Abstract
With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented. In longitudinal datasets across various domains, covering multiple decades, we find increasing gradients and shortened periods in the trajectories of how cultural items receive collective attention. Is this the inevitable conclusion of the way information is disseminated and consumed? Our findings support this hypothesis. Using a simple mathematical model of topics competing for finite collective attention, we are able to explain the empirical data remarkably well. Our modeling suggests that the accelerating ups and downs of popular content are driven by increasing production and consumption of content, resulting in a more rapid exhaustion of limited attention resources. In the interplay with competition for novelty, this causes growing turnover rates and individual topics receiving shorter intervals of collective attention.
Highlights
With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented
By that we focus on one dimension of social acceleration, the increasing rates of change within collective attention[10]
Across the different domains under investigation, we find clear empirical evidence of ever steeper gradients and shorter intervals of collective attention given to each cultural item
Summary
With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented. Online media enable analysis with higher temporal granularity through Google Trends (2010–2018), Reddit (2010–2015), and Wikipedia (2012–2017) (see Supplementary Table 1) We use these dynamical measurements of categorized content as proxies for the amount of collective attention a topic receives. We use the term collective attention in a sense that is closely related to the computational social science notion[11,12,13], that measures various forms of population-level content consumption patterns[14,15,16] This is distinct from the definition of attention used in cognitive psychology[17,18], which describes the selective allocation of cognitive resource on an individual level. By studying a variety of social domains, we emphasize the general nature of the observed development across both, domains and over time
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