BeyondFacts’22: 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis

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.

[1]  M. Hauswirth,et al.  Towards Building Live Open Scientific Knowledge Graphs , 2022, WWW.

[2]  S. Dietze,et al.  Geotagging TweetsCOV19: Enriching a COVID-19 Twitter Discourse Knowledge Base with Geographic Information , 2022, WWW.

[3]  Anab Maulana Barik,et al.  Incorporating External Knowledge for Evidence-based Fact Verification , 2022, WWW.

[4]  Debraj De,et al.  Methodology to Compare Twitter Reaction Trends between Disinformation Communities, to COVID related Campaign Events at Different Geospatial Granularities , 2022, WWW.

[5]  H. Paulheim,et al.  Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection , 2022, WWW.

[6]  S. Dietze,et al.  Beyond facts - a survey and conceptualisation of claims in online discourse analysis , 2022, Semantic Web.

[7]  Stefan Dietze,et al.  TweetsCOV19 - A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic , 2020, CIKM.

[8]  Stefan Dietze,et al.  ClaimsKG: A Knowledge Graph of Fact-Checked Claims , 2019, SEMWEB.

[9]  Christian Hansen,et al.  MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims , 2019, EMNLP.

[10]  Preslav Nakov,et al.  Automatic Fact-Checking Using Context and Discourse Information , 2019, ACM J. Data Inf. Qual..

[11]  Stefan Dietze,et al.  Modeling and Contextualizing Claims , 2019, BlockSW/CKG@ISWC.

[12]  Stefan Dietze,et al.  TweetsKB: A Public and Large-Scale RDF Corpus of Annotated Tweets , 2018, ESWC.

[13]  Xuezhi Wang,et al.  Relevant Document Discovery for Fact-Checking Articles , 2018, WWW.

[14]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[15]  Aristides Gionis,et al.  Quantifying Controversy on Social Media , 2018, ACM Trans. Soc. Comput..

[16]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[17]  Gerhard Weikum,et al.  Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media , 2017, WWW.

[18]  왕숙희,et al.  Geographic Information , 1984, Encyclopedia of GIS.