Purpose

Twitter users generated data, known as tweets, are now not only used for communication and opinion sharing, but it is 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 article, 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. This data is then pre-processed, followed by their labeling in respective categories. Data is 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 The results can be used in the development of information filtering and prediction systems especially in personalized recommendation systems.

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 our approach.