Paper Info
Title
Predicting Trends in the Twitter Social Network: A Machine Learning Approach.
Abstract
The Twitter microblogging site is one of the most popular websites in the Web today, where millions of users post real-time messages (tweets) on different topics of their interest. The content that becomes popular in Twitter (i.e., discussed by a large number of users) on a certain day can be used for a variety of purposes, including recommendation of popular content and marketing and advertisement campaigns. In this scenario, it would be of great interest to be able to predict what content will become popular topics of discussion in Twitter in the recent future. This problem is very challenging due to the inherent dynamicity in the Twitter system, where topics can become hugely popular within short intervals of time. The Twitter site periodically declares a set of trending topics, which are the keywords (e.g., hashtags) that are at the center of discussion in the Twitter network at a given point of time. However, the exact algorithm that Twitter uses to identify the trending topics at a certain time is not known publicly. In this paper, we aim to predict the keywords (hashtags) that are likely to become trending in Twitter in the recent future. We model this prediction task as a machine learning classification problem, and analyze millions of tweets from the Twitter stream to identify features for distinguishing between trending hashtags and non-trending ones. We train classifiers on features measured over one day, and use the classifiers to distinguish between trending and non-trending hashtags on the next day. The classifiers achieve very high precision and reasonably high recall in identifying the hashtags that are likely to become trending.
Year
DOI
Venue
2014
10.1007/978-3-319-20294-5_49
Lecture Notes in Computer Science
Keywords
Field
DocType
Online social network,Twitter,Trending topics,Predicting trends,Machine learning,Classification
Social media,Social network,Exact algorithm,Computer science,Microblogging,Artificial intelligence,Statistical classification,Machine learning
Conference
Volume
ISSN
Citations 
8947
0302-9743
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Anubrata Das100.68
Moumita Roy2508.71
Soumi Dutta300.68
Saptarshi Ghosh459453.82
Asit Kumar Das57316.06