Real-Time Classification of Twitter Trends

TL;DR

This paper presents a real-time, language-independent method for classifying Twitter trends into four categories using social spread features, enabling timely identification of news, memes, and events.

Contribution

The work introduces a novel typology for Twitter trend triggers and a real-time classification approach based on social features without external data.

Findings

Accurate early categorization of trends into four types

Identification of social patterns associated with each trend type

Efficient, language-independent trend classification method

Abstract

Social media users give rise to social trends as they share about common interests, which can be triggered by different reasons. In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with following four types: 'news', 'ongoing events', 'memes', and 'commemoratives'. While previous research has analyzed trending topics in a long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This would allow to provide a filtered subset of trends to end users. We analyze and experiment with a set of straightforward language-independent features based on the social spread of trends to categorize them into the introduced 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 enrich 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.