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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.</abstract>
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%0 Conference Proceedings
%T Adapting Emotion Detection to Analyze Influence Campaigns on Social Media
%A Bhaumik, Ankita
%A Bernhardt, Andy
%A Katsios, Gregorios
%A Sa, Ning
%A Strzalkowski, Tomek
%Y Barnes, Jeremy
%Y De Clercq, Orphée
%Y Klinger, Roman
%S Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F bhaumik-etal-2023-adapting
%X 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.
%R 10.18653/v1/2023.wassa-1.38
%U https://aclanthology.org/2023.wassa-1.38
%U https://doi.org/10.18653/v1/2023.wassa-1.38
%P 441-451

Markdown (Informal)

[Adapting Emotion Detection to Analyze Influence Campaigns on Social Media](https://aclanthology.org/2023.wassa-1.38) (Bhaumik et al., WASSA 2023)

• Adapting Emotion Detection to Analyze Influence Campaigns on Social Media (Bhaumik et al., WASSA 2023)

ACL

• Ankita Bhaumik, Andy Bernhardt, Gregorios Katsios, Ning Sa, and Tomek Strzalkowski. 2023. Adapting Emotion Detection to Analyze Influence Campaigns on Social Media. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 441–451, Toronto, Canada. Association for Computational Linguistics.