Detection and Resolution of Rumors and Misinformation with NLP (2020)

Abstract: Detecting and grounding false and misleading claims on the web has grown to form a substantial sub-field of NLP. The sub-field addresses problems at multiple different levels of misinformation detection: identifying check-worthy claims; tracking claims and rumors; rumor collection and annotation; grounding claims against knowledge bases; using stance to verify claims; and applying style analysis to detect deception. This half-day tutorial presents the theory behind each of these steps as well as the state-of-the-art solutions.

## description now we know that information can be controlled by anyone, and that it can travel more quickly -from walking to another person and talking to them to tell a story, to the modern reality of clicking to share, or commenting in a discourse making it pop up on others' feeds -what is the impact? for misinformation, it is that claims move faster through the web than can be humanly fact-checked @xcite . this means that addressing the challenge of misinformation at scale requires technological solutions. this tutorial is broken down into the following sections: ## type this is a "cutting edge" tutorial; none on the topic have been presented before at the relevant venues. rumor collection and annotation we'll attempt to do some live collection here, of data in the participant's own language, to be used later ## breadth about 25% of the material in the tutorial covers the presenters' own work; the majority is by others. the subfield is quite broad, and so even by selecting a good subset of important and recent papers to present in the time given, it would be unusual for a single researcher's work to have spanned the subfield's entire gamut. see e.g. the breadth in the bibliography for this proposal. ## diversity considerations we'll discuss techniques for multiple languages during the tutorial, and we hope to generate even more multilingual data through brief exercises in the tutorial. the presenters are affiliated in different countries (denmark and uk) with different l1 (english and basque). the tutorial includes some techniques that scale up to multiple languages (e.g. veracity prediction from stance). while both presenters are male, the senior primary investigators that got us into this and continue to lead in the field are both female (kalina bontcheva and maria liakata), so the tutorial comes from a background of gender diversity. ## prerequisites for attendees attendees should be reasonably fluent in python and comfortable using notebooks; we'll try a few live exercises. familiarity with the twitter api is a bonus, and one should have an account before starting the exercises (we don't need to know what the account is, it's simply for getting a developer api key). ## presenters one uk, one denmark (two male). leon derczynski is an assistant professor of computer science at the it university of copenhagen, denmark. he was program co-chair for coling 2018, co-author of an eu project on misinformation @xcite , pheme, co-investigator of an ec horizon 2020 ria, comrades, and has co-organised semeval shared tasks in 2013/15/16/17/19 (e.g. @xcite ). he has published repeatedly in the area of rumour identification and processing and given talks on the topics at a wide range of venues, and teaches a course on nlp-based verification and misinformation detection at innopolis university, russia. leon has taught full university courses in the uk, usa, denmark, russia, and china. ld@itu.dk www.derczynski.com . arkaitz zubiaga is an assistant professor at queen mary university of london, uk. he is on the editorial boards of the information processing & management, online social networks and media, peerj computer science, and information journals. he has published extensively on misinformation both within and beyond nlp venues, and has co-organised multiple semeval and other evaluation tasks and workshops around misinformation. a.zubiaga@qmul.ac.uk https://www.zubiaga.org . ## audience size estimate a social media processing tutorial at eacl 2014 attracted circa 30 audience members. with our field's overall growth, the global nature of the host conference, and the popularity of the subject, we expect an audience of 50-150 depending on venue.