Disinformation in Open Online Media [electronic resource] : Second Multidisciplinary International Symposium, MISDOOM 2020, Leiden, The Netherlands, October 26–27, 2020, Proceedings /

Checkworthiness in Automatic Claim Detection Models: Definitions and Analysis of Datasets -- How Fake News Affect Trust in the Output of a Machine Learning System for News Curation -- A Dip Into a Deep Well: Online Political Advertisements, Valence, and European Electoral Campaigning -- Misinformation from Chinese Web-based Newspapers? Machine Computational Analysis of Metabolic Disease Burden -- Students Assessing Digital News and Misinformation -- Defend Your Enemy. A Qualitative Study on Defending Political Opponents Against Hate Speech Online -- Automatically Identifying Political Ads on Facebook: Towards Understanding of Manipulation via User Targeting -- Identifying Political Sentiments on YouTube: A Systematic Comparison regarding the Accuracy of Recurrent Neural Network and Machine Learning Models -- Abusive Comments in Online Media and How to Fight Them: State of the Domain and a Call to Action -- Fake News Detection on Twitter Using Propagation Structures -- #ArsonEmergency and Australia's "Black Summer": Polarisation and Misinformation on Social Media -- How Identity and Uncertainty Affect Online Social Influence: An Agent-Based Approach -- Do Online Trolling Strategies Differ in Political and Interest Forums: Early Results -- On the Robustness of Rating Aggregators Against Injection Attacks -- FakeYou! - A Gamified Approach for Building and Evaluating Resilience Against Fake News -- Combating Disinformation: Effects of Timing and Correction Format on Factual Knowledge and Personal Beliefs -- Near Real-Time Detection of Misinformation on Online Social Networks -- Multi-modal Analysis of Misleading Political News.

Chapters “Identifying Political Sentiments on YouTube: A Systematic Comparison regarding the Accuracy of Recurrent Neural Network and Machine Learning Models”, “Do Online Trolling Strategies Differ in Political and Interest Forums: Early Results” and “Students Assessing Digital News and Misinformation” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.