Fearing fear itself - Exploring Fear Speech through Quantitative Analysis and Machine Learning: Sweden’s 2024 EU Election Campaigning on Facebook - von Schenck,Sofie (2025)
This thesis investigates the strategic use of fear speech, political communication designed to evoke fear by presenting threats, by Swedish political parties and lead candidates during the 2024 European Parliament election campaign on Facebook. Departing from audience-focused studies, the analysis centres on political actors’ intent, examining who employs fear speech, how frequently, and on which topics. A manually coded CamforS dataset of 803 posts was analysed, and a BERT-based Swedish-language classification model was fine-tuned to detect fear speech. Results show that fear speech is not confined to far-right or ideologically extreme actors; both the Sweden Democrats and centre-left parties such as the Social Democrats and the Greens used it extensively, while the far-left employed it least. Topic patterns aligned with party ideology. The best-performing model achieved an F1-score of 0.78 on fear speech detection. To address the inter-coder reliability challenges and conceptual ambiguity of the term, the thesis proposes a reframing of “fear speech” as “threat presentation”, focusing on observable rhetorical strategies rather than presumed emotional effects. The findings contribute to the conceptual clarification of fear speech, offer a methodological tool for its detection, and challenge prevailing assumptions about its ideological distribution.
Keywords: fear speech; threat presentation; political communication; social media campaigning; machine learning classification