Real‐time classification of Twitter trends
提出一种基于社交传播特征的分类方法，将Twitter趋势实时分为新闻、持续事件、迷因和纪念四类，帮助新闻机构发现突发新闻或识别病毒式传播内容。
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.