Adapting Emotion Detection to Analyze Influence Campaigns on Social Media
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Authors
Bhaumik, Ankita
Bernhardt, Andy
Katsios, Gregorios A
Sa, Ning
Strzalkowski, Tomek
Issue Date
2023-07-01
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Abstract
Social media is an extremely potent tool for influencing public opinion, particularly during important events such as elections, pandemics, and national conflicts. Emotions are a crucial aspect of this influence, but detecting them accurately in the political domain is a significant challenge due to the lack of suitable emotion labels and training datasets. In this paper, we present a generalized approach to emotion detection that can be adapted to the political domain with minimal performance sacrifice. Our approach is designed to be easily integrated into existing models without the need for additional training or fine-tuning. We demonstrate the zero-shot and few-shot performance of our model on the 2017 French presidential elections and propose efficient emotion groupings that would aid in effectively analyzing influence campaigns and agendas on social media.
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Full Citation
Ankita Bhaumik, Andy Bernhardt, Gregorios A Katsios, Ning Sa, Tomek Strzalkowski. Adapting Emotion Detection to Analyze Influence Campaigns on Social Media. WASSA/ACL 2023.
Publisher
Association for Computational Linguistics