Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand. Effective use of such information requires timely selection and analysis of tweets that are relevant to a particular disaster. Even though abundant tweets are promising as a data source, it is challenging to automatically identify relevant messages since tweet are short and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets. Analysis results on eleven datasets with various disaster types show that our technique provides relevant tweets of higher quality and more interpretable results of sentiment analysis tasks when compared to learning approach.
These studies typically used machine learning tools to filter disaster-related tweets. An issue with such approaches is the lack of appropriate interpretation of the results, e.g., why a technique works well on some data (high precision and recall) but do not perform as well in others @cite_23 @cite_12 . In addition, the studies tend to examine one particular disaster and imply that the findings are generalizable to others @cite_34 . However, it is known that information shared on Twitter varies considerably from one crisis to another @cite_5 @cite_31 @cite_16 . We aim to narrow the gap by customizing each component of our framework for a particular disaster.
@cite_16 @cite_23 @cite_5 @cite_31 @cite_34 @cite_12
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The use of social media to communicate timely information during crisis situations has become a common practice in recent years. In particular, the one-to-many nature of Twitter has created an opportunity for stakeholders to disseminate crisis-relevant messages, and to access vast amounts of information they may not otherwise have. Our goal is to understand what affected populations, response agencies and other stakeholders can expect-and not expect-from these data in various types of disaster situations. Anecdotal evidence suggests that different types of crises elicit different reactions from Twitter users, but we have yet to see whether this is in fact the case. In this paper, we investigate several crises-including natural hazards and human-induced disasters-in a systematic manner and with a consistent methodology. This leads to insights about the prevalence of different information types and sources across a variety of crisis situations. A microblogging service like Twitter continues to surge in importance as a means of sharing information in social networks. In the medical domain, several works have shown the potential of detecting public health events (i.e., infectious disease outbreaks) using Twitter messages or tweets. Given its real-time nature, Twitter can enhance early outbreak warning for public health authorities in order that a rapid response can take place. Most of previous works on detecting outbreaks in Twitter simply analyze tweets matched disease names and or locations of interests. However, the effectiveness of such method is limited for two main reasons. First, disease names are highly ambiguous, i.e., referring slangs or non health-related contexts. Second, the characteristics of infectious diseases are highly dynamic in time and place, namely, strongly time-dependent and vary greatly among different regions. In this paper, we propose to analyze the temporal diversity of tweets during the known periods of real-world outbreaks in order to gain insight into a temporary focus on specific events. More precisely, our objective is to understand whether the temporal diversity of tweets can be used as indicators of outbreak events, and to which extent. We employ an efficient algorithm based on sampling to compute the diversity statistics of tweets at particular time. To this end, we conduct experiments by correlating temporal diversity with the estimated event magnitude of 14 real-world outbreak events manually created as ground truth. Our analysis shows that correlation results are diverse among different outbreaks, which can reflect the characteristics (severity and duration) of outbreaks. Keywords: Crisis Informatics ; Social Media Collection ; Social Media Analysis Reference EPFL-CONF-203561 Record created on 2014-11-26, modified on 2017-05-12 In this paper, we introduce Tweedr, a Twitter-mining tool that extracts actionable information for disaster relief workers during natural disasters. The Tweedr pipeline consists of three main parts: classification, clustering and extraction. In the classification phase, we use a variety of classification methods (sLDA, SVM, and logistic regression) to identify tweets reporting damage or casualties. In the clustering phase, we use filters to merge tweets that are similar to one another; and finally, in the extraction phase, we extract tokens and phrases that report specific information about different classes of infrastructure damage, damage types, and casualties. We empirically validate our approach with tweets collected from 12 different crises in the United States since 2006.