Twitter trending topic classification

Kathy Lee*, Diana Palsetia, Ramanathan Narayanan, Md Mostofa Ali Patwary, Ankit Agrawal, Alok Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

227 Scopus citations


With the increasing popularity of microblogging sites, we are in the era of information explosion. As of June 2011, about 200 million tweets are being generated every day. Although Twitter provides a list of most popular topics people tweet about known as Trending Topics in real time, it is often hard to understand what these trending topics are about. Therefore, it is important and necessary to classify these topics into general categories with high accuracy for better information retrieval. To address this problem, we classify Twitter Trending Topics into 18 general categories such as sports, politics, technology, etc. We experiment with 2 approaches for topic classification; (i) the well-known Bag-of-Words approach for text classification and (ii) network-based classification. In text-based classification method, we construct word vectors with trending topic definition and tweets, and the commonly used tf-idf weights are used to classify the topics using a Naive Bayes Multinomial classifier. In network-based classification method, we identify top 5 similar topics for a given topic based on the number of common influential users. The categories of the similar topics and the number of common influential users between the given topic and its similar topics are used to classify the given topic using a C5.0 decision tree learner. Experiments on a database of randomly selected 768 trending topics (over 18 classes) show that classification accuracy of up to 65% and 70% can be achieved using text-based and network-based classification modeling respectively.

Original languageEnglish (US)
Title of host publicationProceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Number of pages8
StatePublished - 2011
Event11th IEEE International Conference on Data Mining Workshops, ICDMW 2011 - Vancouver, BC, Canada
Duration: Dec 11 2011Dec 11 2011

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
CityVancouver, BC


  • Social networks
  • Topic classification
  • Twitter

ASJC Scopus subject areas

  • Engineering(all)


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