Classification of Arabic Twitter Users: A Study Based on User Behaviour and Interests

Abstract

Social networks are among the most popular interactive media today due to their simplicity and their ability to break down the barriers of community rules and their speed and because of the increasing pressures of work environments that make it more difficult for people to visit or call friends. There are many social networking products available and they are widely used for social interaction. As the amount of threading data is growing, producing analysis from this large volume of communications is becoming increasingly difficult for public and private organisations. One of the important applications of this work is to determine the trends in social networks that depend on identifying relationships between members of a community. This is not a trivial task as it has numerous challenges. Information shared between social members does not have a formal data structure but is transmitted in the form of texts, emoticons, and multimedia. The inspiration for addressing this area is that if a company is advertising a sports product, for example, it has a difficulty in identifying targeted samples of Arab people on social networks who are interested in sports. In order to accomplish this, an experiment oriented approach is adopted in this study. A goal for this company is to discover users who have been interacting with other users who have the same interests, so they can receive the same type of message or advertisement. This information will help a company to determine how to develop advertisements based on Arab people’s interests. Examples of such work include the timely advertisement of the utilities that can be effectively marketed to increase the audience; for example, on the weekend days, the effective market approaches can yield considerable results in terms of increasing the sales and profits. In addition, finding an efficient way to recommend friends to a user based on interest similarity, celebrity degree, and online behaviour is of interest to social networks themselves. This problem is explored to establish and apply an efficient and easy way to classify a social network of Arab users based on their interests using available types of information, whether textual or nontextual, and to try to increase the accuracy of interest classification. Since most of the social networking is done from the mobiles nowadays, the efficient and reliable algorithm can help in developing a robust app that can perform the tweet classification on mobile phones.