BeyondFacts’22: 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis
Authors: 
Konstantin Todorov, Pavlos Fafalios, and Stefan DietzeAuthors Info & Claims
Konstantin Todorov, Pavlos Fafalios, and Stefan DietzeAuthors Info & ClaimsApril 2022
Pages 423 - 425
Abstract
Expressing opinions and interacting with others on the Web has led to an abundance of online discourse: claims and viewpoints on controversial topics, their sources and contexts. This constitutes a valuable source of insights for studies into mis- / disinformation spread, bias reinforcement, echo chambers or political agenda setting. While knowledge graphs promise to provide the key to a Web of structured information, they are mainly focused on facts without keeping track of the diversity, connection or temporal evolution of online discourse. As opposed to facts, claims and viewpoints are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understandings strongly diverge across communities from computational social science, to argumentation mining, fact-checking, or viewpoint/stance detection. The 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis (BeyondFacts’22, equivalently abbreviated as KnOD’22) aims at strengthening the relations between these communities, providing a forum for shared works on the modeling, extraction and analysis of discourse on the Web. It addresses the need for a shared understanding and structured knowledge about discourse in order to enable machine-interpretation, discoverability and reuse, in support of studies into the analysis of societal debates.
References
[1]
Mehwish Alam, Andreea Iana, Alexander Grote, Katharina Ludwig, Philipp Müller, and Heiko Paulheim. 2022. Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection. In Companion Proceedings of the Web Conference 2022. ACM.
[2]
Pepa Atanasova, Preslav Nakov, Lluís Màrquez, Alberto Barrón-Cedeño, Georgi Karadzhov, Tsvetomila Mihaylova, Mitra Mohtarami, and James Glass. 2019. Automatic fact-checking using context and discourse information. Journal of Data and Information Quality (JDIQ) 11, 3 (2019), 1–27.
[3]
Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen. 2019. MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims. In Proceedings of the 2019 EMNLP and the 9th IJCNLP. 4677–4691.
[4]
Anab Maulana Barik, Wynne Hsu, and Mong Li Lee. 2022. Incorporating External Knowledge for Evidence-based Fact Verification. In Companion Proceedings of the Web Conference 2022. ACM.
[5]
Katarina Boland, Pavlos Fafalios, Andon Tchechmedjiev, Stefan Dietze, and Konstantin Todorov. 2022. Beyond Facts–A Survey and Conceptualisation of Claims in Online Discourse Analysis. Semantic Web (2022).
[6]
Katarina Boland, Pavlos Fafalios, Andon Tchechmedjiev, Konstantin Todorov, and Stefan Dietze. 2019. Modeling and contextualizing claims. In 2nd International Workshop on Contextualized Knowledge Graphs (CKG 2019 @ISWC).
[7]
Debraj De, Gautam Thakur, Drahomira Herrmannova, and Christopher Carter. 2022. Methodology to Compare Twitter Reaction Trends between Disinformation Communities, to COVID related Campaign Events at Different Geospatial Granularities. In Companion Proceedings of the Web Conference 2022. ACM.
[8]
Dimitar Dimitrov, Erdal Baran, Pavlos Fafalios, Ran Yu, Xiaofei Zhu, Matthäus Zloch, and Stefan Dietze. 2020. TweetsCOV19-A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2991–2998.
[9]
Dimitar Dimitrov, Dennis Segeth, and Stefan Dietze. 2022. Geotagging TweetsCOV19: Enriching a COVID-19 Twitter Discourse Knowledge Base with Geographic Information. In Companion Proceedings of the Web Conference 2022. ACM.
[10]
Pavlos Fafalios, Vasileios Iosifidis, Eirini Ntoutsi, and Stefan Dietze. 2018. Tweetskb: A public and large-scale rdf corpus of annotated tweets. In European Semantic Web Conference. Springer, 177–190.
[11]
Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2018. Quantifying controversy on social media. ACM Trans. on Soc. Comp. 1, 1 (2018), 3.
[12]
Anh Le-Tuan, Carlos Franzreb, Danh Le Phuoc, Sonja Schimmler, and Manfred Hauswirth. 2022. Towards Building Live Open Scientific Knowledge Graphs. In Companion Proceedings of the Web Conference 2022. ACM.
[13]
Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2017. Where the truth lies: Explaining the credibility of emerging claims on the web and social media. In Proceedings of the 26th International Conference on World Wide Web Companion. 1003–1012.
[14]
Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter 19, 1 (2017), 22–36.
[15]
Andon Tchechmedjiev, Pavlos Fafalios, Katarina Boland, Malo Gasquet, Matthäus Zloch, Benjamin Zapilko, Stefan Dietze, and Konstantin Todorov. 2019. ClaimsKG: A knowledge graph of fact-checked claims. In International Semantic Web Conference. Springer, 309–324.
[16]
Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146–1151.
[17]
Xuezhi Wang, Cong Yu, Simon Baumgartner, and Flip Korn. 2018. Relevant Document Discovery for Fact-Checking Articles. In WWW. 525–533.
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- BeyondFacts’22: 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis
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April 2022
1338 pages
ISBN:9781450391306
DOI:10.1145/3487553
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Published: 16 August 2022
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