About this Research Topic
The web is a space for all, allowing participating individuals to read, publish and share content openly. Despite its groundbreaking benefits in areas such as education and communication, it has also become a breeding ground for misbehavior and misinformation. Any individual can reach thousands of people on the web near-instantaneously, purporting whatever they wish while also being shielded by anonymity. Additionally, cutting-edge techniques in generative AI, such as Deepfakes, have made it increasingly important to check the credibility and reliability of data. Similarly, large volumes of data are generated from diverse information channels like social media, online news outlets, and crowd-sourcing contributes valuable knowledge. The value of this large amount of data is encumbered with challenges in ascertaining the credibility of user-generated and machine-generated information, resolving conflicts among heterogeneous data sources, identifying misinformation, rumors and bias, etc. This explosion of information and knowledge has led to rampant increases in misbehavior and misinformation vectors, via harassment, online scams, the spread of propaganda, hate speech, deceptive reviews and more. Such issues have severe implications on both social and financial fronts.
This Research Topic, led by the organizers of MIS2-TrueFact@KDD, provides a venue for researchers and practitioners from academia, government and industry to share insights and identify new challenges and opportunities adjacent to these diverse areas to coalesce around central and timely topics in online misinformation and misbehavior, resolving conflicts, fact-checking and ascertaining the credibility of claims and present recent advances in research.
Topics of interest include (but are not limited to):
• Truth finding and discovery
• Fact-checking, rumor, and misinformation
• Credibility analysis and spam detection
• Fake reviews and reviewers
• Leveraging knowledge bases for reasoning, validating and explaining contentious claims
• Transparency, fairness, bias, privacy and ethics of information systems
• Emerging applications, novel data sources, and case studies
• Explainable and interpretable models
• Robustness detection under adversarial and unknown data poisoning and evasion attacks.
• Heterogeneous and multi-modal information including relational data, natural language text, search logs, images, video, etc.
• Empirical characterization of false information
• Measuring real world and online impact
• Deception in misinformation and misbehavior
• Reputation manipulation
• Measuring economic, ideological, and other rationale behind creation
• Rationale behind spread and success
• Targets or victims of misbehavior and misinformation
• Effect of echo chambers, personalization, confirmation bias, and other socio-psychological and technological phenomenon
• Detection methods using graphs, text, behavior, image, video, and audio analysis
• Adversarial analysis of misbehavior and misinformation
• Prevention and mitigation techniques, tools, and countermeasures
• Theoretical and/or empirical modeling of spread
• Visualizing spread
• Anonymity, security, and privacy aspects of data collection
• Usable security in misbehavior detection
• Ethics, privacy, fairness, and biases in current tools and techniques
• Case studies
Topic Editor Neil Shah is employed by Snap Inc. All other Topic Editors declare no competing interests with regard to the Research Topic subject..
Keywords: Fact-checking, Misinformation, Credibility analysis, Spam detection, Fake reviewers, Misbehavior detection, Transparency, Fairness, Robustness, Fake news
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.