A considerable part of social network analysis literature is dedicated to determining which individuals are to be considered as influential in particular social settings. Concretely, Social Influence can be described as the power or even the ability of a person to yet influence the thoughts as well as the actions of other users. So, User Influence stands as a value that depends on the interest of the followers of a concrete user (via retweets, replies, mentions, favorites, etc.). This paper focuses on identifying such phenomena on the Twitter graph and on presenting a novel methodology for characterizing Twitter Influential Users. The novelty of our approach lies in the fact that we have incorporated a set of features for characterizing social media authors, including both nodal and topical metrics, along with new features concerning temporal aspects of user participation on the topic. We have also implemented cluster-based fusion techniques in order to retrieve result lists for the ranking of top influential users. Hence, results show that the proposed implementations and methodology can assist in identifying influential users, that play a dominant role in information diffusion.

中文翻译：

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使用基于集群的融合方法估计 Twitter 有影响力的用户

相当一部分社交网络分析文献致力于确定哪些个人在特定的社会环境中被认为具有影响力。具体来说，社会影响力可以描述为一个人影响其他用户的思想和行为的力量甚至能力。因此，用户影响力的价值取决于具体用户的追随者的兴趣（通过转发、回复、提及、收藏等）。本文的重点是识别 Twitter 图上的此类现象，并提出一种表征 Twitter 有影响力用户的新方法。我们方法的新颖之处在于，我们结合了一组特征来表征社交媒体作者，包括节点和主题指标，以及有关用户参与该主题的时间方面的新功能。我们还实施了基于集群的融合技术，以检索结果列表以对最有影响力的用户进行排名。因此，结果表明，所提出的实施和方法可以帮助识别有影响力的用户，这些用户在信息传播中起主导作用。