Abstract
Twitter is a well-known microblogging platform for rapid diffusion of views, ideas, and information. During disasters, it has widely been used to communicate evacuation plans, distribute calls for help, and assist in damage assessment. The reliability of such information is very important for decision-making in a crisis situation, but also difficult to assess. There is little research so far on the transferability of quality assessment methods from one geographic region to another. The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters. We examine tweeting activity during two earthquakes in Italy and Myanmar. We compare the granularity of geographic references used, user profile characteristics that are related to credibility, and the performance of Naïve Bayes models for classifying Tweets when used on data from a different region than the one used to train the model. Our results show similar geographic granularity for Myanmar and Italy earthquake events, but the Myanmar earthquake event has less information from locations nearby when compared to Italy. Additionally, there are significant and complex differences in user and usage characteristics, but a high performance for the Naïve Bayes classifier even when applied to data from a different geographic region. This research provides a basis for further research in credibility assessment of users reporting about disasters
Highlights
The growth of social media over the last decade, and its possible use as a source of information about a wide variety of topics including events, news, personal opinions, and many more (Hossmann et al 2011); (Terpstra et al 2012) is unquestionable
We found that users in Italy were more likely to have Uniform Resource Locator (URL) associated with their accounts, while there was little difference in the number of users with descriptions between the two locations. These results point to the difficulty of assessing credibility using simple measures which are not normalized for local differences, since it appears that for events of the same class in different locations we find users with very different average behaviors, implying that a globally applied credibility metric is likely to capture differences in the local properties of Twitter users rather than differences in the credibility of content at these locations
When exploring the granularity of locations reported in Tweets, an initial analysis based only on hierarchies derived from Geonames suggested that the toponyms used in Myanmar of finer granularities were more common than in Italy
Summary
The growth of social media over the last decade, and its possible use as a source of information about a wide variety of topics including events, news, personal opinions, and many more (Hossmann et al 2011); (Terpstra et al 2012) is unquestionable. Not everything shared on social media can be considered as useful and actionable information with respect to natural disasters, since people share spam, personal opinions, and material to explicitly harass other users (Senaratne et al 2017). One may “tremble” in fear, “like an avalanche,” and we may be “flooded” with information, and “fire” is used in many metaphors about emotions. This makes the adoption of methods which can analyze the semantics behind particular terms very important if we wish to categorize information harvested from social media as relevant or irrelevant pieces of information with respect to a particular class of events
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