56 (4): 1457–1467.

    Article  Google Scholar 

  • Lu, Yi-Ju, and Cheng-Te Li. 2020. GCAN: Graph-aware co-attention networks for explainable fake news detection on social media. In ed. Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel R. Tetreault, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5–10, 2020, 505–514. Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.48

  • Ma, Jing, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting rumors from microblogs with recurrent neural networks. In IJCAI, 3818–3824.

    Google Scholar 

  • Ma, Jing, Wei Gao, and Kam-Fai Wong. 2018. Rumor detection on twitter with tree-structured recursive neural networks. In ACL (1).

    Google Scholar 

  • MacCartney, Bill. 2009. Natural language inference. Stanford: Stanford University.

    Google Scholar 

  • Maclin, Richard, and David Opitz. 1997. An empirical evaluation of bagging and boosting. AAAI/IAAI 1997: 546–551.

    Google Scholar 

  • Magdy, Amr, and Nayer Wanas. 2010. Web-based statistical fact checking of textual documents. In Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, 103–110.

    Google Scholar 

  • Mai, Sijie, Haifeng Hu, and Songlong Xing. 2019. Divide, conquer and combine: Hierarchical feature fusion network with local and global perspectives for multimodal affective computing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 481–492.

    Google Scholar 

  • Maigrot, Cédric, Vincent Claveau, Ewa Kijak, and Ronan Sicre. 2016. Mediaeval 2016: A multimodal system for the verifying multimedia use task. In MediaEval 2016: “Verifying Multimedia Use” task.

    Google Scholar 

  • Mitra, Tanushree, and Eric Gilbert. 2015. CREDBANK: A large-scale social media corpus with associated credibility annotations. In Proceedings of the International AAAI Conference on Web and Social Media, vol. 9, 258–267.

    Google Scholar 

  • Mosallanezhad, Ahmadreza, Mansooreh Karami, Kai Shu, Michelle V. Mancenido, and Huan Liu. 2022. Domain adaptive fake news detection via reinforcement learning. CoRR abs/2202.08159. https://arxiv.org/abs/2202.08159

  • Mutlu, Ece C., Toktam Oghaz, Jasser Jasser, Ege Tutunculer, Amirarsalan Rajabi, Aida Tayebi, Ozlem Ozmen, and Ivan Garibay. 2020. A stance data set on polarized conversations on twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19. Data in Brief 33: 106401.

    Article  Google Scholar 

  • Nakamura, Kai, Sharon Levy, and William Yang Wang. 2020. Fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 6149–6157.

    Google Scholar 

  • Nielsen, Dan S. and Ryan McConville. 2022. Mumin: A large-scale multilingual multimodal fact-checked misinformation social network dataset. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 3141–3153.

    Google Scholar 

  • Nørregaard, Jeppe, Benjamin D Horne, and Sibel Adalı. 2019. NELA-GT-2018: A large multi-labelled news dataset for the study of misinformation in news articles. In Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, 630–638.

    Google Scholar 

  • Palani, Balasubramanian, Sivasankar Elango, Vignesh Viswanathan K, et al. 2021. CB-Fake: A multimodal deep learning framework for automatic fake news detection using capsule neural network and bert. Multimedia Tools and Applications 81: 1–34.

    Google Scholar 

  • Patwa, Parth, Shivam Sharma, Srinivas Pykl, Vineeth Guptha, Gitanjali Kumari, Md. Shad Akhtar, Asif Ekbal, Amitava Das, and Tanmoy Chakraborty. 2021. Fighting an infodemic: COVID-19 fake news dataset. In Combating Online Hostile Posts in Regional Languages during Emergency Situation: First International Workshop, CONSTRAINT 2021, Collocated with AAAI 2021, Virtual Event, February 8, 2021, Revised Selected Papers 1, 21–29. Springer.

    Google Scholar 

  • Pennycook, Gordon, Jonathon McPhetres, Yunhao Zhang, Jackson G. Lu, and David G. Rand. 2020. Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological Science 31: 0956797620939054.

    Article  Google Scholar 

  • Pérez-Rosas, Verónica, Bennett Kleinberg, Alexandra Lefevre, and Rada Mihalcea. 2018. Automatic detection of fake news. In Proceedings of the 27th International Conference on Computational Linguistics, 3391–3401.

    Google Scholar 

  • Pham, Lam. 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, 11–15.

    Google Scholar 

  • Potthast, Martin, Johannes Kiesel, Kevin Reinartz, Janek Bevendorff, and Benno Stein. 2018. A stylometric inquiry into hyperpartisan and fake news. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 231–240.

    Google Scholar 

  • Preston, Stephanie, Anthony Anderson, David J. Robertson, Mark P. Shephard, and Narisong Huhe. 2021. Detecting fake news on Facebook: The role of emotional intelligence. Plos one 16 (3): e0246757.

    Article  Google Scholar 

  • Qi, Peng, Juan Cao, Tianyun Yang, Junbo Guo, and Jintao Li. 2019. Exploiting multi-domain visual information for fake news detection. In 2019 IEEE International Conference on Data Mining (ICDM), 518–527. IEEE.

    Google Scholar 

  • Qin, Yumeng, Dominik Wurzer, Victor Lavrenko, and Cunchen Tang. 2016. Spotting rumors via novelty detection. CoRR abs/1611.06322. http://arxiv.org/abs/1611.06322

  • Rashkin, Hannah, Eunsol Choi, Jin Yea Jang, Svitlana Volkova, and Yejin Choi. 2017. 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, 2931–2937.

    Google Scholar 

  • Reiter, Ehud, and Robert Dale. 1997. Building applied natural language generation systems. Natural Language Engineering 3 (1): 57–87.

    Article  Google Scholar 

  • Ren, Gang, and Taeho Hong. 2019. Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews. Information Processing & Management 56 (4): 1425–1438.

    Article  Google Scholar 

  • Rish, Irina et al. 2001. An empirical study of the naive bayes classifier. In IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, 41–46.

    Google Scholar 

  • Rohit, Wankhede, Sachin Deshmukh, and Rajkumar Jagdale. 2018. Novelty detection in BBC sports news streams. International Journal of Scientific Research in Computer Science Applications and Management Studies 7 (2).

    Google Scholar 

  • Rubin, Victoria L., Yimin Chen, and Nadia K. Conroy. 2015. Deception detection for news: three types of fakes. Proceedings of the Association for Information Science and Technology 52 (1): 1–4.

    Article  Google Scholar 

  • Ruchansky, Natali, Sungyong Seo, and Yan Liu. 2017. CSI: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 797–806.

    Google Scholar 

  • Sachan, Tanmay, Nikhil Pinnaparaju, Manish Gupta, and Vasudeva Varma. 2021. Scate: Shared cross attention transformer encoders for multimodal fake news detection. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 399–406.

    Google Scholar 

  • Sadeghi, Fariba, Amir Jalaly Bidgoly, and Hossein Amirkhani. 2022. Fake news detection on social media using a natural language inference approach. Multimedia Tools and Applications 81 (23): 33801–33821.

    Article  Google Scholar 

  • Saikh, Tanik, Tirthankar Ghosal, Asif Ekbal, and Pushpak Bhattacharyya. 2017. Document level novelty detection: Textual entailment lends a helping hand. In Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017), 131–140.

    Google Scholar 

  • Saikh, Tanik, Arkadipta De, Asif Ekbal, and Pushpak Bhattacharyya. 2020. A deep learning approach for automatic detection of fake news. CoRR abs/2005.04938. https://arxiv.org/abs/2005.04938

  • Salem, Fatima K. Abu, Roaa Al Feel, Shady Elbassuoni, Mohamad Jaber, and May Farah. 2019. FA-KES: A fake news dataset around the Syrian war. In Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, 573–582.

    Google Scholar 

  • Santia, Giovanni, and Jake Williams. 2018. BuzzFace: A news veracity dataset with Facebook user commentary and egos. In Proceedings of the International AAAI Conference on Web and Social Media, vol. 12, 531–540.

    Google Scholar 

  • Shahi, Gautam Kishore, and Durgesh Nandini. 2020. FakeCovid–a multilingual cross-domain fact check news dataset for COVID-19. arXiv preprint arXiv:2006.11343.

    Google Scholar 

  • Sharma, Dilip Kumar, and Sunidhi Sharma. 2021. Comment filtering based explainable fake news detection. In Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, 447–458. Springer.

    Google Scholar 

  • Shrestha, Maniz. 2018. Detecting fake news with sentiment analysis and network metadata. Earlham College, Richmond.

    Google Scholar 

  • Shu, Kai, Limeng Cui, Suhang Wang, Dongwon Lee, and Huan Liu. 2019a defend: Explainable fake news detection. In ed. Ankur Teredesai, Vipin Kumar, Ying Li, Rómer Rosales, Evimaria Terzi, and George Karypis, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4–8, 2019, 395–405. ACM. https://doi.org/10.1145/3292500.3330935

  • Shu, Kai, Suhang Wang, and Huan Liu. 2019b. Beyond news contents: The role of social context for fake news detection. In 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 312–320. Association for Computing Machinery.

    Google Scholar 

  • Shu, Kai, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, and Huan Liu. 2020. FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media. Big Data 8 (3): 171–188.

    Article  Google Scholar 

  • Singhal, Shivangi, Rajiv Ratn Shah, Tanmoy Chakraborty, Ponnurangam Kumaraguru, and Shin’ichi Satoh. 2019. SpotFake: A multi-modal framework for fake news detection. In 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), 39–47. IEEE.

    Google Scholar 

  • Sivasangari, V., Ashok Kumar Mohan, M. Sethumadhavan, and K. Suthendran. 2018. Isolating rumors using sentiment analysis. Journal of Cyber Security and Mobility 7: 181–200.

    Google Scholar 

  • Slovikovskaya, Valeriya, and Giuseppe Attardi. 2020. Transfer learning from transformers to fake news challenge stance detection (FNC-1) task. In Proceedings of the Twelfth Language Resources and Evaluation Conference, 1211–1218.

    Google Scholar 

  • Song, Yan-Yan, and LU Ying. 2015. Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry 27 (2): 130.

    Google Scholar 

  • Song, Chenguang, Nianwen Ning, Yunlei Zhang, and Bin Wu. 2021a. Knowledge augmented transformer for adversarial multidomain multiclassification multimodal fake news detection. Neurocomputing 462 :88–100.

    Article  Google Scholar 

  • Song, Chenguang, Nianwen Ning, Yunlei Zhang, and Bin Wu. 2021b. A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. Information Processing & Management 58 (1): 102437.

    Article  Google Scholar 

  • Suryavardan, S., Shreyash Mishra, Parth Patwa, Megha Chakraborty, Anku Rani, Aishwarya Reganti, Aman Chadha, Amitava Das, Amit Sheth, Manoj Chinnakotla, et al. 2022. Factify 2: A multimodal fake news and satire news dataset. Proceedings ISSN 1613: 0073. http://ceur-ws.org

    Google Scholar 

  • Tacchini, Eugenio, Gabriele Ballarin, Marco L. Della Vedova, Stefano Moret, and Luca de Alfaro. 2017. Some like it hoax: Automated fake news detection in social networks. CoRR abs/1704.07506. http://arxiv.org/abs/1704.07506

  • Thorne, James, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018. Fever: A large-scale dataset for fact extraction and verification. arXiv preprint arXiv:1803.05355.

    Google Scholar 

  • Vlachos, Andreas, and Sebastian Riedel. 2014. Fact checking: Task definition and dataset construction. In Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, 18–22.

    Google Scholar 

  • Wang, William Yang. 2017. “liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648.

    Google Scholar 

  • Wang, Yichen, and Aditya Pal. 2015. Detecting emotions in social media: A constrained optimization approach. In Twenty-Fourth International Joint Conference on Artificial Intelligence.

    Google Scholar 

  • Wang, Yaqing, Fenglong Ma, Zhiwei Jin, Ye Yuan, Guangxu Xun, Kishlay Jha, Lu Su, and Jing Gao. 2018. Eann: Event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 849–857. ACM.

    Google Scholar 

  • Wang, Jingzi, Hongyan Mao, and Hongwei Li. 2022. FMFN: Fine-grained multimodal fusion networks for fake news detection. Applied Sciences 12 (3): 1093.

    Article  Google Scholar 

  • Wu, Lianwei, Yuan Rao, Ambreen Nazir, and Haolin Jin. 2020. Discovering differential features: Adversarial learning for information credibility evaluation. Information Sciences 516: 453–473.

    Article  Google Scholar 

  • Wu, Kun, Xu Yuan, and Yue Ning. 2021. Incorporating relational knowledge in explainable fake news detection. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 403–415. Springer.

    Google Scholar 

  • Xiaoye, SHI. 2019. Analysis of ByteDance. PhD thesis, Ph. D. Dissertation. Swiss Federal Institute of Technology Zurich.

    Google Scholar 

  • Xiong, Xi, Yuanyuan Li, Rui Zhang, Zhan Bu, Guiqing Li, and Shenggen Ju. 2020. DGI: Recognition of textual entailment via dynamic gate matching. Knowledge-Based Systems 194: 105544.

    Article  Google Scholar 

  • Yang, Yang, Lei Zheng, Jiawei Zhang, Qingcai Cui, Zhoujun Li, and Philip S. Yu. 2018c. Ti-CNN: Convolutional neural networks for fake news detection. arXiv preprint arXiv:1806.00749.

    Google Scholar 

  • Yang, K.-C., T. Niven, and Hung-Yu Kao. 2019a Fake news detection as natural language inference. In 12th ACM International Conference on Web Search and Data Mining (WSDM-2019)(in Fake News Classification Challenge, WSDM Cup 2019).

    Google Scholar 

  • Yu, Feng, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan, et al. 2017a. A convolutional approach for misinformation identification. In IJCAI, 3901–3907.

    Google Scholar 

  • Yu, Zhou, Jun Yu, Jianping Fan, and Dacheng Tao. 2017b. Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In Proceedings of the IEEE International Conference on Computer Vision, 1821–1830.

    Google Scholar 

  • Zarrabian, Shahram, and Peyman Hassani-Abharian. 2020. COVID-19 pandemic and the importance of cognitive rehabilitation. Basic and Clinical Neuroscience 11: 189–190.

    Google Scholar 

  • Zhang, Zhongheng. 2016. Introduction to machine learning: k-nearest neighbors. Annals of Translational Medicine 4 (11): 218.

    Article  Google Scholar 

  • Zhang, Jiawei, Limeng Cui, Yanjie Fu, and Fisher B. Gouza. 2018. Fake news detection with deep diffusive network model. arXiv preprint arXiv:1805.08751.

    Google Scholar 

  • Zhang, Jiawei, Bowen Dong, and S. Yu Philip. 2020. FAKEDETECTOR: Effective fake news detection with deep diffusive neural network. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), 1826–1829. IEEE.

    Google Scholar 

  • Zhang, Xueyao, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu. 2021b Mining dual emotion for fake news detection. In Proceedings of the Web Conference 2021, 3465–3476.

    Google Scholar 

  • Zhou, Deyu, Xuan Zhang, Yin Zhou, Quan Zhao, and Xin Geng. 2016. Emotion distribution learning from texts. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 638–647.

    Google Scholar 

  • Zlatkova, Dimitrina, Preslav Nakov, and Ivan Koychev. 2019. Fact-checking meets fauxtography: Verifying claims about images. 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), 2099–2108.

    Google Scholar 

  • Zubiaga, Arkaitz, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, and Peter Tolmie. 2016. Analysing how people orient to and spread rumours in social media by looking at conversational threads. PloS one 11 (3): e0150989.

    Article  Google Scholar 

  • Zubiaga, Arkaitz, Elena Kochkina, Maria Liakata, Rob Procter, Michal Lukasik, Kalina Bontcheva, Trevor Cohn, and Isabelle Augenstein. 2018. Discourse-aware rumour stance classification in social media using sequential classifiers. Information Processing & Management 54 (2): 273–290.

    Article  Google Scholar 

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  1. Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  2. KIIT University, Bhubaneswar, India

    Rina Kumari

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Ekbal, A., Kumari, R. (2024). Recent Advancements in Misinformation Detection. In: Dive into Misinformation Detection. The Information Retrieval Series, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-031-54834-5_2

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