Discovery and classification of user interests on social media
| DOI | https://doi.org/10.1108/IDD-03-2017-0023 |
| Date | 21 August 2017 |
| Pages | 130-138 |
| Published date | 21 August 2017 |
| Author | Basit Shahzad,Ikramullah Lali,M. Saqib Nawaz,Waqar Aslam,Raza Mustafa,Atif Mashkoor |
| Subject Matter | Library & information science,Library & information services,Lending,Document delivery,Collection building & management,Stock revision,Consortia |
Discovery and classification of user interests
on social media
Basit Shahzad
King Saud University, Riyadh, Saudi Arabia
Ikramullah Lali
Department of Software Engineering, University of Gujrat, Gujrat, Pakistan
M. Saqib Nawaz
Department of Informatics, School of Mathematical Sciences, Peking University, Beijing, China
Waqar Aslam
Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
Raza Mustafa
Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal, Pakistan, and
Atif Mashkoor
Hagenberg GmbH, Hagenberg, Austria
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.
Keywords World wide web, Websites, SVM, Opinion, Google trends, Twitter trends
Paper type Research paper
1. Introduction
In the past decade, online communities and social media have
emerged into a forum for worldwide interpersonal
communication and sharing of data. It facilitates users for
constant and continuous information sharing, making
connections and conveying their thoughts across the world via
various media. In recent past, people are increasingly relying
on social media to access all kinds of information, news and
event. Through social media, one can create and share online
contents, participate in group activities and live events and
follow breaking news. With easy access to the internet, social
media is now affecting and facilitating nearly every aspect of
modern life, from education and technology to business and
government (Mustafa et al., 2017). Twitter is a social
networking medium that allows user to write messages
(tweets) up to 140 characters. Twitter effectively participates
in any mega events or breaking news around the world (Bollen
et al., 2011). On Twitter, users can write or share tweets that
are of interest to them. A tweet may reflect on user work, job
or a combination of various areas and seen by followers who
have an interest in it (Webberley et al., 2011). Hashtags are
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/2398-6247.htm
Information Discovery and Delivery
45/3 (2017) 130–138
© Emerald Publishing Limited [ISSN 2398-6247]
[DOI 10.1108/IDD-03-2017-0023]
Received 30 March 2017
Revised 21 June 2017
Accepted 7 July 2017
130
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