A survey of recent methods on deriving topics from Twitter: algorithm to evaluation

Robertus Nugroho*, Cecile Paris, Surya Nepal, Jian Yang, Weiliang Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


In recent years, studies related to topic derivation in Twitter have gained a lot of interest from businesses and academics. The interconnection between users and information has made social media, especially Twitter, an ultimate platform for propagation of information about events in real time. Many applications require topic derivation from this social media platform. These include, for example, disaster management, outbreak detection, situation awareness, surveillance, and market analysis. Deriving topics from Twitter is challenging due to the short content of the individual posts. The environment itself is also highly dynamic. This paper presents a review of recent methods proposed to derive topics from social media platform from algorithms to evaluations. With regard to algorithms, we classify them based on the features they exploit, such as content, social interactions, and temporal aspects. In terms of evaluations, we discuss the datasets and metrics generally used to evaluate the methods. Finally, we highlight the gaps in the research this far and the problems that remain to be addressed.

Original languageEnglish
Pages (from-to)2485-2519
Number of pages35
JournalKnowledge and Information Systems
Issue number7
Early online date9 Jan 2020
Publication statusPublished - Jul 2020


  • Algorithms
  • Evaluations
  • Topic derivation
  • Twitter analysis


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