Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts (2022)

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.

## introduction large-scale crises such as the covid-19 pandemic continuously cause emotional turmoil worldwide. people are emotionally affected in different ways, e.g., online education has led to mental health issues among students @xcite as well as parents @xcite ; lock-down policies are protective for the vulnerable @xcite while economically disastrous for many @xcite . emotion analysisboth detecting emotion and understanding what triggers the emotion -brings invaluable insights both practically (e.g., for first responders, counselors, etc) and in scientific research @xcite . while emotion detection (typically formulated as a classification task among standard emotion taxonomies) is a well-established task (e.g., @xcite ; @xcite ; @xcite ; @xcite ; @xcite and in crises contexts @xcite ), fewer have studied what leads to these emotions in the scope of the text concerned in a data-driven manner. @xcite adopt an extraction setup to identify emotion "causes" that is limited to the clause level, where only one (explicitly expressed) emotion and one cause are associated. this does not generalize to long, spontaneous social media posts that are emotionally charged. illustrated in figure 1 , distinct emotions are triggered by different events across multiple sentences. additionally, how these events are subjectively evaluated, interpreted or appraised, e.g., "i can't do anything about it" in the first example of figure 1 , also contribute to the emotion @xcite . the fact that different individuals may have distinct appraisals towards the same event @xcite further highlights the challenging nature of understanding what triggers an emotion. in this work we take a novel view, and formulate emotion-trigger detection as an abstractive summarization task that synthesizes a natural language description of the events and their appraisals that trigger a particular emotion. we frame our work as emotion detection and trigger summarization (figure 1 ), which entails both detecting perceived emotions in text, and summarizing triggers for each emotion. we present covidet (emotions and their triggers during covid-19), a new dataset sourced from 1, 883 english reddit posts about the covid-19 pandemic. each post is annotated with 7 finegrained emotion labels; for each emotion, annotators provided a concise, abstractive summary describing the triggers of the emotion. the triggers are further validated in a separate stage. covidet spans from june 2021 to january 2022, capturing various significant events as well as how they were emotionally appraised during the pandemic. compared to prior emotion studies that consider only sentence-level texts @xcite or (short) tweets @xcite , covidet is challenging as it contains significantly longer texts. we showcase examples of covidet in appendix §a. analyses of covidet reveal that negative emotions such as fear and anger are prevalent. these emotions co-occur most frequently with anticipation, which consistently rise after the omicron subvariant became more dominant with fear dropping. topic modeling over the trigger summaries points to irritations toward those who don't mask or get vaccinated, and positivity towards the vaccines. using covidet, we benchmark models for emotion detection and emotion-trigger summarization. we employ both separate emotion detection and trigger summarization models, as well as joint models that we designed to simultaneously detect emotions and generate trigger summaries. our experiments showcase the distinct nature of our task, emphasizing that covidet is vital to training reliable emotion detection and trigger summarization approaches in a covid-19 context. covidet bears various unique characteristics, ranging from its long sequences and invaluable context to the nature of the task itself. therefore, general emotion detection or summarization models unsurprisingly lag behind in performance compared to our meth-ods. moreover, human evaluation of the generated trigger summaries tailored for emotion-trigger summarization indicates that our models are effective in capturing the underlying triggers of the post. we release covidet and our code at https: //github.com/honglizhan/covidet . in this sense our setup fits into settings where only a certain part of the content is of interest to the user. we could view our task as answering a query, "why does the writer feel [emotion]?". however, such queries are more general than query-based summarization @xcite @xcite @xcite @xcite , where queries tend to be more document-specific. perhaps a closer task is opinion summarization, or aspect-based summarization more generally. in opinion summarization, models need to summarize affect/opinions about a certain aspect of a service or product @xcite @xcite @xcite ; on the contrary, our setup entails identifying the emotions and summarizing the events and how they were made sense of with respect to each emotion. in aspect-based summarization, existing work has explored summarizing with respect to predesignated aspects of certain news @xcite , and entities mentioned in text @xcite . ## dataset construction we present covidet, a novel dataset from english reddit posts that is manually annotated with emotions and summaries of their triggers. this section discusses the data creation process; in §4, we discuss inter-annotator agreement and our human verification process. ## agreement and validation to account for the quality of covidet, we measure the inter-annotator agreement in emotions ( §4.1) and triggers ( §4.2). the annotations are further validated through human inspection in §4.3. results reveal that annotators tend to agree with each other in emotions whilst using varied vocabularies when summarizing the triggers. ## data analysis emotion distribution. on average, there are 2.46 emotions ("none" excluded) per example in covidet. figure 3 shows the general emotion distribution of covidet. fear is the most common emotion in covidet, closely followed by anticipation. there is clearly a gap among the emotions, with positively valenced emotions such as trust and joy rarely present in covidet. this is predicted given the catastrophic nature of our domain. we present the emotion co-occurrence heatmap in figure 5 . anticipation co-occurs with fear and anger most frequently in covidet. close scrutiny of the data reveals that the poster is either predicting negative events during covid-19, or expecting advice on what do do under austere situations. emotion trend. we present a temporal analysis of the emotion distribution by week in figure 4 , using a red vertical line to separate pre-and post-omicron. interestingly, we notice that the amount of anticipation consistently rises after the outbreak of the omicron covid-19 variant, whereas the expression of negative emotions including anger and fear becomes less prevalent, possibly due to the nature of the omicron variant, which was less harmful compared to previous variants @xcite . this result is also unsurprising in that people are getting weary and tired after two years of avoiding covid-19. ## methods we discuss our methods across three main dimensions: emotion detection, summarization, and joint emotion detection and trigger summarization. ## conclusion we propose a new task entitled emotion detection and trigger summarization, which aims to jointly detect perceived emotions in text and summarize the events as well as their appraisals that trigger each emotion. to address the task, we introduce covidet, a dataset of 1, 883 english reddit posts on covid-19 annotated with emotions and abstractive summaries of their triggers. experiments using our proposed joint model on the dataset reveal that covidet is vital resource for training models to capture emotions and their triggers in text. our thorough evaluation of model-generated summaries emphasize that covidet is a challenging benchmark, and our error analysis indicates potential areas of improvements (e.g., improving the factuality of the summaries).