EWASS 2017 Special Session SS13 "Relativity at 100"
Prague, 28 June 2017
EPJ Data Science Highlight - Estimating unemployment rates from Twitter user routines
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- Published on Wednesday, 12 July 2017 17:00
The buzz of busy commuters, as well as the lack of it, leave behind digital footprints that are rich in information about all aspects of people's lives. In EPJ Data Science, Eszter Bokányi and team analyze 63 million tweets originating all over the US for a period of 10 months, and find links between unemployment rates and and the users' Twitter activity.
EPJ Data Science Highlight - Social media trending: real or manufactured?
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- Published on Tuesday, 11 July 2017 18:16
The era of "fake news" is upon us. Navigating social media is a constant exercise of judgement, but data science can be a helpful to distinguish real from fabricated trending topics. In EPJ Data Science, Emilio Ferrara and team set out to determine from very early on whether information is being organically or artificially disseminated on social media.
EPJ B Highlight - Between extinction and survival of endangered populations
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- Published on Tuesday, 11 July 2017 18:00
Lifetime simulation of biological populations reveals dramatic population fluctuations before extinction
Populations of endangered species reach a critical point in their life where they either survive or evolve towards extinction. Therefore, efforts to predict and even prevent the extinction of biological species require a thorough understanding of the underlying mechanisms. In a new study published in EPJ B, Hatem Barghathi and colleagues from Missouri University of Science and Technology, USA, have investigated how environmental disturbance at random times could cause strong fluctuations in the number of individuals in biological populations. This, in turn, makes extinction easier, even for large populations. They found that environmental disorder can lead to a period of slow population increase interrupted by sudden population collapses. These findings also have implications for solving the opposite problem when attempting to predict, control and eradicate population of viruses in epidemics.
