Disinformation in Open Online Media: Second Multidisciplinary International Symposium, Misdoom 2020, Leiden, the Netherlands, October 26-27, 2020, Pro

ISBN-13: 9783030618407 / Angielski / Miękka / 2020 / 277 str.

Max Van Duijn; Mike Preuss; Viktoria Spaiser

Spis treści

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.

Blurb

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.