no code implementations • ACL (WOAH) 2021 • Piush Aggarwal, Michelle Espranita Liman, Darina Gold, Torsten Zesch
This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021.
no code implementations • COLING (WNUT) 2022 • Piush Aggarwal, Torsten Zesch
Hate speech detection systems have been shown to be vulnerable against obfuscation attacks, where a potential hater tries to circumvent detection by deliberately introducing noise in their posts.
no code implementations • 7 Feb 2024 • Piush Aggarwal, Jawar Mehrabanian, Weigang Huang, Özge Alacam, Torsten Zesch
This paper delves into the formidable challenge of cross-domain generalization in multimodal hate meme detection, presenting compelling findings.
no code implementations • 11 Feb 2023 • Piush Aggarwal, Pranit Chawla, Mithun Das, Punyajoy Saha, Binny Mathew, Torsten Zesch, Animesh Mukherjee
Empirically, we find a noticeable performance drop of as high as 10% in the macro-F1 score for certain attacks.
no code implementations • RANLP 2019 • Piush Aggarwal
For users to be able to draw on benefits from news reported on social media it is necessary to reliably identify news content and differentiate it from non-news.
no code implementations • RANLP 2019 • Piush Aggarwal, Ahmet Aker
We also perform a comparative analysis with sentiments showing that sentiment alone is not enough to distinguish between good and bad news.
no code implementations • SEMEVAL 2019 • Piush Aggarwal, Tobias Horsmann, Michael Wojatzki, Torsten Zesch
We present results for Subtask A and C of SemEval 2019 Shared Task 6.
no code implementations • WS 2018 • Vincentius Kevin, Birte H{\"o}gden, Claudia Schwenger, Ali {\c{S}}ahan, Neelu Madan, Piush Aggarwal, Anusha Bangaru, Farid Muradov, Ahmet Aker
This paper discusses the choice of the labels, their implementation and visualization.