Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts

TL;DR

This paper introduces CovidET, a dataset of COVID-19 related Reddit posts annotated for emotions and their triggers, and proposes models for emotion detection and trigger summarization in social media texts.

Contribution

The paper presents a novel dataset and baseline models for joint emotion detection and trigger summarization in social media posts about COVID-19.

Findings

CovidET enables emotion-specific summarization challenges.

Multi-emotion detection is complex in long social media posts.

Baseline models show promising results for joint emotion and trigger analysis.

Abstract

Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people's emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scattering across multiple sentences. This paper takes a novel angle, namely, emotion detection and trigger summarization, aiming to both detect perceived emotions in text, and summarize events and their appraisals that trigger each emotion. To support this goal, we introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of ~1,900 English Reddit posts related to COVID-19, which contains manual annotations of perceived emotions and abstractive summaries of their triggers described in the post. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.