Traveling Trends: Social Butterflies or Frequent Fliers?

Abstract

Trending topics are the online conversations that grab collective attention on social media. They are continually changing and often reflect exogenous events that happen in the real world. Trends are localized in space and time as they are driven by activity in specific geographic areas that act as sources of traffic and information flow. Taken independently, trends and geography have been discussed in recent literature on online social media; although, so far, little has been done to characterize the relation between trends and geography. Here we investigate more than eleven thousand topics that trended on Twitter in 63 main US locations during a period of 50 days in 2013. This data allows us to study the origins and pathways of trends, how they compete for popularity at the local level to emerge as winners at the country level, and what dynamics underlie their production and consumption in different geographic areas. We identify two main classes of trending topics: those that surface locally, coinciding with three different geographic clusters (East coast, Midwest and Southwest); and those that emerge globally from several metropolitan areas, coinciding with the major air traffic hubs of the country. These hubs act as trendsetters, generating topics that eventually trend at the country level, and driving the conversation across the country. This poses an intriguing conjecture, drawing a parallel between the spread of information and diseases: Do trends travel faster by airplane than over the Internet?

Proceedings of the first ACM conference on Online social networks, pp. 213-222, 2013

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• An Evaluation Tool for Backbone Extraction Techniques in Weighted Complex Networks
• Latent Space Model for Multi-Modal Social Data
• Self-organization on social media: endo-exo bursts and baseline fluctuations
• Parallel clustering of high-dimensional social media data streams
• Geolocated Social Media Posts are Happier: Understanding the Characteristics of Check-in Posts on Twitter
• On predictability of rare events leveraging social media: a machine learning perspective
• Hashtag Usage in a Geographically-Local Microblogging App