Twitter, Facebook, and other related systems that we call social awareness streams are rapidly changing the information and communication dynamics of our society. These systems, where hundreds of millions of users share short messages in real time, expose the aggregate interests and attention of global and local communities. In particular, emerging temporal trends in these systems, especially those related to a single geographic area, are a significant and revealing source of information for, and about, a local community. This study makes two essential contributions for interpreting emerging temporal trends in these information systems. First, based on a large dataset of Twitter messages from one geographic area, we develop a taxonomy of the trends present in the data. Second, we identify important dimensions according to which trends can be categorized, as well as the key distinguishing features of trends that can be derived from their associated messages. We quantitatively examine the computed features for different categories of trends, and establish that significant differences can be detected across categories. Our study advances the understanding of trends on Twitter and other social awareness streams, which will enable powerful applications and activities, including user-driven real-time information services for local communities.
IntroductionIn recent years, a class of communication and information platforms we call social awareness streams (SAS) has been shifting the manner in which we consume and produce information. Available from social media services such as Facebook, Twitter, FriendFeed, and others, these hugely popular networks allow participants to post streams of lightweight content artifacts, from short status messages to links, pictures, and videos. These SAS platforms have already Received July 30, 2010; revised December 20, 2010; accepted December 21, 2010 © 2011 ASIS&T • Published online 7 March 2011 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/asi.21489 shown considerable impact on the information, communication, and media infrastructure of our society (Johnson, 2009), as evidenced during major global events such as the Iran election or the reaction to the earthquake in Haiti (Kwak, Lee, Park, & Moon, 2010), as well as in response to local events and emergencies (Shklovski, Palen, & Sutton, 2008;Starbird, Palen, Hughes, & Vieweg, 2010).SAS allow for rapid, immediate sharing of information aimed at known contacts or the general public. The content of the often-public shared items ranges from personal status updates to opinions and information sharing (Naaman, Boase, & Lai, 2010). In aggregate, however, the postings by hundreds of millions of users of Facebook, Twitter, and other systems expose global interests, happenings, and attitudes in almost real time (Kwak et al., 2010).These interests and happenings as reflected in SAS data change rapidly. The strong temporal nature of SAS information allows for the detection of significant events and other temporal trends ...