Prédiction de l'activité dans les réseaux sociaux. (Activity prediction in social-networks)

This dissertation is devoted to a social-media-mining problem named the activity-prediction problem. In this problem one aims to predict the number of user-generated-contents that will be created about a topic in the near future. The user-generated-contents that belong to a topic are not necessary related to each other. In order to study the activity-prediction problem without referring directly to a particular social-media, a generic framework is proposed. This generic framework allows to describe various social-media in a unified way. With this generic framework the activityprediction problem is defined independently of an actual social-media. Three examples are provided to illustrate how this generic framework describes social-media. Three definitions of the activity-prediction problem are proposed. Firstly the magnitude prediction problem defines the activity-prediction as a regression problem. With this definition one aims to predict the exact activity of a topic. Secondly, the buzz classification problem defines the activity-prediction as a binary classification problem. With this definition one aims to predict if a topic will have an activity burst of a predefined amplitude. Thirdly the rank prediction problem defines the activity-prediction as a learning-to-rank problem. With this definition one aims to rank the topics accordingly to theirs future activity-levels. These three definitions of the activity prediction problem are tackled with state-of-the-art machine learning approaches applied to generic features. Indeed, these features are defined with the help of the generic framework. Therefore these features are easily adaptable to various social-media. There are two types of features. Firstly the features which describe a single topic. Secondly the features which describe the interplay between two topics. Our ability to predict the activity is tested against an industrial-size multilingual dataset. The data has been collected during 51 weeks. Two sources of data were used: Twitter and a bulletin-board-system. The collected data contains three languages: English, French and German. More than five hundred millions user-generated-contents were captured. Most of these user-generated-contents are related to computer hardware, video games, and mobile telephony. The data collection necessitated the implementation of a daily routine. The data was prepared so that commercial-contents and technical failure are not sources of noise. A cross-validation method that takes into account the time of observations is used. In addition an unsupervised method to extract buzz candidates is proposed. Indeed the training-sets are very ill-balanced for the buzz classification problem, and it is necessary to preselect buzz candidates. The activity-prediction problems are studied within two different experimental settings. The first experimental setting includes data from Twitter and the bulletin-board-system, on a long time-scale, and with three different languages. The second experimental setting is dedicated specifically to Twitter. This second experiment aims to increase the reproducibility of experiments as much as possible. Hence, this experimental setting includes user-generated-contents collected with respect to a list of unambiguous English terms. In addition the observation are restricted to ten consecutive weeks. Hence the risk of unannounced change in the public API of Twitter is minimized.