Twitter is the most popular real-time micro-blogging service and it is a platform where users provide and obtain information at rapid pace. In this scenario, one of the biggest challenge is to find a way to automatically identify the most influential users of a given topic. Currently, there are several approaches that try to address this challenge using different Twitter signals (e.g., number of followers, lists, metadata), but results are not clear and sometimes conflicting. In this paper, we propose TRank, a novel method designed to address the problem of identifying the most influential Twitter users on specific topics identified with hashtags. The novelty of our approach is that it combines different Twitter signals (that represent both the user and the user's tweets) to provide three different indicators that are intended to capture different aspects of being influent. The computation of these indicators is not based on the magnitude of the Twitter signals alone, but they are computed taking into consideration also human factors, as for example the fact that a user with many active followings might have a very noisy time lime and, thus, miss to read many tweets. The experimental assessment confirms that our approach provides results that are more reasonable than the one obtained by mechanisms based on the sole magnitude of data.

TRank: Ranking Twitter Users According to Specific Topics / Furini, Marco; Montangero, Manuela. - STAMPA. - (2015), pp. 767-772. (Intervento presentato al convegno 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC 2015) tenutosi a Las Vegas, Nevada, USA nel 9-12 January 2015) [10.1109/CCNC.2015.7158074].

TRank: Ranking Twitter Users According to Specific Topics

FURINI, Marco;MONTANGERO, Manuela
2015

Abstract

Twitter is the most popular real-time micro-blogging service and it is a platform where users provide and obtain information at rapid pace. In this scenario, one of the biggest challenge is to find a way to automatically identify the most influential users of a given topic. Currently, there are several approaches that try to address this challenge using different Twitter signals (e.g., number of followers, lists, metadata), but results are not clear and sometimes conflicting. In this paper, we propose TRank, a novel method designed to address the problem of identifying the most influential Twitter users on specific topics identified with hashtags. The novelty of our approach is that it combines different Twitter signals (that represent both the user and the user's tweets) to provide three different indicators that are intended to capture different aspects of being influent. The computation of these indicators is not based on the magnitude of the Twitter signals alone, but they are computed taking into consideration also human factors, as for example the fact that a user with many active followings might have a very noisy time lime and, thus, miss to read many tweets. The experimental assessment confirms that our approach provides results that are more reasonable than the one obtained by mechanisms based on the sole magnitude of data.
2015
2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC 2015)
Las Vegas, Nevada, USA
9-12 January 2015
767
772
Furini, Marco; Montangero, Manuela
TRank: Ranking Twitter Users According to Specific Topics / Furini, Marco; Montangero, Manuela. - STAMPA. - (2015), pp. 767-772. (Intervento presentato al convegno 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC 2015) tenutosi a Las Vegas, Nevada, USA nel 9-12 January 2015) [10.1109/CCNC.2015.7158074].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1072988
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