The PGNSC Benchmark: How Do We Predict Where Information Spreads? (2024.findings-acl)
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| Challenge: | Social networks have become ideal vehicles for news dissemination because posted content is easily able to reach users beyond a news outlet’s direct audience. |
| Approach: | They propose a benchmark that builds information pathways based on the audiences of influential news sources and uses their content to characterize the communities. |
| Outcome: | The proposed benchmark builds information pathways based on the audiences of influential news sources and uses their content to characterize the communities. |
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| Challenge: | a gap in computational work to support the "Miss Havisham is dead" "She died" research focuses on the representation of social networks in literature . |
| Approach: | They propose a pipeline for measuring information propagation in literature . they analyze the dynamics of information propagations in over 5,000 works of fiction . |
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MicroC-KT: Modeling Community Effect via Learning Micro-Environment for Evidence-Grounded Explainable Knowledge Tracing (2026.acl-long)
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| Challenge: | Existing graph-based methods focus on exercise-concept relations, but lack the broader context of group references and contrastive evidence. |
| Approach: | They propose a framework that incorporates learning micro-environments to provide social-cognitive anchors for KT by extracting contrastive group evidence. |
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Identifying Informational Sources in News Articles (2023.emnlp-main)
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| Challenge: | Identifying sources of information in news articles is relevant to many tasks in NLP, including misinformation detection and argumentation. |
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Tracking the Newsworthiness of Public Documents (2024.acl-long)
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| Challenge: | a new method to model news coverage of local government is needed . we show that newsworthiness predictions can be useful for journalists seeking to keep abreast of local governments. |
| Approach: | They propose a method that explicitly models when and why stories get press attention . they use an annotated corpus of news articles to build models that predict if a policy item will get covered . |
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The Engage Corpus: A Social Media Dataset for Text-Based Recommender Systems (2022.lrec-1)
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| Challenge: | Existing studies have examined the impact of recommendation algorithms on how users discover and join online groups, but there are few standardized datasets for generating such models. |
| Approach: | They propose to use Reddit to build a dataset that can be used to build models of user engagement with online groups. |
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The Remarkable Benefit of User-Level Aggregation for Lexical-based Population-Level Predictions (D18-1)
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Salvatore Giorgi, Daniel Preoţiuc-Pietro, Anneke Buffone, Daniel Rieman, Lyle Ungar, H. Andrew Schwartz
| Challenge: | Social media data is often aggregated without regard to users in the Twitter populations of each community. |
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GREENER: Graph Neural Networks for News Media Profiling (2022.emnlp-main)
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| Challenge: | a new method for profiling news media on the Web addresses the factuality of reporting and bias problem . a recent study has focused on text features but has focused primarily on text . |
| Approach: | They propose a model that models the similarity between media outlets based on their audience overlap . they propose GREENER, which builds a graph of inter-media connections based upon audience overlap. |
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Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations (D18-1)
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| Challenge: | Existing methods for summarizing online conversations require large amounts of training data. |
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Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (P19-1)
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| Challenge: | Existing knowledge graphs (KGs) are incomplete or partial information, in the form of missing relations between entities, which gives rise to the task of knowledge base completion (also known as relation prediction). |
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Predicting Factuality of Reporting and Bias of News Media Sources (D18-1)
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| Challenge: | a new study examines the factuality of news media and its biases . social media has democratized content creation and spread information online . |
| Approach: | They propose to characterize entire news media to predict factuality and bias . they experiment with news websites and a set of features derived from their content . |
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