Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media

TL;DR

This paper introduces a new bilingual dataset and a methodology for detecting agenda control in social media, specifically Twitter, during the 2022 French Presidential Elections, emphasizing the effectiveness of textual entailment approaches.

Contribution

It provides a novel French-English dataset for agenda detection and demonstrates a textual entailment-based approach that reduces the need for extensive annotated data.

Findings

Textual entailment approach improves agenda detection accuracy.

Limited annotated data can be mitigated using this methodology.

The dataset enables cross-lingual agenda analysis.

Abstract

The behavior and decision making of groups or communities can be dramatically influenced by individuals pushing particular agendas, e.g., to promote or disparage a person or an activity, to call for action, etc.. In the examination of online influence campaigns, particularly those related to important political and social events, scholars often concentrate on identifying the sources responsible for setting and controlling the agenda (e.g., public media). In this article we present a methodology for detecting specific instances of agenda control through social media where annotated data is limited or non-existent. By using a modest corpus of Twitter messages centered on the 2022 French Presidential Elections, we carry out a comprehensive evaluation of various approaches and techniques that can be applied to this problem. Our findings demonstrate that by treating the task as a textual entailment problem, it is possible to overcome the requirement for a large annotated training dataset.