Explainable Deep Learning for False Information Identification: An Argumentation Theory Approach
Published Online:3 Aug 2023https://doi.org/10.1287/isre.2020.0097
References
- (2008) CyberGate: A design framework and system for text analysis of computer-mediated communication. MIS Quart. 32(4):811–837.Crossref, Google Scholar
- (2018) Text analytics to support sense-making in social media: A language-action perspective. MIS Quart. 42(2):427–464.Crossref, Google Scholar
- (2023) Expl(AI)ned: The impact of explainable artificial intelligence on users’ information processing. Inform. Systems Res., ePub ahead of print March 3, https://doi.org/10.1287/isre.2023.1199.Link, Google Scholar
- (2006) The public sphere and online, independent journalism. Canadian J. Ed. 29(1):109–130.Crossref, Google Scholar
- (2007) Argumentation in artificial intelligence. Artificial Intelligence 171(10–15):619–641.Crossref, Google Scholar
- (1956) Structural balance: A generalization of Heider’s theory. Psych. Rev. 63(5):277–293.Crossref, Google Scholar
- (2019) Task-dependent algorithm aversion. J. Marketing Res. 56(5):809–825.Crossref, Google Scholar
- (2011) Information credibility on Twitter. Proc. 20th Internat. Conf. World Wide Web (ACM, New York), 675–684.Google Scholar
- (2023) A theory-driven deep learning method for voice chat–based customer response prediction. Inform. Systems Res., ePub ahead of print January 12, https://doi.org/10.1287/isre.2022.1196.Link, Google Scholar
- (2020) Explainable rumor detection using inter and intra-feature attention networks. KDD TrueFact Workshop 2020.Google Scholar
- (2018) Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. Trends Appl. Knowledge Discovery Data Mining PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018, Revised Selected Papers 22 (Springer International Publishing, New York), 40–52.Google Scholar
- (2016) Deep reinforcement learning for mention-ranking coreference models. Proc. 2016 Conf. Empirical Methods Natural Language Processing (ACM, New York), 2256–2262.Google Scholar
- (2020) Electra: Pre-training text encoders as discriminators rather than generators. Preprint, submitted March 23, https://arxiv.org/pdf/2003.10555.pdf%3C/p%3E.Google Scholar
- (2021) Fake news, investor attention, and market reaction. Inform. Systems Res. 32(1):35–52.Link, Google Scholar
- CNBC (2021) Why content moderation costs social media companies billions. Accessed February 28, 2021, https://www.youtube.com/watch?v=OBZoVpmbwPk.Google Scholar
- (1999) Form and content: Dissociating syntax and semantics in sentence comprehension. Neuron 24(2):427–432.Crossref, Google Scholar
- (2019) BERT: Pre-training of deep bidirectional transformers for language understanding. Proc. 2019 Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Language Tech., vol. 1 (Association for Computational Linguistics, Stroudsburg, PA) 4171–4186.Google Scholar
- (2021) Attention in natural language processing. IEEE Trans. Neural Networks Learn. Systems 32(10):4291–4308.Crossref, Google Scholar
- (2015) Aging and financial decision making. Management Sci. 61(11):2603–2610.Link, Google Scholar
- (2004) The formulation of design theories for information systems. Linger H, Fisher J, Wojtkowski W, Wojtkowski WG, Zupančič J, Vigo K, Arnold J, eds. Constructing the Infrastructure for the Knowledge Economy (Springer, Boston), 83–93.Crossref, Google Scholar
- (2021) Informal logic. Zalta EN, Nodelman U, eds. The Stanford Encyclopedia of Philosophy (Stanford University, Stanford, CA), https://plato.stanford.edu/archives/win2022/entries/logic-informal/.Google Scholar
- (2020) A deep look into neural ranking models for information retrieval. Inform. Processing Management 57(6):102067.Crossref, Google Scholar
- (1954) Distributional structure. Word 10(2–3):146–162.Crossref, Google Scholar
- (2017) Neuroscience-inspired artificial intelligence. Neuron 95(2):245–258.Crossref, Google Scholar
- (1946) Attitudes and cognitive organization. J. Psych. 21(1):107–112.Crossref, Google Scholar
- (2020) Soliciting human-in-the-loop user feedback for interactive machine learning reduces user trust and impressions of model accuracy. Proc. AAAI Conf. Human Comput. Crowdsourcing (AAAI, Washington, DC), 8:63–72.Google Scholar
- (2021) Disentangling semantics and syntax in sentence embeddings with pre-trained language models. Proc. 2021 Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Language Tech. (Association for Computational Linguistics, Stroudsburg, PA), 1372–1379.Google Scholar
- (2019) Attention is not explanation. Proc. 2019 Conf. North Amer. Chapter Assoc. Comput. Linguistics Human Language Tech., vol. 1 (Association for Computational Linguistics, Stroudsburg, PA), 3543–3556.Google Scholar
- (2017) Fully automated fact checking using external sources. Proc. Internat. Conf. Recent Adv. Natural Language Processing (INCOMA Ltd., Shumen, Bulgaria), 344–353.Google Scholar
- (2008) Toulmin’s rhetorical logic: What’s the warrant for warrants? Philos. Rhetoric 41(1):22–50.Crossref, Google Scholar
- (2020) Interpretable rumor detection in microblogs by attending to user interactions. Proc. Conf. AAAI Artificial Intelligence 34(5):8783–8790.Crossref, Google Scholar
- (2019) Combating fake news on social media with source ratings: The effects of user and expert reputation ratings. J. Management Inform. Systems 36(3):931–968.Crossref, Google Scholar
- (2006) The effects of trust-assuring arguments on consumer trust in Internet stores: Application of Toulmin’s model of argumentation. Inform. Systems Res. 17(3):286–300.Link, Google Scholar
- (2016) How much information? Effects of transparency on trust in an algorithmic interface. Proc. 2016 CHI Conf. Human Factors Comput. Systems (ACM, New York), 2390–2395.Google Scholar
- (2016) Argumentation mining: State of the art and emerging trends. ACM Trans. Internet Tech. 16(2):1–25.Crossref, Google Scholar
- (2019) Roberta: A robustly optimized Bert pretraining approach. Preprint, submitted July 26, https://arxiv.org/pdf/1907.11692.pdf%5C.Google Scholar
- (2017) A unified approach to interpreting model predictions. Proc. Adv. Neural Inform. Processing Systems 30 (Curran Associates, Inc., Red Hook, NY), 1–10.Google Scholar
- (1985) Implications of theories of language for information systems. MIS Quart. 9(1):61–74.Crossref, Google Scholar
- (2019) April fools: Traders chase another unexplainable bitcoin rally. Accessed April 7, 2019, https://bloom.bg/38JaCeD.Google Scholar
- (2011) Trust in a specific technology: An investigation of its components and measures. ACM Trans. Management Inform. Systems 2(2):1–25.Crossref, Google Scholar
- (2019) Fake news on social media: People believe what they want to believe when it makes no sense at all. MIS Quart. 43(4):1343–1360.Crossref, Google Scholar
- (2007) Argument based machine learning. Artificial Intelligence 171(10–15):922–937.Crossref, Google Scholar
- (2018) An interpretable joint graphical model for fact-checking from crowds. 32nd AAAI Conf. Artificial Intelligence.Google Scholar
- (2020) Investigating the importance of first impressions and explainable ai with interactive video analysis. Extended Abstracts 2020 CHI Conf. Human Factors Comput. Systems, 1–8.Google Scholar
- (2014) Glove: Global vectors for word representation. Proc. 2014 Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 1532–1543.Google Scholar
- (1986) Induction of decision trees. Machine Learn. 1(1):81–106.Crossref, Google Scholar
- (2017) A simple but tough-to-beat baseline for the Fake News Challenge stance detection task. Preprint, submitted May 21, https://arxiv.org/pdf/1707.03264.pdf.Google Scholar
- (2019) Is attention interpretable? Proc. 57th Annual Meeting Assoc. Comput. Linguistics (Association for Computational Linguistics, Stroudsburg, PA), 2931–2951.Google Scholar
- (2019) dEFEND: Explainable fake news detection. Proc. 25th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 395–405.Google Scholar
- (2017) Fake news detection on social media: A data mining perspective. SIGKDD Explorations 19(1):22–36.Crossref, Google Scholar
- Spacy (2021) English: Available trained pipelines for English. Accessed, https://spacy.io/models/en.Google Scholar
- (2021) Theories of meaning. Zalta EN, ed. The Stanford Encyclopedia of Philosophy (Stanford University, Stanford, CA), https://plato.stanford.edu/archives/spr2021/entries/meaning/.Google Scholar
- (2017) Some like it hoax: Automated fake news detection in social networks. Proc. 2nd Workshop Data Sci. Soc. Goods (Skopje, Macedonia).Google Scholar
- (2010) Online news exposure and trust in the mainstream media: Exploring possible associations. Amer. Behav. Sci. 54(1):22–42.Crossref, Google Scholar
- (2020) Explainable recommendation for repeat consumption. Fourteenth ACM Conf. Recommender Systems (ACM, New York), 462–467.Google Scholar
- (2019) Attention interpretability across NLP tasks. Preprint, submitted September 24, https://arxiv.org/pdf/1909.11218.pdf.Google Scholar
- (2017) Attention is all you need. Proc. Adv. Neural Inform. Processing Systems, 5998–6008.Google Scholar
- (2009) The Toulmin argument model in artificial intelligence. Simari G, Rahwan I, eds. Argumentation in Artificial Intelligence (Springer, Boston), 219–238.Crossref, Google Scholar
- (2018) The spread of true and false news online. Science 359(6380):1146–1151.Crossref, Google Scholar
- (2009) Argumentation theory: A very short introduction. Simari G, Rahwan I, eds. Argumentation in Artificial Intelligence (Springer, Boston), 1–22.Crossref, Google Scholar
- (2019) Attention is not not explanation. Proc. 2019 Conf. Empirical Methods Natural Language Processing 9th Internat. Joint Conf. Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 11–20.Google Scholar
- (2012) Automatic detection of rumor on Sina Weibo. Proc. ACM SIGKDD Workshop Mining Data Semantics (ACM, New York).Google Scholar
- (2019) Xfake: Explainable fake news detector with visualizations. World Wide Web Conf. (ACM, New York), 3600–3604.Google Scholar
- (2023) Getting personal: A deep learning artifact for text-based measurement of personality. Inform. Systems Res. 34(1):194–222.Link, Google Scholar
- (2020) Learning to detect few-shot-few-clue misinformation. KDD TrueFact Workshop 2020 (ACM, New York), 1–9.Google Scholar
- (2015) Enquiring minds: Early detection of rumors in social media from enquiry posts. Proc. 24th Internat. Conf. World Wide Web (International World Wide Web Conferences Steering Committee, Geneva), 1395–1405.Google Scholar
- (2016) Learning reporting dynamics during breaking news for rumour detection in social media. Preprint, submitted October 24, https://arxiv.org/pdf/1610.07363.pdf.Google Scholar
- (2018) Detection and resolution of rumours in social media: A survey. ACM Comput. Surveys 51(2):32.Google Scholar

