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

https://doi.org/10.35595/2414-9179-2020-1-26-464-470

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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;
E-mail: mo-hasan89@hotmail.com

Аннотация

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.

Список литературы

  1. Alexander D.E. Social media in disaster risk reduction and crisis management. Science and Engineering, 2014. V. 20. No 3. P. 717–733.
  2. Alharith A., Samak Y. Fighting terrorism more effectively with the aid of GIS: Kingdom of Saudi Arabia case study. American Journal of Geographic Information System, 2018. V. 7 No 1. P. 15–31.
  3. Barbier G., Goolsby R., Gao H. Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intelligent Systems, 2011. V. 26. No 3. P. 10–14.
  4. Goldberg D.W., Wilson J.P., Knoblock C.A. From text to geographic coordinates: the current state of geocoding. Journal of Spatial Information Science, 2014. V. 9. P. 37–70.
  5. Golubovic N., Krintz C., Wolski R., Lafia S., Hervey T., Kuhn W. Extracting spatial information from social media in support of agricultural management decisions. GIR’16. Proceedings of the 10th Workshop on Geographic Information Retrieval, October 2016. 2017. Article No 4. P. 1–2.
  6. Goodchild M., Glennon A. Crowdsourcing geographic information for disaster response: A research frontier. International Journal of Digital Earth, 2010. V. 3. No 3. P. 231–241.
  7. Hasan M., Panidi E., Badenko V. Comparative evaluation of NoSQL and relational databases performance while analyzing semi-structured geospatial data. 5th International Scientific Conference GEOBALCANICA 2019. 2019. P. 541–549. DOI: 10.18509/GBP.2019.64.
  8. Hay S.I., George D.B., Moyes C.L., Brownstein J.S. Big data opportunities for global infectious disease surveillance. PLoS Medicine. 2013. V. 10, No 4. Article No e1001413.
  9. Hecht B., Hong L., Suh B. Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. 29th Annual CHI Conference on Human Factors in Computing Systems, 2011. P. 237–246.
  10. Hill L. Core elements of digital gazetteers: placenames, categories, and footprints. Research and Advanced Technology for Digital Libraries, 2000. V. 1923. P. 280–290.
  11. Huang Q., Xiao Y. Geographic situational awareness: mining tweets for disaster preparedness, emergency response, impact, and recovery. ISPRS International Journal of Geo-Information, 2015. V. 4. No 3. P. 1549–1568.
  12. Jin F., Wang W., Chakraborty P., Self N., Chen F., Ramakrishnan N. Tracking multiple social media for stock market event prediction. Advances in Data Mining. Applications and Theoretical Aspects, 2017. V. 10357. P. 16–30.
  13. Kaplan A.M., Haenlein M. Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 2010. V. 53. No 1. P. 59–68. DOI: 10.1016/j.bushor.2009.09.003.
  14. Ming-Hsiang T. Research challenges and opportunities in mapping social media and big data. Cartography and Geographic Information Science, 2015. V. 42. No 1. P. 70–74.
  15. Sakaki T., Okazaki M. Earthquake shakes twitter users: Real-time event detection by social sensors. Proceedings of the Nineteenth International Conference on WWW, 2010. P. 851–860.
  16. Simon T., Goldberg A., Adini B. Socializing in emergencies, a review of the use of social media in emergency situations. International Journal of Information Management, 2015. V. 35. No 1. P. 609–619.
  17. Soulis K., Varlamis I., Giannakoulopoulos A., Charatsev F. A tool for the visualization of public opinion. International Journal of Electronic Governance, 2013. V. 6. No 3. P. 218–231.
  18. Wang S., Hu H., Lin T., Liu Y., Padmanabhan A., Soltani K. CyberGIS for data-intensive knowledge discovery. SIGSPATIAL Special, 2014. V. 6. No 2. P. 26–33.

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