Apprentissage d'ordonnancement et influence de l'ambiguïté pour la prédiction d'activité sur les réseaux sociaux

Abstract : Forecasting keyword/topic activities in social networking sites has been the subject of many recent studies, as such activities represent, in many cases, a direct estimate of the spreadth of real-world phenomena, ıt eg. box-office revenues or flu epidemies. Most of these studies rely on pointwise, regression-like prediction algorithms and focus on few, usually unambiguous, keywords/topics. We study different strategies to rank keyword activities through a comparison of pointwise and pairwise learning to rank approaches, as well as the impact of keyword ambiguity, keyword activity and keyword set size on the prediction results. 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 and including 1497 keywords.
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Communication dans un congrès
Coria'2014, Mar 2014, Nancy, France, France. pp.1-15, 2014
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https://hal.archives-ouvertes.fr/hal-00946968
Contributeur : Maria-Irina Nicolae <>
Soumis le : vendredi 14 février 2014 - 14:31:32
Dernière modification le : mardi 28 octobre 2014 - 18:35:02

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  • HAL Id : hal-00946968, version 1

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François Kawala, Eric Gaussier, Ahlame Douzal-Chouakria, Eustache Diemert. Apprentissage d'ordonnancement et influence de l'ambiguïté pour la prédiction d'activité sur les réseaux sociaux. Coria'2014, Mar 2014, Nancy, France, France. pp.1-15, 2014. 〈hal-00946968〉

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