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01-12-2021 | Original Article | Issue 1/2021

Social Network Analysis and Mining 1/2021

Integrating location and textual information for detecting affected people in a crisis

Journal:
Social Network Analysis and Mining > Issue 1/2021
Authors:
Esraa Karam, Wedad Hussein, Tarek F. Gharib
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Abstract

Social networks have become a major source of news with millions of people writing and trading news daily. At the occurrence of any event, news spread faster on social networks compared to other news sources, which makes them a good source for event identification. The most common approach for event detection from social networks is text analysis.In the case of a crisis, social media plays an important role in understanding the situation at the location of the crisis as the information is received directly from the affected people. If the collected information is used effectively this can minimize casualties and help in providing basic needs and medical attention. In this paper, we propose a hybrid approach that combines text analysis techniques and location identification to efficiently detect affected people. The addition of location information is done for the purpose of filtering out users writing about the crisis without being affected. The experimental results on Twitter data showed that the combination of text analysis and location yielded an accuracy of 96% as compared to 87% when using text analysis only.

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