Search Results for author: Nedjma Ousidhoum

Found 11 papers, 10 papers with code

Words as Trigger Points in Social Media Discussions

no code implementations16 May 2024 Dimosthenis Antypas, Christian Arnold, Jose Camacho-Collados, Nedjma Ousidhoum, Carla Perez Almendros

Our work is the first to introduce trigger points to computational studies of online communication.

The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who

1 code implementation27 Apr 2023 Michael Schlichtkrull, Nedjma Ousidhoum, Andreas Vlachos

Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation.

Fact Checking Misinformation

SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)

1 code implementation13 Apr 2023 Shamsuddeen Hassan Muhammad, Idris Abdulmumin, Seid Muhie Yimam, David Ifeoluwa Adelani, Ibrahim Sa'id Ahmad, Nedjma Ousidhoum, Abinew Ayele, Saif M. Mohammad, Meriem Beloucif, Sebastian Ruder

We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github. com/afrisenti-semeval/afrisent-semeval-2023.

Classification Sentiment Analysis +2

Varifocal Question Generation for Fact-checking

1 code implementation22 Oct 2022 Nedjma Ousidhoum, Zhangdie Yuan, Andreas Vlachos

Our method outperforms previous work on a fact-checking question generation dataset on a wide range of automatic evaluation metrics.

Fact Checking Question Answering +2

Probing Toxic Content in Large Pre-Trained Language Models

1 code implementation ACL 2021 Nedjma Ousidhoum, Xinran Zhao, Tianqing Fang, Yangqiu Song, Dit-yan Yeung

Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems.

Probing Language Models Sentence

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