EviDense: a Graph-based Method for Finding Unique High-impact Events with Succinct Keyword-based Descriptions

TL;DR

EviDense is a graph-based method that effectively identifies high-impact social media events and generates concise, relevant keyword descriptions, outperforming existing approaches by integrating social media and mainstream news data.

Contribution

The paper introduces EviDense, a novel graph-based approach for detecting high-impact events and providing succinct keyword descriptions, with improved accuracy and reduced duplicates.

Findings

Outperforms state-of-the-art methods in precision and succinctness

Reduces duplicate event detection

Enhances event descriptions by incorporating mainstream media

Abstract

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.