Benjamin Shultz - An Entity-Aware Approach to Logical Fallacy

Conference Paper

Benjamin Shultz
ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, Kusadasi, Türkiye, 2024 Mar 15, pp. 780-783

DOI: 10.1145/3625007.3627988

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APA Click to copy
Shultz, B. (2024). An Entity-Aware Approach to Logical Fallacy Detection in Kremlin Social Media Content. In ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 780–783). Kusadasi, Türkiye: ACM. https://doi.org/10.1145/3625007.3627988

Chicago/Turabian Click to copy
Shultz, Benjamin. “An Entity-Aware Approach to Logical Fallacy Detection in Kremlin Social Media Content.” In ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 780–783. Kusadasi, Türkiye: ACM, 2024.

MLA Click to copy
Shultz, Benjamin. “An Entity-Aware Approach to Logical Fallacy Detection in Kremlin Social Media Content.” ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, 2024, pp. 780–83, doi:10.1145/3625007.3627988.

BibTeX Click to copy

@inproceedings{benjamin2024a,
  title = {An Entity-Aware Approach to Logical Fallacy Detection in Kremlin Social Media Content},
  year = {2024},
  month = mar,
  day = {15},
  address = {Kusadasi, Türkiye},
  journal = {ASONAM '23: Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},
  organization = {ACM},
  pages = {780-783},
  doi = {10.1145/3625007.3627988},
  author = {Shultz, Benjamin},
  month_numeric = {3}
}

Abstract:
Logical fallacy detection has emerged as a novel and challenging task for language models, more complex than traditional fake news or hate speech detection. This research-in-progress examines an Entity-Aware Approach for logical fallacy detection adapted for a timely use case of Kremlin social media content. As part of this study, a curated dataset of tweets about the war in Ukraine published by Russian government accounts, RuFal, is introduced, on which the Entity-Aware Approach is tested. Preliminary results show the Entity-Aware Approach outperforms baseline pre-trained language models by at least 0.83% on the domain non-specific LOGIC dataset and when both directly transferred to and trained on the domain specific RuFal dataset, by at least 3.09% and 0.45%, respectively, showing the Entity-Aware Approach warrants further research.