A novel algorithm for estimation of Twitter users location using public available information
References
- Abbas, A. K., Bayat, O. and Nuri Ucan, O. 2017. Estimation of Twitter user’s nationality based on friends and follower’s information. Computers & Electrical Engineering, 66: 517–530, doi: 10.1016/j.compeleceng.2017.06.033.
- Anber, H., Salah, A. and El-aziz, A. A. 2016. A literature review on Twitter data analysis. International Journal of Computer and Electrical Engineering 8(3): 241–249, doi: 10.17706/ijcee.2016.8.3.241-249.
- Atefeh, F. and Khreich, W. 2015. A survey of techniques for event detection in Twitter. Computational Intelligence 31: 132–164, doi: 10.1111/coin.12017.
- Bothos, E., Apostolou, D. and Mentzas, G. 2010. Using social media to predict future events with agent-based markets. IEEE Intelligent Systems 25(6): 50–58.
- Brown, D. E., Abbasi, A. and Lau, R. Y. K. 2015. Predictive analytics. IEEE Intelligent Systems 30(2): 6–8.
- Chang, H. -W., Lee, D., Eltaher, M. and Lee, J. 2012. @ Phillies Tweeting from Philly? Predicting Twitter user locations with spatial word usage. pp. 111-118, doi: 10.1109/ASONAM.2012.29.
- Culotta, A., Ravi, N. K. and Cutler, J. 2015. Predicting the demographics of Twitter users from website traffic data. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 72–78.
- De Choudhury, M., Gamon, M., Counts, S. and Horvitz, E. 2013. Predicting depression via social media. ICWSM, 13: 1–10.
- Gentry, J. 2015. R Based Twitter Client Description Provides an interface to the Twitter web API, available at: http://lists.hexdump.org/listinfo.cgi/twitter-users-hexdump.org.
- Golbeck, J., Robles, C. and Turner, K. 2011. Predicting personality with social media. Conference on Human Factors in Computing Systems – Proceedings, 253–262, doi: 10.1145/1979742.1979614.
- Hecht, B., Hong, L., Suh, B. and Chi, E. H. 2011. Tweets from Justin Bieber’s heart: the dynamics of the “Location” field in user profiles. CHI ‘11 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Vancouver, May 7–12, 237–246.
- Huang, W., Weber, I. and Vieweg, S. 2014. Inferring nationalities of Twitter users and studying inter-national linking. Proceedings of the 25th ACM Conference on Hypertext and Social Media, 237–242.
- Jayanthi, N., Babu, B. V. and Rao, N. S. 2017. Survey on clinical prediction models for diabetes prediction. Journal of Big Data, 4(26), doi: 10.1186/s40537-017-0082-7.
- Kong, L., Liu, Z. and Huang, Y. 2014. SPOT: locating social media users based on social network context. Proceedings of the VLDB Endowment 7: 1681–1684.
- Lazarus, R., Achrekar, H., Lazarus, R. and Park, W. C. 2011. Predicting flu trends using Twitter data. IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS, 702–707, doi: 10.1109/INFCOMW.2011.5928903.
- Liu, H. 2013. Some computational challenges in mining social media. 2013 IEEE/ACM International Conference in Advances in Social Networks Analysis and Mining (ASONAM), Niagara Falls, August 25-28, doi: 10.1109/ASONAM.2013.6785672.
- Mcgee, J., Caverlee, J. and Cheng, Z. 2013. Location prediction in social media based on tie strength. CIKM ‘13 Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, 459–468.
- Package based Twitter client description provides an interface to the Twitter web API. Version 1.1.8, February 20, 2015.
- Tsujioka, S., Kondo, A. and Watanabe, K. 2016. Estimation of residence information of Twitter users based on their posted messages: data for tourism development. International Journal of Research in Chemical, Metallurgical and Civil Engineering 3(1): 180–183.
- Tufekci, Z. 2014. Big questions for social media big data: representativeness, validity and other methodological pitfalls. ICWSM 14, 505–514.
- Tumasjan, A., Sprenger, T. O., Sandner, P. G. and Welpe, I. M. 2010. Predicting elections with Twitter: what 140 characters reveal about political sentiment. Conference: Proceedings of the Fourth International Conference on Weblogs and Social Media, ICWSM, Washington, DC, May 23-26, 178–185.
- Volkova, S. and Yarowsky, D. (2014), “Improving gender prediction of social media users via weighted annotator rationales”, In NIPS 2014 Workshop on Personalization, San Diego, CA, December 8–13.
- Zhang, X., Fuehres, H. and Gloor, P. A. 2011. “Predicting stock market indicators through Twitter “I hope it is not as bad as I fear”, Procedia – Social and Behavioral Sciences 26: 55–62.
DOI: https://doi.org/10.21307/ijssis-2020-012 | Journal eISSN: 1178-5608
Language: English
Page range: 1 - 10
Submitted on: Dec 10, 2019
Published on: Jul 9, 2020
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2020 Yasser Almadany, Khalid Mohammed Saffer, Ahmed K. Jameil, Saad Albawi, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.