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.
@article{arxiv.2309.16275,
title = {UPB @ ACTI: Detecting Conspiracies using fine tuned Sentence Transformers},
author = {Andrei Paraschiv and Mihai Dascalu},
journal= {arXiv preprint arXiv:2309.16275},
year = {2023}
}