Estimation of Twitter user's nationality based on friends and followers information. (February 2018)

• Record Type: Journal Article
• Authors: Abbas, Ahmed k.; Bayat, Oguz; Ucan, Osman Nuri
• Is Part Of: Computers & electrical engineering. Volume 66(2018)
• Page Start: 517
• Page End: 530
• Publication Date: 2018-02
• Subjects: Social media analyzing -- Location prediction -- Twitter user location -- KNIME -- Nationality prediction -- Big data analyzing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2017.06.033
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Big Data has become very useful in many fields since it provides answers to many important questions that can significantly enhance decision making and process optimization. One of the most interesting domains in big data is the prediction of human features, facts and behaviors. In this paper a new and effective algorithm to predict the nationality of Twitter users is proposed. The proposed algorithm tries to prognosticate the Twitter user's location from their friend's location information only without needing GPS information. Although only approximately 30% of Twitter users write their location information in meaningful form, this paper proves that this percentage is enough to determine the nationality of any Twitter user correctly. The proposed algorithm is applied to estimate the thresholds that will be used to determine the nationality of Twitter users. The results show that our algorithm can correctly classify an average of 90% of the Twitter users.