Unveiling Political Influence Through Social Media: Network and Causal Dynamics in the 2022 French Presidential Election

TL;DR

This study analyzes social media interactions during the 2022 French presidential election to uncover directional political influences and key topics shaping online political discourse using causal inference techniques.

Contribution

It introduces a novel application of Convergent Cross Mapping to social media data for identifying true directional influence among political communities.

Findings

Health and foreign policy issues drive cross-party influence.

Asymmetric influence relationships are revealed among political parties.

Causal dynamics differ across critical election phases.

Abstract

During the 2022 French presidential election, we collected daily Twitter messages on key topics posted by political candidates and their close networks. Using a data-driven approach, we analyze interactions among political parties, identifying central topics that shape the landscape of political debate. Moving beyond traditional correlation analyses, we apply a causal inference technique: Convergent Cross Mapping, to uncover directional influences among political communities, revealing how some parties are more likely to initiate changes in activity while others tend to respond. This approach allows us to distinguish true influence from mere correlation, highlighting asymmetric relationships and hidden dynamics within the social media political network. Our findings demonstrate how specific issues, such as health and foreign policy, act as catalysts for cross-party influence, particularly during critical election phases. These insights provide a novel framework for understanding political discourse dynamics and have practical implications for campaign strategists and media analysts seeking to monitor and respond to shifts in political influence in real time.