English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Online media has revolutionized the way political information is disseminatedand consumed on a global scale, and this shift has compelled political figuresto adopt new strategies of capturing and retaining voter attention. Thesestrategies often rely on emotional persuasion and appeal, and as visual contentbecomes increasingly prevalent in virtual space, much of politicalcommunication too has come to be marked by evocative video content and imagery.The present paper offers a novel approach to analyzing material of this kind.We apply a deep-learning-based computer-vision algorithm to a sample of 220YouTube videos depicting political leaders from 15 different countries, whichis based on an existing trained convolutional neural network architectureprovided by the Python library fer. The algorithm returns emotion scoresrepresenting the relative presence of 6 emotional states (anger, disgust, fear,happiness, sadness, and surprise) and a neutral expression for each frame ofthe processed YouTube video. We observe statistically significant differencesin the average score of expressed negative emotions between groups of leaderswith varying degrees of populist rhetoric as defined by the Global Party Survey(GPS), indicating that populist leaders tend to express negative emotions to agreater extent during their public performance than their non-populistcounterparts. Overall, our contribution provides insight into thecharacteristics of visual self-representation among political leaders, as wellas an open-source workflow for further computational studies of theirnon-verbal communication.

The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning