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The 2015 Middle East respiratory syndrome (MERS) outbreak in South Korea gave rise to chaos caused by psychological anxiety, and it has been assumed that people shared rumors about hospital lists through social media. Sharing rumors is a common form of public perception and risk communication among individuals during an outbreak. Social media analysis offers an important window into the spatiotemporal patterns of public perception and risk communication about disease outbreaks. Such processes of socially mediated risk communication are a process of meme diffusion. This article aims to investigate the role of social media meme diffusion and its spatiotemporal patterns in public perception and risk communication. To do so, we applied analytical methods including the daily number of tweets for metropolitan cities and geovisualization with the weighted mean centers. The spatiotemporal patterns shown by Twitter users' interests in specific places, triggered by real space events, demonstrate the spatial interactions among places in public perception and risk communication. Public perception and risk communication about places are relevant to both social networks and spatial proximity to where Twitter users live and are interpreted in reference to both Zipf's law and Tobler's law. [ABSTRACT FROM AUTHOR]

A sleeping beauty in diffusion indicates that certain information, whether an idea or innovation, will experience a hibernation period before it undergoes a sudden spike of popularity, and this pattern is found widely in the citation history of scientific publications. However, in this study, we demonstrate that the sleeping beauty is an interesting and unexceptional phenomenon in information diffusion; more inspiring is that there exists two consecutive sleeping beauties in the entire lifetime of a meme's propagation, which suggests that the information, including scientific topics, search queries or Wikipedia entries, which we call memes, will go unnoticed for a period and suddenly attract some attention, and then it falls asleep again and later wakes up with another unexpected popularity peak. Further exploration of this phenomenon shows that the intervals between two wake-ups follow an exponential distributions, both the rising and falling stage lengths, follow power law distributions, and the second wake-up tends to reach its peak in a shorter period of time. In addition, the total volumes of the two wake-ups have positive correlations. Taking these findings into consideration, an upgraded Bass model is presented to well describe the diffusion dynamics of memes on different media. Our results can help understand the common mechanism behind the propagation of different memes and are instructive towards locating the tipping point in marketing or in finding innovative publications in science. [ABSTRACT FROM AUTHOR]

The study of social phenomena is becoming increasingly reliant on big data from online social networks. Broad access to social media data, however, requires software development skills that not all researchers possess. Here we present the IUNI Observatory on Social Media, an open analytics platform designed to facilitate computational social science. The system leverages a historical, ongoing collection of over 70 billion public messages from Twitter. We illustrate a number of interactive open-source tools to retrieve, visualize, and analyze derived data from this collection. The Observatory, now available at osome.iuni.iu.edu, is the result of a large, six-year collaborative effort coordinated by the Indiana University Network Science Institute.