# ContCommRTD: A Distributed Content-based Misinformation-aware Community Detection System for Real-Time Disaster Reporting

arXiv: https://arxiv.org/abs/2301.12984 · HTML: https://mlsanity.com/paper/2301.12984/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 83 references that are in the map.
- First posted on arXiv 30 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Natural Disasters Detection in Social Media and Satellite imagery: a survey](https://mlsanity.com/paper/1901.04277/) (2019)
- [Neural Topic Modeling with Continual Lifelong Learning](https://mlsanity.com/paper/2006.10909/) (2020)
- [Unsupervised Detection of Sub-events in Large Scale Disasters](https://mlsanity.com/paper/1912.13332/) (2019)
- [Density Level Set Estimation on Manifolds with DBSCAN](https://mlsanity.com/paper/1703.03503/) (2017)
- [Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach](https://mlsanity.com/paper/1505.05657/) (2015)
- [City-level Geolocation of Tweets for Real-time Visual Analytics](https://mlsanity.com/paper/1910.02213/) (2019)
- [TextBenDS: a generic Textual data Benchmark for Distributed Systems](https://mlsanity.com/paper/2108.05689/) (2021)
- [The Forgotten Document-Oriented Database Management Systems: An Overview and Benchmark of Native XML DODBMSes in Comparison with JSON DODBMSes](https://mlsanity.com/paper/2102.02246/) (2021)
- [TriggerCit: Early Flood Alerting using Twitter and Geolocation -- a comparison with alternative sources](https://mlsanity.com/paper/2202.12014/) (2022)

## Built on by (most cited first, 8 of 8)

- [Recent Advances of Multimodal Continual Learning: A Comprehensive Survey](https://mlsanity.com/paper/2410.05352/) (2024)
- [Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Data](https://mlsanity.com/paper/2412.14276/) (2024)
- [Disaster Management in the Era of Agentic AI Systems: A Vision for Collective Human-Machine Intelligence for Augmented Resilience](https://mlsanity.com/paper/2510.16034/) (2025)
- [DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection](https://mlsanity.com/paper/2302.01756/) (2023)
- [MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media](https://mlsanity.com/paper/2302.12190/) (2023)
- [An Explainable Deep Neural Network with Frequency-Aware Channel and Spatial Refinement for Flood Prediction in Sustainable Cities](https://mlsanity.com/paper/2509.08003/) (2025)
- [GETAE: Graph information Enhanced deep neural NeTwork ensemble ArchitecturE for fake news detection](https://mlsanity.com/paper/2412.01825/) (2024)
- [SDEC: Semantic Deep Embedded Clustering](https://mlsanity.com/paper/2508.15823/) (2025)

## Abstract (arXiv)

Real-time social media data can provide useful information on evolving hazards. Alongside traditional methods of disaster detection, the integration of social media data can considerably enhance disaster management. In this paper, we investigate the problem of detecting geolocation-content communities on Twitter and propose a novel distributed system that provides in near real-time information on hazard-related events and their evolution. We show that content-based community analysis leads to better and faster dissemination of reports on hazards. Our distributed disaster reporting system analyzes the social relationship among worldwide geolocated tweets, and applies topic modeling to group tweets by topics. Considering for each tweet the following information: user, timestamp, geolocation, retweets, and replies, we create a publisher-subscriber distribution model for topics. We use content similarity and the proximity of nodes to create a new model for geolocation-content based communities. Users can subscribe to different topics in specific geographical areas or worldwide and receive real-time reports regarding these topics. As misinformation can lead to increase damage if propagated in hazards related tweets, we propose a new deep learning model to detect fake news. The misinformed tweets are then removed from display. We also show empirically the scalability capabilities of the proposed system.