Uncovering Agendas: A Novel French & English Dataset for Agenda Detection on Social Media
This addresses the challenge of identifying influence campaigns in online political discourse, though it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of detecting agenda control in social media with limited annotated data, specifically for the 2022 French Presidential Elections, and found that framing the task as textual entailment reduces the need for large training datasets.
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.