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Variable attention and variable noise: Forecasting user activity

: Ojeda, C.; Cvejoski, K.; Sifa, R.; Bauckhage, C.

Fulltext ()

Krestel, R.:
LWDA 2016, Lernen, Wissen, Daten, Analysen : Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" Potsdam, Germany, September 12-14, 2016
Potsdam, 2016 (CEUR Workshop Proceedings 1670)
ISSN: 1613-0073
Conference "Lernen, Wissen, Daten, Analysen" (LWDA) <2016, Potsdam>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()

The study of collective attention is of growing interest in an age where mass-and social media generate massive amounts of often short lived information. That is, the problem of understanding how particular ideas, news items, or memes grow and decline in popularity has become a central problem of the information age. Recent research efforts in this regard have mainly addressed methods and models which quantify the success of such memes and track their behavior over time. Surprisingly, however, the aggregate behavior of users over various news and social media platforms where this content originates has large been ignored even though the success of memes and messages is linked to the way users interact with web platforms. In this paper, we therefore present a novel framework that allows for studying the shifts of attention of whole populations related to websites or blogs.