Querying Social Media to Track People’s Movements and Discussions Via a Browser Interface


  • Hanan A. Tahir Department of Information Technology Management, Duhok Polytechnic University, Kurdistan Region of Iraq
  • Hoger Mahmud Department of Computer Science, University of Human Development, Sulaymaniyah, Kurdistan Region of Iraq




Social networking is micro-blogging site where users post short text-based messages. These messages often relate to a particular topic, event or product and are frequently coupled with the authors’ opinion. This information provides an exciting opportunity for individuals and companies who might benefit from being able to collate and analyze the data to discover opinion on an item of interest. This paper reports on the outcome of a project that aims to produce a web page that queries the social Web (specifically Twitter.com and Foursquare.com.) through the available APIs to track people’s movements and the topic of their discussions.  Tweets will be the main data source and queries results will be ranked and sorted out by total frequency in a Web accessible MySQL database. The result shows that the system has been successful in achieving the set aims.


Download data is not yet available.


1. Drews, F.A., Pasupathi, M., & Strayer, D.L. (2008). Passenger and cell phone conversations in simulated driving. Journal of Experimental Psychology: Applied, 14, 392-400. doi: 10.1037/a0013119
2. AOuth Authorisation Framework. (n.d.). Retrieved 15–5, 2019, from https://oauth.net/
3. Asur, S. & Huberman, B. A. (2010). Predicting the future with social media. In 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (Vol. 1, pp. 492–499).
4. Batrinca, B. & Treleaven, P. C. (2015). Social media analytics: a survey of techniques, tools and platforms. Ai \& Society, 30(1), 89–116.
5. Chang, J. & Sun, E. (2011). Location3: How users share and respond to location-based data on social. In Fifth International AAAI conference on weblogs and social media.
6. Derczynski, L. R., Yang, B. & Jensen, C. S. (2013). Towards context-aware search and analysis on social media data. In Proceedings of the 16th international conference on extending database technology (pp. 137–142).
7. Hasan, S. & Ukkusuri, S. V. (2015). Location contexts of user check-ins to model urban geo life-style patterns. PloS One, 10(5).
8. Hecht, B., Hong, L., Suh, B. & Chi, E. H. (2011). Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 237–246).
9. Jansen, B. J., Zhang, M., Sobel, K. & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169–2188.
10. Kaplan, A. M. & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59–68.
11. Kwak, H., Lee, C., Park, H. & Moon, S. (2010). What is Twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web (pp. 591–600).
12. Manasa, K. & Padma, M. (2019). A Study on Sentiment Analysis on Social Media Data. In Emerging Research in Electronics, Computer Science and Technology (pp. 661–667). Springer.
13. Maynard, D., Bontcheva, K. & Rout, D. (2012). Challenges in developing opinion mining tools for social media. Proceedings of The@ NLP Can U Tag\# Usergeneratedcontent, 15–22.
14. McClellan, C., Ali, M. M., Mutter, R., Kroutil, L. & Landwehr, J. (2017). Using social media to monitor mental health discussions- evidence from Twitter. Journal of the American Medical Informatics Association, 24(3), 496–502.
15. Phillips, L., Dowling, C., Shaffer, K., Hodas, N. & Volkova, S. (2017). Using social media to predict the future: a systematic literature review. arXiv Preprint arXiv:1706.06134.
16. Rashidi, T. H., Abbasi, A., Maghrebi, M., Hasan, S. & Waller, T. S. (2017). Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges. Transportation Research Part C: Emerging Technologies, 75, 197–211.
17. Ratkiewicz, J., Conover, M. D., Meiss, M., Gonçalves, B., Flammini, A. & Menczer, F. M. (2011). Detecting and tracking political abuse in social media. In Fifth international AAAI conference on weblogs and social media.
18. Sadilek, A., Kautz, H. & Silenzio, V. (2012). Modeling spread of disease from social interactions. In Sixth International AAAI Conference on Weblogs and Social Media.
19. Saumya, S., Singh, J. P. & Kumar, P. (2016). Predicting stock movements using social network. In Conference on e-Business, e-Services and e-Society (pp. 567–572).
20. Spyratos, S., Vespe, M., Natale, F., Weber, I., Zagheni, E. & Rango, M. (2018). Migration data using social media: a European perspective.
21. Zhu, Q. (2017). Citizen-driven international networks and globalization of social movements on Twitter. Social Science Computer Review, 35(1), 68–83.



How to Cite

Tahir, H. A., & Mahmud, H. (2020). Querying Social Media to Track People’s Movements and Discussions Via a Browser Interface. Academic Journal of Nawroz University, 9(3), 102–113. https://doi.org/10.25007/ajnu.v9n3a743




Most read articles by the same author(s)