# MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways

arXiv: https://arxiv.org/abs/2310.02529 · HTML: https://mlsanity.com/paper/2310.02529/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 9 of 9)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [DICE: Data-Efficient Clinical Event Extraction with Generative Models](https://mlsanity.com/paper/2208.07989/) (2022)
- [EventPlus: A Temporal Event Understanding Pipeline](https://mlsanity.com/paper/2101.04922/) (2021)
- [Predicting Information Pathways Across Online Communities](https://mlsanity.com/paper/2306.02259/) (2023)
- [Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks](https://mlsanity.com/paper/2109.09190/) (2021)

## Built on by (most cited first, 2 of 2)

- [Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach](https://mlsanity.com/paper/2311.09630/) (2023)
- [BAR-Analytics: A Web-based Platform for Analyzing Information Spreading Barriers in News: Comparative Analysis Across Multiple Barriers and Events](https://mlsanity.com/paper/2503.24220/) (2025)

## 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.