Finding Eyewitness Tweets During Crises

TL;DR

This paper explores methods to identify non-geotagged tweets originating from crisis regions by analyzing linguistic patterns, aiming to improve real-time situational awareness for disaster response agencies.

Contribution

It introduces a novel approach to classify non-geotagged tweets as within-region or outside-region during crises using linguistic analysis.

Findings

Language differs between within-region and outside-region tweets during crises.

Linguistic patterns can effectively distinguish the origin of tweets.

Proposed methods enable real-time identification of crisis-region tweets.

Abstract

Disaster response agencies have started to incorporate social media as a source of fast-breaking information to understand the needs of people affected by the many crises that occur around the world. These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region. However only 1% of all tweets are geotagged with explicit location information. First responders lose valuable information because they cannot assess the origin of many of the tweets they collect. In this work we seek to identify non-geotagged tweets that originate from within the crisis region. Towards this, we address three questions: (1) is there a difference between the language of tweets originating within a crisis region and tweets originating outside the region, (2) what are the linguistic patterns that can be used to differentiate within-region and outside-region tweets, and (3) for non-geotagged tweets, can we automatically identify those originating within the crisis region in real-time?