Papers by Dan Goldwasser
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding (2025.findings-emnlp)
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| Challenge: | EmoGist is a training-free, in-context learning method for visual emotion classification . context-dependent definitions of emotion labels could allow more accurate predictions of emotions . |
| Approach: | They introduce EmoGist, a training-free, in-context learning method for performing visual emotion classification with LVLMs. |
| Outcome: | The proposed method improves micro F1 scores and macro F1 with LVLMs. |
Modeling Human Mental States with an Entity-based Narrative Graph (2021.naacl-main)
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| Challenge: | Understanding narrative text requires capturing characters’ motivations, goals, and mental states. |
| Approach: | They propose an Entity-based Narrative Graph (ENG) to model the internal-states of characters in a story and evaluate it on two narrative understanding tasks. |
| Outcome: | The proposed model is based on two narrative understanding tasks: predicting character mental states, and desire fulfillment. |
Using RL to Identify Divisive Perspectives Improves LLMs Abilities to Identify Communities on Social Media (2024.findings-emnlp)
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| Challenge: | Experimental results show improvements on Reddit and Twitter data . |
| Approach: | They propose to take advantage of Large Language Models (LLMs) to better identify user communities. |
| Outcome: | The proposed model improves on Reddit and Twitter data and tasks of community detection, bot detection, and news media profiling. |
TAIGR: Towards Modeling Influencer Content on Social Media via Structured, Pragmatic Inference (2026.acl-long)
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| Challenge: | Health influencers are often conveyed through conversational narratives and rhetorical strategies rather than explicit factual claims. |
| Approach: | They propose a framework to analyze influencer discourse using takeaway argumentation inference with Grounded References. |
| Outcome: | The proposed framework is based on a content validation task over influencer video transcripts on health, showing that accurate validation requires modeling the discourse’s pragmatic and argumentative structure rather than treating transcripts as flat collections of claims. |
Cross-Lingual Document Retrieval with Smooth Learning (2020.coling-main)
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| Challenge: | Cross-lingual document search is an information retrieval task in which the queries’ language and the documents’ language are different. |
| Approach: | They propose a robust framework that measures the relevance and a loss function that is a novel objective function. |
| Outcome: | The proposed framework achieves significant gains under commonly used ranking metrics on cross-lingual document retrieval task in a variety of languages. |
“We Demand Justice!”: Towards Social Context Grounding of Political Texts (2024.emnlp-main)
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| Challenge: | Political discourse on social media often contains similar language with opposing intended meanings. |
| Approach: | They propose to characterize the social context required to fully understand political discourse . structured models outperform larger models on both tasks, but still lag behind human performance . |
| Outcome: | The proposed models outperform larger models on both tasks but lag behind human performance. |
Understanding the Language of Political Agreement and Disagreement in Legislative Texts (2020.acl-main)
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| Challenge: | Despite the fact that state-level legislation is rarely discussed, it has a dramatic influence on the everyday life of residents of the respective states. |
| Approach: | They propose a large-scale dataset linking state bills and legislator information, geographical information about their districts, and donations and donors’ information. |
| Outcome: | The proposed model improves over strong text-based models by integrating the state-level text and the legislative context. |
Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media (2020.emnlp-main)
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| Challenge: | a new study suggests a minimally supervised approach for identifying nuanced political frames in news articles on politically divisive topics. |
| Approach: | They propose a minimally supervised approach for identifying nuanced policy frames in news coverage of politically divisive topics. |
| Outcome: | The proposed subframes can capture differences in political ideology better . the proposed frameworks were tested on immigration, gun control and abortion topics . |
SOLAR: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offs (2025.emnlp-main)
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| Challenge: | Existing studies suggest that Large Language Models can account for individual-level subjectivity, yet exploring whether LLMs can generate perspectives and reasoning that align well with a specific persona or demographic information has not been adequately studied. |
| Approach: | They propose a framework that observes value conflicts and trade-offs in user-generated texts to better represent subjective ground of individuals. |
| Outcome: | The proposed framework improves inference performance for users with limited data and in controversial situations. |
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate (2022.naacl-main)
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Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, Dan Goldwasser
| Challenge: | Covid-19 infodemic has led to low quality information leading to poor health decisions . authors propose a framework for analyzing false claims and reasoning about the decisions a person makes . |
| Approach: | They propose a framework linking stance and reason analysis and moral sentiment analysis. |
| Outcome: | The proposed framework provides reliable predictions even in low-supervision settings. |
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks (2022.acl-long)
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| Challenge: | Social media has enabled the propagation of fake news, text published by news sources with an intent to spread misinformation and sway beliefs. |
| Approach: | They propose to use inference operators to analyze social media for fake news spread to uncover unobserved interactions between documents and users' engagement patterns. |
| Outcome: | The proposed algorithms improve the performance of two fake news detection tasks. |
Modeling Content and Context with Deep Relational Learning (2021.tacl-1)
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| Challenge: | Existing frameworks for combining neural and symbolic representations are limited to simple relational learning tasks. |
| Approach: | They propose a declarative framework for specifying deep relational models that integrates expressive language encoders and provides an interface to study the interactions between representation, inference and learning. |
| Outcome: | The proposed framework integrates with expressive language encoders and provides an interface to study the interactions between representation, inference and learning. |
Understanding Politics via Contextualized Discourse Processing (2021.emnlp-main)
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| Challenge: | Recent advances in pretrained language models do not capture nuanced biases in political discourse . a new approach to represent political content is to use contextualized embeddings to create effective representations . |
| Approach: | They propose a model that captures and leverages political content to generate more effective representations . they use tweets, press releases, issues, news articles and participating entities to generate composed representations. |
| Outcome: | The proposed model generates representations for political entities over multiple issues or events . qualitative and quantitative analysis shows that the model is meaningful and effective . |
Talking Point based Ideological Discourse Analysis in News Events (2025.findings-acl)
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Nishanth Sridhar Nakshatri, Nikhil Mehta, Siyi Liu, Sihao Chen, Daniel Hopkins, Dan Roth, Dan Goldwasser
| Challenge: | Existing models of ideological discourse analysis fail to capture the key elements that shape real-world narratives and lack the ability to integrate contextual information required for understanding abstract ideological views. |
| Approach: | They propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events. |
| Outcome: | The proposed framework can generate ideology-specific viewpoints (partisan perspectives) it can be used to generate event snapshots, a visual way of interpreting event discourse. |
“where is this relationship going?”: Understanding Relationship Trajectories in Narrative Text (2020.starsem-1)
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| Challenge: | Existing models of human social relationships lack the syntactic and semantic signals needed to comprehend complex relationships. |
| Approach: | They propose two commonsense reasoning tasks to help people understand relationships . they use a dataset of 1250 stories documenting daily social interactions . |
| Outcome: | The proposed evaluation tasks parallel two real-life questions people frequently ponder upon as they navigate different social situations. |
Hands-On Interactive Neuro-Symbolic NLP with DRaiL (2022.emnlp-demos)
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| Challenge: | Existing methods to enhance and correct NLP models require feedback from users. |
| Approach: | They propose to enhance DRaiL with an easy to use Python interface that allows users to define, modify and augment models, as well as debug and visualize the predictions. |
| Outcome: | The proposed framework supports predicting sentence and entity level moral sentiment in political tweets. |
Using LLM for Improving Key Event Discovery: Temporal-Guided News Stream Clustering with Event Summaries (2023.findings-emnlp)
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| Challenge: | Using hierarchical Dirichlet processes, we characterize news articles associated with key events from news streams. |
| Approach: | They propose a generic framework for news stream clustering that analyzes the temporal trend of news articles to automatically extract the underlying key news events that draw significant media attention. |
| Outcome: | The proposed framework produces more coherent clusters based on event summaries . the proposed framework is a first step in a new field of news analysis . |
ConceptCarve: Dynamic Realization of Evidence (2025.acl-long)
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| Challenge: | ConceptCarve is a social media-based evidence retrieval framework that enables users to analyze complex thought patterns that manifest differently across social media communities. |
| Approach: | They propose ConceptCarve, an evidence retrieval framework that utilizes traditional retrieval systems and LLMs to dynamically characterize the search space during retrieval. |
| Outcome: | The proposed framework surpasses traditional retrieval systems in finding evidence within a social media community and produces an interpretable representation of the evidence for that community. |
Analysis of State-Level Legislative Process in Enhanced Linguistic and Nationwide Network Contexts (2024.naacl-long)
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| Challenge: | a new framework for understanding state-level legislative process improves understanding of state legislation and its implications. |
| Approach: | They propose to use generative large language models to decode legislators' behavior and implications of state policies by establishing a shared nationwide network. |
| Outcome: | The framework decodes legislators’ behavior and implications of state policies by establishing a shared nationwide network enriched with diverse contexts, such as information on interest groups influencing public policy and legislators' courage test results, which reflect their political positions. |
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections (2023.findings-acl)
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| Challenge: | Topic modeling is a popular method for identifying emerging themes from text collections. |
| Approach: | They propose a framework that receives and encodes expert feedback at different levels of abstraction. |
| Outcome: | The proposed framework combines automation and manual coding, allowing experts to maintain control while reducing the manual effort required. |
Using Social and Linguistic Information to Adapt Pretrained Representations for Political Perspective Identification (2021.findings-acl)
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| Challenge: | a new framework for political perspective detection is proposed to improve text training costs . current deep learning models lack the ability to focus on text span for bias detection . |
| Approach: | They propose a framework that pretrains the text model using social and linguistic contexts . they demonstrate that the framework improves performance by identifying bias-related text spans based on entity mentions and news sharing . |
| Outcome: | The proposed framework improves on two news bias datasets and improves performance on the general source and task. |
Classification of Moral Foundations in Microblog Political Discourse (P18-1)
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| Challenge: | a recent study shows correlation between political ideologies and moral foundations expressed in text . a moral foundation theory suggests that there are five basic moral values which underlie human moral perspectives . |
| Approach: | They propose to model the moral foundations of tweets by using an annotation framework . they propose to use policy frames to predict the morality of political tweets . |
| Outcome: | The proposed model can predict moral foundations of political tweets, the authors show . their model can be used to predict political slogans and political ideologies, they say . |
Towards Explaining Subjective Ground of Individuals on Social Media (2022.findings-emnlp)
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| Challenge: | Large-scale language models have been reducing the gap between machines and humans in understanding how the real world works, yet understanding an individual’s perspective and behavior from text is far from being resolved. |
| Approach: | They propose a neural model that learns subjective grounds of individuals and accounts for their judgments on situations posted on social media. |
| Outcome: | The proposed model provides human-readable explanations of an individual’s subjective preference in judging social situations and also learns an individual's orientation towards abstract moral concepts. |
Structured Representation Learning for Online Debate Stance Prediction (C18-1)
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| Challenge: | Existing models for understanding debate dialog ignore relationships between different topics and focus on textual content and user interaction. |
| Approach: | They propose to view this task as a representation learning problem and embed the text and authors jointly based on their interactions. |
| Outcome: | The proposed model can achieve significantly better results compared to competing models. |
LLM-Human Pipeline for Cultural Grounding of Conversations (2025.naacl-long)
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| Challenge: | addressing parents by name is commonplace in the West, but it is rare in most Asian cultures. |
| Approach: | They propose a Cultural Context Schema for conversations that incorporates conversational information and cultural information such as social norms, violations, etc. |
| Outcome: | The proposed model significantly improves the empirical performance of a Chinese conversational norm and violation description using an interactive human-in-loop framework. |
Encoding Social Information with Graph Convolutional Networks forPolitical Perspective Detection in News Media (P19-1)
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| Challenge: | Identifying the political perspective shaping the way news events are discussed in the media is an important and challenging task. |
| Approach: | They propose a neural architecture for representing relational information to capture social context of news documents. |
| Outcome: | The proposed model performs better than supervised models in the supervised setting and shows that it provides a distant supervision signal. |
Using Natural Language Relations between Answer Choices for Machine Comprehension (N19-1)
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| Challenge: | Current approaches to the reading comprehension task quantify the relationship between each question and answer choice independently and pick the highest scoring option. |
| Approach: | They propose a method to leverage natural language relations between answer choices to improve machine comprehension. |
| Outcome: | The proposed model improves the performance of a reading comprehension task by leveraging natural language relations between answer choices. |
Post-hoc Study of Climate Microtargeting on Social Media Ads with LLMs: Thematic Insights and Fairness Evaluation (2025.findings-emnlp)
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| Challenge: | Climate change communication on social media increasingly employs microtargeting strategies to effectively reach and influence specific demographic groups. |
| Approach: | They analyze social media ads using large language models to examine their performance . they find that LLMs perform well overall, but certain biases exist . |
| Outcome: | The results show that LLMs perform well overall, but certain biases exist in certain demographic groups. |
An Interactive Framework for Profiling News Media Sources (2024.naacl-long)
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| Challenge: | Existing tools for detecting fake news are difficult for automated systems . e.g., we focus on the source level, and ask: Is this source factual or politically biased? |
| Approach: | They propose an interactive framework for news media profiling that uses graphs and pre-trained large language models to characterize social context on social media. |
| Outcome: | The proposed framework can detect fake and biased news media with as little as 5 human interactions . it can scale better, as often sources publish have same factuality/political bias as source . |
Modeling U.S. State-Level Policies by Extracting Winners and Losers from Legislative Texts (2022.acl-long)
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| Challenge: | State-level legislation is the cornerstone of national policies and has long-lasting effects on residents of US states. |
| Approach: | They build a dataset for multiple US states that interconnects multiple sources of data including bills, stakeholders, legislators, and money donors. |
| Outcome: | The proposed model predicts winners/losers of bills and then utilizes them to determine the legislative body’s vote breakdown according to demographic/ideological criteria, e.g., gender. |
Identifying Power Relations in Conversations using Multi-Agent Social Reasoning (2025.naacl-short)
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| Challenge: | Existing approaches to understanding power relationships in conversations are based on task-specific supervised learning. |
| Approach: | They propose a multi-agent social reasoning framework that leverages social science tools to generate and evaluate reasons from multiple perspectives and construct a factor graph for inference. |
| Outcome: | The proposed framework outperforms standard prompting baselines on power dynamics in conversations. |
Multi-Relational Script Learning for Discourse Relations (P19-1)
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| Challenge: | Existing script knowledge models only represent a single event relationship, co-occurrence . this is coarse for commonsense, which should account for fine-grained relationships . |
| Approach: | They propose to view learning event embedding as a multi-relational problem . they model a rich set of event relations derived from the Penn Discourse Tree Bank . |
| Outcome: | The proposed model captures different aspects of event pairs, including cause and contrast. |
VIBE: Can a VLM Read the Room? (2025.findings-emnlp)
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| Challenge: | Vision Language Models (LLMs) cannot account for the role that non-verbal cues play in understanding social situations. |
| Approach: | They propose a task to test the capabilities of Vision Language Models (VLMs) to account for the visual social-pragmatic inference gap. |
| Outcome: | The proposed task tests the capabilities of a VLM for a social reasoning task. |
Iterative Dual-Model Alignment for Story Evaluation (2026.acl-long)
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| Challenge: | Existing evaluators of large language models are static and lack the ability to refine their reasoning through interaction. |
| Approach: | They propose an Alpha–Beta Learning framework that trains two complementary 8B models: an Alpha classifier that assesses pairwise story engagement, and a Beta generator that produces structured, rubric-guided comparative explanations. |
| Outcome: | The proposed framework outperforms strong single-model baselines on human-annotated story-pair datasets in both accuracy and explanation quality across multiple iterative rounds. |
Weakly-Supervised Modeling of Contextualized Event Embedding for Discourse Relations (2020.findings-emnlp)
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| Challenge: | Structured knowledge representations capture temporal relations between events to describe human-level representations of common scenarios. |
| Approach: | They propose to represent narrative graphs and learn contextualized event representations over them using a relational graph neural network model. |
| Outcome: | The proposed model improves performance when learning script knowledge without supervision and provides a better representation for the implicit discourse sense classification task. |
Towards Understanding Counseling Conversations: Domain Knowledge and Large Language Models (2024.findings-eacl)
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| Challenge: | Existing language models such as Transformer-based models fail to predict the conversation outcome. |
| Approach: | They propose to integrate human-annotated domain knowledge and LLM-generated features to provide richer context to counseling conversations. |
| Outcome: | The proposed model improves by 15% when combined with human-annotated domain knowledge and LLM-generated features. |
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy (2025.findings-naacl)
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| Challenge: | Supervised methods are adept at text categorization, but dynamic nature of social media debates pose challenges for them . traditional methods for extracting themes from public discourse often reveal overarching patterns that might not capture specific nuances. |
| Approach: | They propose a generic approach that leverages the advanced capabilities of Large Language Models to extract latent arguments from social media messaging. |
| Outcome: | The proposed approach leverages the advanced capabilities of Large Language Models (LLMs) to extract latent arguments from social media messaging. |
“A Tale of Two Movements’: Identifying and Comparing Perspectives in #BlackLivesMatter and #BlueLivesMatter Movements-related Tweets using Weakly Supervised Graph-based Structured Prediction (2023.findings-emnlp)
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| Challenge: | a weakly supervised graph-based approach to model #BLM-related tweets is difficult to obtain . |
| Approach: | They propose a weakly supervised graph-based approach that explicitly models perspectives in #BackLivesMatter-related tweets. |
| Outcome: | The proposed model outperforms multitask baselines by a large margin. |
Predicting Stance Change Using Modular Architectures (2020.coling-main)
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| Challenge: | Existing studies on argumentative text in isolation have shown that ideological stances are highly correlated with different moral arguments preferences. |
| Approach: | They propose a modular learning approach which decomposes the task into multiple modules and focuses on different aspects of the interaction between users, their beliefs, and the arguments they are exposed to. |
| Outcome: | The proposed approach archives significantly better results over the end-to-end approach using BERT over the same inputs. |
Sentiment Tagging with Partial Labels using Modular Architectures (P19-1)
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| Challenge: | Many NLP learning tasks can be decomposed into sub-tasks, each associated with a partial label. |
| Approach: | They propose a modular learning approach where sub-tasks are learned using functional modules . they propose 'learning with partial labels' approach that decomposes tasks into partial labels . |
| Outcome: | The proposed approach can simplify learning and reduce supervision efforts. |
Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)
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| Challenge: | Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts . |
| Approach: | They propose to leverage deep structured prediction and expressive neural encoders for argumentation mining tasks. |
| Outcome: | The proposed framework can be used for argumentation mining tasks without expensive inference tools. |
Splits! Flexible Sociocultural Linguistic Investigation at Scale (2026.acl-long)
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| Challenge: | Variation in language use offers a rich lens into cultural perspectives, values, and opinions. |
| Approach: | They propose to construct a "sandbox" for systematic and flexible sociolinguistic research by splitting a reddit dataset into demographically/topically split SLPs. |
| Outcome: | The proposed method analyzes a demographically/topically split Reddit dataset validated by self-identification and replicating several known SLPs from existing literature. |
Improving Natural Language Interaction with Robots Using Advice (N19-1)
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| Challenge: | Recent studies focus on learning models for physically grounded language understanding tasks such as the blocks world domain. |
| Approach: | They propose a protocol for including advice, high-level observations about the task, which can help constrain the agent’s prediction. |
| Outcome: | The proposed approach can be extended to include advice, high-level observations about the task, and reduce the effort involved in supplying the advice. |
Semi-supervised Autoencoding Projective Dependency Parsing (2020.coling-main)
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| Challenge: | Existing models for semi-supervised dependency parsing use labeled data, but they require large amounts of labeles. |
| Approach: | They propose two end-to-end autoencoding models for semi-supervised graph-based projective dependency parsing. |
| Outcome: | The proposed models outperform a semi-supervised model on WSJ and UD dependency parsing data sets. |
Identifying Morality Frames in Political Tweets using Relational Learning (2021.emnlp-main)
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| Challenge: | Moral sentiment is often motivated by its targets, which can correspond to individuals or collective entities. |
| Approach: | They propose a model to predict moral attitudes towards entities and moral foundations jointly using tweets written by US politicians. |
| Outcome: | The proposed model predicts moral attitudes towards entities and moral foundations jointly from tweets written by US politicians. |