Abstract
The paper gives a brief overview of the four shared tasks to be organized at the PAN 2022 lab on digital text forensics and stylometry hosted at the CLEF 2022 conference. The tasks include authorship verification across discourse types, multi-author writing style analysis, author profiling, and content profiling. Some of the tasks continue and advance past editions (authorship verification and multi-author analysis) and some are new (profiling irony and stereotypes spreaders and trigger detection). The general goal of the PAN shared tasks is to advance the state of the art in text forensics and stylometry while ensuring objective evaluation on newly developed benchmark datasets.
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To generate the datasets, we have followed a methodology that complies with the EU General Data Protection Regulation [10].
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Acknowledgments
The contributions from Bauhaus-Universität Weimar and Leipzig University have been partially funded by the German Ministry for Science and Education (BMBF) project “Shared Tasks as an innovative approach to implement AI and Big Data-based applications within universities (SharKI)” (grant FKZ 16DHB4021). The Cross-DT corpus was developed at the Aston Institute for Forensic Linguistics with funding from Research England’s Expanding Excellence in England (E3) Fund. The work of the researchers from the Universitat Politècnica de València was partially funded by the Spanish MICINN under the project MISMIS-FAKEnHATE on MISinformation and MIScommunication in social media: FAKE news and HATE speech (PGC2018-096212-B-C31), and by the Generalitat Valenciana under the project DeepPattern (PROMETEO/2019/121). The work of Francisco Rangel has been partially funded by the Centre for the Development of Industrial Technology (CDTI) of the Spanish Ministry of Science and Innovation under the research project IDI-20210776 on Proactive Profiling of Hate Speech Spreaders - PROHATER (Perfilador Proactivo de Difusores de Mensajes de Odio).
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Bevendorff, J. et al. (2022). Overview of PAN 2022: Authorship Verification, Profiling Irony and Stereotype Spreaders, Style Change Detection, and Trigger Detection. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_42
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DOI: https://doi.org/10.1007/978-3-030-99739-7_42
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