The Battlefront of Combating Misinformation and Coping with Media Bias (2022)

Abstract: Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust, confusion, and anxiety that cause damage on our daily life judgments and public policy decisions. While recent studies have explored various fake news detection and media bias detection techniques in attempts to tackle the problem, there remain many ongoing challenges yet to be addressed, as can be witnessed from the plethora of untrue and harmful content present during the COVID-19 pandemic and the international crises of late. In this tutorial, we provide researchers and practitioners with a systematic overview of the frontier in fighting misinformation. Specifically, we dive into the important research questions of how to (i) develop a robust fake news detection system, which not only fact-check information pieces provable by background knowledge but also reason about the consistency and the reliability of subtle details for emerging events; (ii) uncover the bias and agenda of news sources to better characterize misinformation; as well as (iii) correct false information and mitigate news bias, while allowing diverse opinions to be expressed. Moreover, we discuss the remaining challenges, future research directions, and exciting opportunities to help make this world a better place, with safer and more harmonic information sharing.

## introduction the growth of online platforms has greatly facilitated the way people communicate with each other and stay informed about trending events. however, it has also spawned unprecedented levels of inaccurate or misleading information, as traditional journalism gate-keeping fails to keep up with the pace of media dissemination. these undesirable phenomena have caused societies to be torn over irrational beliefs, money lost from impulsive stock market moves, and deaths occurred that could have been avoided during the covid-19 pandemic, due to the infodemic that came forth with it, etc. @xcite @xcite . even people who do not believe the misinformation may still be plagued by the pollution of unhealthy content surrounding them, an unpleasant situation known as information disorder @xcite . thus, it is of pertinent interest for our community to better understand, and to develop effective mechanisms for remedying misinformation and biased reporting. the emerging nature of news events, which also span diverse domains (e.g., economy, military, health, sports, etc.) and reporting style (e.g., long text vs. short text, realistic image vs. artistic image, etc.), makes misinformation detection and characterization challenging. combating fake news and biased reports involve an interdisciplinary research area of reasoning on the semantics, style, crossmedia contextualization, background knowledge, and propagation patterns, among others @xcite @xcite . moreover, the recent trends towards a more comprehensive understanding of the source stance, reporting intent, target audience, and propaganda technique behind a problematic piece of news @xcite require greater socio-cultural norm and common sense awareness. in this half-day tutorial, we aim to present a systematic overview of technological advancement in tackling interconnected tasks related to misinformation, media bias, malicious intent monitoring, and corrective actions. first, we will review prevailing paradigms and data resources for misinformation detection and characterization. moreover, we will discuss the latest approaches to automatically explain why a news piece is inaccurate or misleading, and perform rectification of biased reporting. the participants will learn about trends and emerging challenges, representative deep neural network models, ready-to-use training resources, as well as how state-of-the-art language (and multimedia) techniques can help build applications for the social good. ## specification of the tutorial the proposed tutorial is a cutting-edge tutorial that introduces new frontiers in research on battling misinformation and news bias. the presented topic has not been covered by previous acl/naacl/aacl tutorials in the past four years. while there has been an emnlp'20 tutorial on "fact-checking, fake news, propaganda, and media bias: truth seeking in the post-truth era" @xcite ) and a col-ing'20 tutorial on "detection and resolution of rumors and misinformation with nlp" (derczynski and zubiaga, 2020), fake news is a continuously evolving and extremely important societal problem. in our tutorial, we place particular emphasis on the latest lines of development, including an emphasis on multimedia contextualization, sociocultural awareness in characterization, and corrective actions. we estimate at least 75% of the work we reference has not been covered in the two previous aforementioned tutorials. we further estimate that at least 75% of the research covered in this tutorial is by researchers other than the instructors. ## tutorial instructors below, we give the biographies of the speakers. yi r. fung is a second-year ph.d. student at the computer science department of uiuc, with research interests in knowledge reasoning, misinformation detection, and computation for the social good. her recent works include the info-surgeon fake news detection framework, and multiview news summarization. yi is a recipient of the naacl'21 best demo paper, the uiuc lauslen and andrew fellowship, and the national association of asian american professionals future leaders award. she has also been previously selected for invited talk (1 hour presentation) at the harvard medical school bioinformatics seminar. additional information is available at https://yrf1.github.io . kung-hsiang huang is a first-year ph.d. student at the computer science department of uiuc. his research focuses on fact-checking and fake news detection. prior to joining uiuc, he obtained his b.eng. in computer science from the hong kong university of science and technology, and his m.s. in computer science is from usc. he is also a co-founder of an ai startup, rosetta.ai. additional information is available at https://khuangaf .github.io . preslav nakov is a principal scientist at the qatar computing research institute (qcri), hbku, who received his phd degree from the university of california at berkeley (supported by a fulbright grant). dr. nakov is president of acl siglex, secretary of acl sigslav, a member of the eacl advisory board, as well as a member of the editorial board of computational linguistics, tacl, cs&l, ieee tac, nle, ai communica-tions, and frontiers in ai. his research on fake news was featured by over 100 news outlets, including forbes, boston globe, aljazeera, mit technology review, science daily, popular science, the register, wired, and engadget, among others. he has driven relevant tutorials such as: • wsdm'22: fact-checking, fake news, propaganda, media bias, and the covid-19 infodemic. • cikm'21: fake news, disinformation, propaganda, and media bias. • emnlp'20: fact-checking, fake news, propaganda, and media bias: truth seeking in the post-truth era. additional information is available at https:// en.wikipedia.org/wiki/preslav_nak ov. heng ji is a professor at the computer science department of the university of illinois urbana-champaign, and an amazon scholar. her research interests focus on nlp, especially on multimedia multilingual information extraction, knowledge base population and knowledge-driven generation. she was selected as "young scientist" and a member of the global future council on the future of computing by the world economic forum. the awards she received include "ai's 10 to watch" award, nsf career award, google research award, ibm watson faculty award, bosch research award, amazon aws award, acl2020 best demo paper award, and naacl2021 best demo paper award. she has given a large number of keynotes and 20 tutorials on information extraction, natural language understanding, and knowledge base construction in many conferences including but not limited to acl, emnlp, naacl, neurips, aaai, sigir, www, ijcai, coling and kdd. a selected handful of her recent tutorials include: • aaai'22: deep learning on graphs for natural language processing. language processing. • emnlp'21: knowledge-enriched natural language generation. • acl'21: event-centric natural language processing. additional information is available at https:// blender.cs.illinois.edu/hengji.h tml.