Discovery and classification of user interests on social media

Abstract

Purpose

Twitter users’ generated data, known as tweets, are now not only used for communication and opinion sharing, but they are considered an important source of trendsetting, future prediction, recommendation systems and marketing. Using network features in tweet modeling and applying data mining and deep learning techniques on tweets is gaining more and more interest.

Design/methodology/approach

In this paper, user interests are discovered from Twitter Trends using a modeling approach that uses network-based text data (tweets). First, the popular trends are collected and stored in separate documents. These data are then pre-processed, followed by their labeling in respective categories. Data are then modeled and user interest for each Trending topic is calculated by considering positive tweets in that trend, average retweet and favorite count.

Findings

The proposed approach can be used to infer users’ topics of interest on Twitter and to categorize them. Support vector machine can be used for training and validation purposes. Positive tweets can be further analyzed to find user posting patterns. There is a positive correlation between tweets and Google data.

Practical implications

The results can be used in the development of information filtering and prediction systems, especially in personalized recommendation systems.

Social implications

Twitter microblogging platform offers content posting and sharing to billions of internet users worldwide. Therefore, this work has significant socioeconomic impacts.

Originality/value

This study guides on how Twitter network structure features can be exploited in discovering user interests using tweets. Further, positive correlation of Twitter Trends with Google Trends is reported, which validates the correctness of the authors’ approach.

Citation

Shahzad, B., Lali, I., Nawaz, M.S., Aslam, W., Mustafa, R. and Mashkoor, A. (2017), "Discovery and classification of user interests on social media", Information Discovery and Delivery, Vol. 45 No. 3, pp. 130-138. https://doi.org/10.1108/IDD-03-2017-0023