Abstract
This study examines the application of algorithmic technologies to the analysis of political communication, with a specific focus on the role of facial detection systems in electoral contexts. Although existing scholarship on digital campaigning has prioritized textual and discursive strategies, the non-verbal dimension of political communication remains insufficiently explored. Facial recognition and expression-detection tools provide a novel methodological framework for assessing how candidates’ body language—particularly facial cues—shapes perceptions of credibility, leadership, and emotional connection with the electorate. By incorporating these computational approaches, the research addresses a significant gap in the study of non-verbal political communication. The empirical analysis centers on the Spanish case, where recent national elections have showcased the strategic use of televised debates and digital media as platforms for candidates to project persuasive non-verbal signals. Applying algorithmic face detection to these contexts allows for the systematic measurement of expressions such as confidence, hesitation, or empathy, thus offering new insights into how political figures construct their public image beyond verbal discourse. The study demonstrates how the integration of advanced technologies into political communication research not only expands methodological possibilities but also enhances our understanding of the subtle, yet influential, role of non-verbal communication in democratic processes.
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
KEYWORDS
Political Communication, Algorithms, Face Detection, Elections
