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.
@article{arxiv.1912.02484,
title = {EviDense: a Graph-based Method for Finding Unique High-impact Events with Succinct Keyword-based Descriptions},
author = {Oana Balalau and Carlos Castillo and Mauro Sozio},
journal= {arXiv preprint arXiv:1912.02484},
year = {2019}
}