Using social media data to map the areas most affected by ISIS in Syria

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Об авторе

Mohamad Hasan

Saint Petersburg State University, Institute of Earth Sciences, Department of Cartography and Geoinformatics,
10th line of Vasilievsky island, 31–33, 199178, St. Petersburg, Russia;


This paper presents a model to collect, save, geocode, and analyze social media data. The model is used to collect and process the social media data concerned with the ISIS terrorist group (the Islamic State in Iraq and Syria), and to map the areas in Syria most affected by ISIS accordingly to the social media data. Mapping process is assumed automated compilation of a density map for the geocoded tweets. Data mined from social media (e.g., Twitter and Facebook) is recognized as dynamic and easily accessible resources that can be used as a data source in spatial analysis and geographical information system. Social media data can be represented as a topic data and geocoding data basing on the text of the mined from social media and processed using Natural Language Processing (NLP) methods. NLP is a subdomain of artificial intelligence concerned with the programming computers to analyze natural human language and texts. NLP allows identifying words used as an initial data by developed geocoding algorithm. In this study, identifying the needed words using NLP was done using two corpora. First corpus contained the names of populated places in Syria. The second corpus was composed in result of statistical analysis of the number of tweets and picking the words that have a location meaning (i.e., schools, temples, etc.). After identifying the words, the algorithm used Google Maps geocoding API in order to obtain the coordinates for posts.

Ключ. слова

ISIS, GIS, data mining, geocoding, NLP.

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For citation: Hasan M. Using social media data to map the areas most affected by ISIS in Syria InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: Moscow University Press, 2020. V. 26. Part 1. P. 464–470. DOI: 10.35595/2414-9179-2020-1-26-464-470