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Journal Articles International Journal of Social Network Mining Year : 2016

A study of different keyword activity prediction problems in social media

Abstract

Forecasting keyword activities in social networking sites has been the subject of many studies, as such activities represent, in many cases, a direct estimate of the spread of real-world phenomena, e.g. box-office revenues or flu epidemic. Most of these studies rely on point-wise, regression-like prediction algorithms and focus on few, usually unambiguous, keywords. We study in this paper the impact of keyword activity on three different problems: a) classification of keywords according to the increase of their activity in the near future; b) prediction of the activity value of each keyword in the near future; c) ranking of a set of keywords according to their future activity values. It is the first time, to our knowledge, that such dimensions are evaluated in this framework. Our experiments are conducted on a large dataset built by monitoring Twitter over a year. The different methods tested are evaluated using standard scores as well as a newly defined, application driven quality measure.
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Dates and versions

hal-01579048 , version 1 (30-08-2017)

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François Kawala, Éric Gaussier, Ahlame Douzal Chouakria, Eustache Diemert. A study of different keyword activity prediction problems in social media. International Journal of Social Network Mining, 2016, 2 (3), pp.224-255. ⟨10.1504/IJSNM.2016.082642⟩. ⟨hal-01579048⟩
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