Carley Reardon, Sejin Paik, Ge Gao, Meet Parekh, Yanling Zhao, Lei Guo, Margrit Betke, Derry Tanti Wijaya
| Challenge: | Using a dataset that contains headline and image pairings from 840 news articles, we explore the relationship between image and text influence on human emotional response. |
| Approach: | They propose to use a U.S. gun violence news dataset that contains headline and image pairings from 840 news articles with 15K high-quality crowdsourced annotations on emotional responses. |
| Outcome: | The proposed dataset includes annotations on the dominant emotion experienced with the content, the intensity of the selected emotion and an open-ended, written component. |
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Prediction of People’s Emotional Response towards Multi-modal News (2022.aacl-main)
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Ge Gao, Sejin Paik, Carley Reardon, Yanling Zhao, Lei Guo, Prakash Ishwar, Margrit Betke, Derry Tanti Wijaya
| Challenge: | BU-NEmo dataset extends from 320 to 1,297 news headline and lead image pairings and collects 38,910 annotations in a crowdsourcing experiment. |
| Approach: | They extend the U.S. gun violence news-to-emotions dataset from 320 to 1,297 news headline and lead image pairings and collect annotations in a crowdsourcing experiment. |
| Outcome: | The proposed models outperform baseline models on the NEmo+ dataset by large margins across several metrics. |
An Individualized News Affective Response Dataset (2024.acl-srw)
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| Challenge: | a new dataset captures subjective affective responses to news headlines . current methods to assess emotion detection ignore subjective differences in groups and individuals . |
| Approach: | They propose a large-scale dataset capturing subjective affective responses to news headlines . the dataset includes Facebook post screenshots from popular UK media outlets . |
| Outcome: | The proposed dataset captures subjective affective responses to headlines from popular media outlets. |
iNews: A Multimodal Dataset for Modeling Personalized Affective Responses to News (2025.acl-long)
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| Challenge: | Current approaches to modeling individual behavior ignore individual differences in how people interpret and react to identical stimuli. |
| Approach: | They propose a large-scale dataset specifically designed to facilitate the modeling of personalized affective responses to news content. |
| Outcome: | The proposed dataset includes annotations from 291 demographically diverse UK participants across 2,899 multimodal Facebook news posts from major UK outlets. |
Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage (2021.findings-emnlp)
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Isidora Tourni, Lei Guo, Taufiq Husada Daryanto, Fabian Zhafransyah, Edward Edberg Halim, Mona Jalal, Boqi Chen, Sha Lai, Hengchang Hu, Margrit Betke, Prakash Ishwar, Derry Tanti Wijaya
| Challenge: | Journalists have been using both text and images to frame news stories . lead images may carry additional background knowledge about the event . |
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| Outcome: | The study shows that combining lead images with text improves prediction of news frames . it also shows that using multiple modes of information improves frame image relevance . |
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception (2020.lrec-1)
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| Challenge: | Fewer studies address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. |
| Approach: | They propose to annotate 5000 English news headlines with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, and the reader’s perception of the emotion of the headline. |
| Outcome: | The proposed method enables further research on emotion classification, emotion intensity prediction, emotion cause detection and supports qualitative studies. |
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories (L18-1)
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| Challenge: | a new dataset is used to classify text into positive, negative, and neutral classes . a large amount of work on automatic detecting emotions from text has focused on classifying text into basic emotion categories . |
| Approach: | They use Twitter as the source of the textual data they annotate to find out which emotions often present together in tweets . |
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Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech (2025.emnlp-main)
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| Challenge: | a new study examines how users interact with LGBTQ+ news content . a corpus of 1,419,047 comments on 3,161 YouTube news videos is used to analyze the content - both positive and negative - of cable news outlets. |
| Approach: | They analyze how users interact with LGBTQ+ news content via a corpus of 1,419,047 comments on 3,161 YouTube news videos of major US cable news outlets. |
| Outcome: | The proposed classifier detects positive (hope speech), negative, neutral, and irrelevant content. |
Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events (2026.acl-long)
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Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao, Zhanpeng Jin
| Challenge: | A critical bottleneck is the lack of ground-truth human data to link personality traits to emotional shifts. |
| Approach: | They propose a large-scale dataset to capture reader-based emotional variations across news, social media, and life narratives. |
| Outcome: | The proposed model captures reader-based emotional variations across news, social media, and life narratives. |
To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence (2021.findings-emnlp)
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| Challenge: | a new study examines the media coverage of police violence in the United States by examining the framing of 82k news articles spanning 7k police killings. |
| Approach: | They propose an NLP framework to measure entity-centric framing to understand media coverage on police violence in the United States in a new police violence frames corpus of 82k news articles spanning 7k police killings. |
| Outcome: | The proposed framework reveals significant differences in the way liberal and conservative news sources frame both the issue of police violence and the entities involved. |
Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings (N19-1)
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| Challenge: | a new framework for studying political polarization in social media is needed to understand how group divisions manifest in language. |
| Approach: | They propose to cluster tweet embeddings to uncover four dimensions of political polarization in social media . their results apply existing lexical methods to analyze 4.4M tweets on 21 mass shootings . |
| Outcome: | The proposed framework generates more cohesive topics than traditional models. |