Real-Time Classification of Twitter Trends

  • Arkaitz Zubiaga
  • Damiano Spina
  • Raquel Martínez
  • Víctor Fresno
Journal of the Association for Information Science and Technology (JASIST), 2015

In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with the following 4 types: 'news', 'ongoing events', 'memes', and 'commemoratives'. While previous research has analyzed trending topics over the long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This allows us to provide a filtered subset of trends to end users. We experiment with a set of straightforward language-independent features based on the social spread of trends and categorize them using the typology. Our method provides an efficient way to accurately categorize trending topics without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might inform marketing decisions, among others. The analysis of social features also reveals patterns associated with each type of trend, such as tweets about ongoing events being shorter as many were likely sent from mobile devices, or memes having more retweets originating from a few trend-setters.

@article{zubiaga2014realtime,
	author = "Zubiaga, Arkaitz and Spina, Damiano and Mart{\'i}nez, Raquel and Fresno, V{\'i}ctor",
        title = {Real-time classification of Twitter trends},
        journal = {Journal of the Association for Information Science and Technology},
        volume = {66},
        number = {3},
        issn = {2330-1643},
        url = {http://dx.doi.org/10.1002/asi.23186},
        doi = {10.1002/asi.23186},
        pages = {462--473},
        keywords = {automatic classification, taxonomy, real time processing},
        year = {2015}
}
Damiano Spina
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