UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers

Andrei Paraschiv; Mihai Dascalu

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arXiv · 2309.16275

UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers

Andrei Paraschiv·Mihai Dascalu

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Abstract

Conspiracy theories have become a prominent and concerning aspect of online discourse, posing challenges to information integrity and societal trust. As such, we address conspiracy theory detection as proposed by the ACTI @ EVALITA 2023 shared task. The combination of pre-trained sentence Transformer models and data augmentation techniques enabled us to secure first place in the final leaderboard of both sub-tasks. Our methodology attained F1 scores of 85.71% in the binary classification and 91.23% for the fine-grained conspiracy topic classification, surpassing other competing systems.

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Andrei Paraschiv, Mihai Dascalu. 2023-09-28. UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers. https://arxiv.org/abs/2309.16275

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