ContCommRTD: A Distributed Content-based Misinformation-aware Community Detection System for Real-Time Disaster Reporting
This addresses the need for faster and more reliable disaster management by emergency responders and the public, though it is incremental as it builds on existing social media analysis methods.
The paper tackles the problem of detecting geolocation-content communities on Twitter for real-time disaster reporting, proposing a distributed system that improves report dissemination speed and accuracy by integrating topic modeling and a deep learning model to filter misinformation.
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.