Foundations of statistical natural language processing
Foundations of statistical natural language processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting the Future with Social Media
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Twitter under crisis: can we trust what we RT?
Proceedings of the First Workshop on Social Media Analytics
Towards detecting influenza epidemics by analyzing Twitter messages
Proceedings of the First Workshop on Social Media Analytics
Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Twitter for crisis communication: lessons learned from Japan's tsunami disaster
International Journal of Web Based Communities
Lightweight methods to estimate influenza rates and alcohol sales volume from Twitter messages
Language Resources and Evaluation
Efficient sentiment correlation for large-scale demographics
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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We examine the response to the recent natural disaster Hurricane Irene on Twitter.com. We collect over 65,000 Twitter messages relating to Hurricane Irene from August 18th to August 31st, 2011, and group them by location and gender. We train a sentiment classifier to categorize messages based on level of concern, and then use this classifier to investigate demographic differences. We report three principal findings: (1) the number of Twitter messages related to Hurricane Irene in directly affected regions peaks around the time the hurricane hits that region; (2) the level of concern in the days leading up to the hurricane's arrival is dependent on region; and (3) the level of concern is dependent on gender, with females being more likely to express concern than males. Qualitative linguistic variations further support these differences. We conclude that social media analysis provides a viable, real-time complement to traditional survey methods for understanding public perception towards an impending disaster.