Political Communities on Twitter: Case Study of the 2022 French Presidential Election
Hadi Abdine | Yanzhu Guo | Virgile Rennard | Michalis Vazirgiannis
Proceedings of the LREC 2022 workshop on Natural Language Processing for Political Sciences
With the significant increase in users on social media platforms, a new means of political campaigning has appeared. Twitter and Facebook are now notable campaigning tools during elections. Indeed, the candidates and their parties now take to the internet to interact and spread their ideas. In this paper, we aim to identify political communities formed on Twitter during the 2022 French presidential election and analyze each respective community. We create a large-scale Twitter dataset containing 1.2 million users and 62.6 million tweets that mention keywords relevant to the election. We perform community detection on a retweet graph of users and propose an in-depth analysis of the stance of each community. Finally, we attempt to detect offensive tweets and automatic bots, comparing across communities in order to gain insight into each candidate’s supporter demographics and online campaign strategy.
Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency
Yanzhu Guo | Chloé Clavel | Moussa Kamal Eddine | Michalis Vazirgiannis
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
The topic of summarization evaluation has recently attracted a surge of attention due to the rapid development of abstractive summarization systems. However, the formulation of the task is rather ambiguous, neither the linguistic nor the natural language processing communities have succeeded in giving a mutually agreed-upon definition. Due to this lack of well-defined formulation, a large number of popular abstractive summarization datasets are constructed in a manner that neither guarantees validity nor meets one of the most essential criteria of summarization: factual consistency. In this paper, we address this issue by combining state-of-the-art factual consistency models to identify the problematic instances present in popular summarization datasets. We release SummFC, a filtered summarization dataset with improved factual consistency, and demonstrate that models trained on this dataset achieve improved performance in nearly all quality aspects. We argue that our dataset should become a valid benchmark for developing and evaluating summarization systems.
BERTweetFR : Domain Adaptation of Pre-Trained Language Models for French Tweets
Yanzhu Guo | Virgile Rennard | Christos Xypolopoulos | Michalis Vazirgiannis
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)
We introduce BERTweetFR, the first large-scale pre-trained language model for French tweets. Our model is initialised using a general-domain French language model CamemBERT which follows the base architecture of BERT. Experiments show that BERTweetFR outperforms all previous general-domain French language models on two downstream Twitter NLP tasks of offensiveness identification and named entity recognition. The dataset used in the offensiveness detection task is first created and annotated by our team, filling in the gap of such analytic datasets in French. We make our model publicly available in the transformers library with the aim of promoting future research in analytic tasks for French tweets.
- Michalis Vazirgiannis 3
- Virgile Rennard 2
- Christos Xypolopoulos 1
- Hadi Abdine 1
- Chloé Clavel 1
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