Visualizing Social Media Sentiment in Disaster Scenarios

Abstract

Recently, social media, such as Twitter, has been successfully used as a proxy to gauge the impacts of disasters in real time. However, most previous analyses of social media during disaster response focus on the magnitude and location of social media discussion. In this work, we explore the impact that disasters have on the underlying sentiment of social media streams. During disasters, people may assume negative sentiments discussing lives lost and property damage, other people may assume encouraging responses to inspire and spread hope. Our goal is to explore the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment. In this paper, we propose a novel visual analytics framework for sentiment visualization of geo-located Twitter data. The proposed framework consists of two components, sentiment modeling and geographic visualization. In particular, we provide an entropybased metric to model sentiment contained in social media data. The extracted sentiment is further integrated into a visualization framework to explore the uncertainty of public opinion. We explored Ebola Twitter dataset to show how visual analytics techniques and sentiment modeling can reveal interesting patterns in disaster scenarios.

On Identifying Disaster-Related Tweets: Matching-Based or Learning-Based?

“…However, 1 https://github.com/infolab-usc/bdr-tweet this problem is challenging as the social media content are often short and unstructured [11], [12]. There has been a growing body of work addressing such challenges [5], [20]. In [5], sentiment classification of twitter messages during Hurricane Sandy was performed and the extracted sentiments were visualized on a geographical map centered around the hurricane.…”

Forecasting People’s Needs in Hurricane Events from Social Network

“…In the work of [5], they identified fifteen distinct disaster social media uses including but not limited to preparing and receiving disaster preparedness information and warnings, signaling and detecting disasters before an event, and even (re)connecting community members following a disaster. [16] explored the underlying trends in positive and negative sentiment with respect to disasters and geographically related sentiment using Twitter data. Assessment of disaster damage was also investigated at [17].…”

The State of the Art in Sentiment Visualization

“…The authors address the task of uncertainty visualization with stream graphs: stacked layers encode the volumes for several sentiment classification certainty levels over time. The previous work from the same authors [LHW*15] also uses blurred map glyphs to represent uncertainty of Twitter data polarity analysis for disaster scenarios.…”