PAN at CLEF 2023
- Cross-Discourse Type Authorship Verification
- Profiling Cryptocurrency Influencers with Few-shot Learning
- Multi-Author Writing Style Analysis
- Trigger Detection
- May 10, 2023: Early bird software submission phase (optional)
- June 10, 2023: Software submission deadline (extended, was May 29)
- June 05, 2023: Participant paper submission Midnight CEST [guidelines] [submission] [template (use this one, not the one from CLEF]
- June 23, 2023: Peer review notification
- July 07, 2023: Camera-ready participant papers submission Midnight CEST
- tba: Early bird conference registration
- September 18-21, 2023: Conference
The timezone of all deadlines is Anywhere on Earth.
Detecting and Disrupting Disinformation: Social Network Analysis and Natural Language Processing In recent years, social network analysis has become an increasingly important tool in understanding and combating the spread of disinformation. By examining the patterns of information flow, we can identify the sources and pathways through which false information spreads and take steps to prevent its dissemination. However, the problem of disinformation is not limited to the propagation of already-existing false information; AI is now being used to generate convincing fake news, amplifying the spread of disinformation and making it increasingly difficult to distinguish fact from fiction. Despite the challenges posed by AI-generated false content, AI can also play an important role in the fight against disinformation. Through the use of natural language processing techniques, we can reveal key characteristics of false information, enabling us to more effectively identify and combat disinformation campaigns. Additionally, machine learning algorithms can be used to identify patterns of disinformation and to distinguish between false and genuine content. In this talk, we will explore the current state of the art in social network analysis and AI with respect to disinformation. We will present examples of different models and architectures that have been developed to combat disinformation, including case studies of real-world disinformation campaigns. By combining the strengths of social network analysis and AI (i.e., large language models), we can develop more effective tools for combating the spread of disinformation, protecting the integrity of public discourse, and upholding the principles of truth and accuracy in our information ecosystem.