Abstract
The paper gives a brief overview of the four shared tasks organized at the PAN 2023 lab on digital text forensics and stylometry to be hosted at the CLEF 2023 conference. The general goal of the PAN lab is to advance the state-of-the-art in text forensics and stylometry while ensuring objective evaluation of new and established methods on newly developed benchmark datasets. PAN’s tasks cover four areas of digital text forensics: author identification, multi-author analysis, author profiling, and content analysis. Some tasks follow up on past editions (cross-domain authorship verification, multi-author writing style analysis) and some explore novel ideas (profiling cryptocurrency influencers in social media and trigger detection). As with the previous editions, PAN invites software submissions rather than run submissions; more than 400 pieces of software have been submitted from PAN’12 through PAN’22 combined, with recent evaluations running on the TIRA experimentation platform. This proposal briefly outlines our goals for PAN as a lab and our contributions proposed for PAN’23.
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Notes
- 1.
Find PAN’s past shared tasks at pan.webis.de/shared-tasks.html.
- 2.
Find PAN’s datasets at pan.webis.de/data.html.
- 3.
All our datasets comply with the EU General Data Protection Regulation [12].
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Acknowledgments
The work from Symanto Research has been partially funded by the Pro\(^2\)Haters - Proactive Profiling of Hate Speech Spreaders (CDTi IDI-20210776), the XAI-DisInfodemics: eXplainable AI for disinformation and conspiracy detection during infodemics (MICIN PLEC2021-007681), and the ANDHI - ANomalous Diffusion of Harmful Information (CPP2021-008994) R &D grants.
The work of Paolo Rosso was in the framework of the FairTransNLP research project (PID2021-124361OB-C31).
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Bevendorff, J. et al. (2023). Overview of PAN 2023: Authorship Verification, Multi-author Writing Style Analysis, Profiling Cryptocurrency Influencers, and Trigger Detection. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_60
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