Abstract:
Twitter, a microblogging network, allow its users to post content in real-time
according to their interest and share ideas, thoughts and information with each other. Contents
can be an image, a movie, a link to a news article or a short message known as “Tweet”.
Although Twitter provides a list of most popular topics, called Trending Topics, but users are
usually concerned about a small quantity of tweets from their own topic of interest. It is rather
challenging to predict which kind of information is expected to attract interest of more users
in such a large collection of tweets and can become more popular within short time interval.
In this study, we use the “likes” of tweet as a measurement for the popularity among the
Twitter users and study the interesting problem of Tweet Likes Count Prediction (TLCP) to
explore the characteristics for popularity of tweets for top Trending Topics in the near future.
Valuation of possible popularity is of great importance and is quite challenging. For a
particular Tweet, we measure the impact of three main attributes (Tweet Content, Number of
followers and Geographical Location) for TLCP by using prediction models and evaluate
their performance using F-measure. A real world dataset from Twitter was extracted covering
tweets from August 4, 2016 till August 21, 2016. Experimental results show that Bayesian
Network outperform 70% performance with combined features (Tweet, Followers, Location)
on likes as a best predictive model than others on the basis of Accuracy, Precision, Recall and
F-measure.
