Disaster analysis through tweets

Abstract

Social networks offer a wealth of information for capturing additional information on people's behavior, trends, opinions and emotions during any human-affecting events such as natural disasters. During disaster, social media provides a plethora of information which includes information about the nature of disaster, affected people's emotions and relief efforts. In this paper we propose a natural-disaster analysis interface that solely makes use of tweets generated by the Twitter users during the event of a natural disasters. We collect streaming tweets relating to disasters and build a sentiment classifier in order to categorize the users' emotions during disasters based on their various levels of distress. Various analysis techniques are applied on the collected tweets and the results are presented in the form of detailed graphical analysis which demonstrates users' emotions during a disaster, frequency distribution of various disasters and geographical distribution of disasters. We observe that our analysis of data from social media provides a viable, economical, uncensored and real-time alternative to traditional methods for disaster analysis and the perception of affected population towards a natural disaster.

Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs

“…The authors identified the intensity of five types of emotions for each subtopic over time. Reference [ 17 ] collected streaming tweets relating to disasters and built a sentiment classifier to categorize users’ emotions during disasters (earthquakes, forest fires, floods, and drought) based on their varying levels of distress. They subcategorized users’ emotions in response to different disasters as negative sentiments, unhappy, depressed, angry, and positive sentiments.…”

“…There have been several series of investigations of the information flow of social networks during disasters; these have focused on issues such as crowd psychological states [ 6 , 7 , 8 ], disaster management strategies [ 9 , 10 ], detection of natural disasters [ 11 , 12 ], and public attention to disasters [ 13 , 14 , 15 ]. Specific to public sentiment analysis, scholars have proposed crowd emotion detection solutions for natural disasters (e.g., earthquakes [ 16 ], forest fires, floods, and droughts [ 17 ]) based on microblogging contents. If we view the aforementioned research as a pointwise exploration of public emotion at a particular moment caused by short-lived disasters, this study extends the exploration from point to line and adopts a period-wise approach for this longer term disaster situation.…”

Emergency response with mobile geosocial sensing in the post‐app era

“…During disaster events, socially sensed data tend to be mostly composed of redundant, misleading, or unwanted noisy posts (Shekhar & Setty, 2015). Additionally, Twitter bots can post content automatically without human involvement to attempt to alter perceptions about the disaster event, spread misinformation or unreal help requests, or manipulate help requests.…”

Analyzing the Sentiment of Crowd for Improving the Emergency Response Services

“…SentiStory [24] proposes a system that summarizes the events after eliminating the redundancy in microblogs based on course-grained sentiment analysis and detecting the significant changes and causes of changes in the microblogs. This Research [25] present a technique for visualizing the emotional state of the people during different disaster event through the analysis of tweets. This recent study [26] has showed that change point detection is useful in identifying potential sub-events that causes the changes in sentiments of public over Twitter using the Delhi Election 2015 tweets data.…”