EviDense: a Graph-based Method for Finding Unique High-impact Events with Succinct Keyword-based Descriptions
This addresses the challenge of extracting valuable event information from social media for applications like disaster response, though it appears incremental as it builds on existing methods.
The paper tackles the problem of automatically identifying high-impact events like disasters from social media by presenting EviDense, a graph-based method that provides succinct keyword-based descriptions including what, where, and when. The evaluation on a large tweet dataset over 19 months shows it outperforms state-of-the-art approaches with higher precision, fewer duplicates, and more informative descriptions, and results are further improved by incorporating news from mainstream media.
Despite the significant efforts made by the research community in recent years, automatically acquiring valuable information about high impact-events from social media remains challenging. We present EviDense, a graph-based approach for finding high-impact events (such as disaster events) in social media. One of the challenges we address in our work is to provide for each event a succinct keyword-based description, containing the most relevant information about it, such as what happened, the location, as well as its timeframe. We evaluate our approach on a large collection of tweets posted over a period of 19 months, using a crowdsourcing platform. Our evaluation shows that our method outperforms state-of-the-art approaches for the same problem, in terms of having higher precision, lower number of duplicates, and presenting a keyword-based description that is succinct and informative. We further improve the results of our algorithm by incorporating news from mainstream media. A preliminary version of this work was presented as a 4-pages short paper at ICWSM 2018.