MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
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Abstract (arXiv)
We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights, including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information. Besides discovering information flow patterns among users, we construct communities among users and develop the propagation forecasting capability, enabling tracing and understanding of how information is disseminated at a higher level.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 4 Oct 2023.
Builds on
9 of 18 references.
References · 9
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2022Unified Structure Generation for Universal Information Extractioncited by 220
- 2020CoAID: COVID-19 Healthcare Misinformation Datasetcited by 83
- 2022DICE: Data-Efficient Clinical Event Extraction with Generative Modelscited by 20
- 2021EventPlus: A Temporal Event Understanding Pipelinecited by 17
- 2023Predicting Information Pathways Across Online Communitiescited by 15
- 2021Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networkscited by 3
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.
2 papers.