Discovery and classification of user interests on social media

B. Shahzad, I. Lali, M. Nawaz, W. Aslam, R. Mustafa. Discovery and classification of user interests on social media. Information Discovery and Delivery, volume 45, number 3, pages 130-138, DOI 10.1108/IDD-03-2017-0023, 11, 2017.

Autoren
  • Basit Shahzad
  • Ikramullah Lali
  • M. Saqib Nawaz
  • Waqar Aslam
  • Raza Mustafa
TypArtikel
JournalInformation Discovery and Delivery
Nummer3
Band45
DOI10.1108/IDD-03-2017-0023
ISSN2398-6247
Monat11
Jahr2017
Seiten130-138
Abstract
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.