Exploring the Role of Context to Distinguish Rhetorical and Information-Seeking Questions (2020.acl-srw)
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| Challenge: | Social media posts often contain questions, but many of them are rhetorical and do not seek information. |
| Approach: | They propose a dataset containing questions in tweets paired with their prior tweets to provide context . they find that prior tweet and topic features can improve performance on this task . |
| Outcome: | The proposed dataset compares questions in tweets with their prior tweets to provide context . it shows that prior tweet and topic features can improve performance on this task . |
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| Challenge: | Existing datasets do not contain many rhetorical questions that can be rhetorical or informational depending on context. |
| Approach: | They propose a dataset explicitly constructed to support the study of rhetorical ambiguity . they evaluate the performance of state-of-the-art language models on the dataset . |
| Outcome: | The proposed dataset shows that state-of-the-art language models struggle to recognize rhetorical questions. |
Rhetorical Questions in LLM Representations: A Linear Probing Study (2026.acl-long)
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| Challenge: | Rhetorical questions are asked not to seek information, but to persuade or signal stance . how large language models internally represent rhetorical questions remains unclear . |
| Approach: | They analyze rhetorical questions in LLM representations using linear probes on two social-media datasets with different discourse contexts. |
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Context in Informational Bias Detection (2020.coling-main)
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| Challenge: | Informational bias is conveyed through sentences or clauses that provide tangential, speculative or background information that can sway readers’ opinions towards entities. |
| Approach: | They explore four kinds of context for informational bias in English news articles . integrating event context improves classification performance over a strong baseline . |
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Exploring Author Context for Detecting Intended vs Perceived Sarcasm (P19-1)
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| Challenge: | Existing studies on textual sarcasm detection use manual labelling and tag-based distant supervision to detect sarcasm. |
| Approach: | They define author context as the embedded representation of their historical tweets and suggest neural models that extract these representations. |
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The Power of Framing: How News Headlines Guide Search Behavior (2025.findings-emnlp)
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| Challenge: | Framing effects on judgment are well documented, but their impact on subsequent search behavior is less understood. |
| Approach: | They conducted a controlled experiment where participants issued queries and selected headlines filtered by specific linguistic frames. |
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Exploring the Usability of Persuasion Techniques for Downstream Misinformation-related Classification Tasks (2024.lrec-main)
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| Challenge: | systematically explore the predictive power of features derived from Persuasion Techniques detected in texts for different tasks of interest for media analysis. |
| Approach: | They propose a set of meaningful features aimed at capturing persuasiveness of a text . they also assess the discriminatory power of these features in different text classification tasks . |
| Outcome: | The proposed features can be applied to detecting mis/disinformation, fake news, propaganda, partisan news and conspiracy theories. |
Context versus Prior Knowledge in Language Models (2024.acl-long)
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| Challenge: | Existing studies have investigated how often a model will rely on prior knowledge over conflicting contextual information in answering questions. |
| Approach: | They propose two mutual information-based metrics to measure a model’s dependency on a context and on its prior about an entity. |
| Outcome: | The proposed metrics show that language models can integrate prior knowledge and new information in a predictable way across different questions and contexts. |
“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. |
An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events (2020.findings-emnlp)
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| Challenge: | Social media platforms such as Twitter contain a vast amount of information about the general public’s needs. |
| Approach: | They propose to use Twitter to extract a list of needed resources and detecting sentences that specify who-needs-what resources. |
| Outcome: | The proposed methods achieve 0.64 precision on a set of 1,000 annotated tweets and achieve 0.68 F1-score. |
#HowYouTagTweets: Learning User Hashtagging Preferences via Personalized Topic Attention (2021.emnlp-main)
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| Challenge: | Existing methods based on latent topics cannot capture user interests and thus can't be used to predict how likely a user will post with a hashtag. |
| Approach: | They propose a personalized topic attention model that captures salient contents to personalize hashtag contexts by predicting how likely a user will post with a hashtag. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art recommendation approach without exploiting latent topics. |