Abstract. Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.
Centrality and influence spread are two of the most studied concepts in social network analysis. In recent years, centrality measures have attracted the attention of many researchers, generating a large and varied number of new studies about social network analysis and its applications. However, as far as we know, traditional models of influence spread have not yet been exhaustively used to define centrality measures according to the influence criteria. Most of the considered work in this topic is based on the independent cascade model. In this paper we explore the possibilities of the linear threshold model for the definition of centrality measures to be used on weighted and labeled social networks. We propose a new centrality measure to rank the users of the network, the Linear Threshold Rank (LTR), and a centralization measure to determine to what extent the entire network has a centralized structure, the Linear Threshold Centralization (LTC). We appraise the viability of the approach through several case studies. We consider four different social networks to compare our new measures with two centrality measures based on relevance criteria and another centrality measure based on the independent cascade model. Our results show that our measures are useful for ranking actors and networks in a distinguishable way.Peer ReviewedPostprint (author's final draft
A key problem in social network analysis is identifying influential users within a social network. To address this problem, numerous centrality measures have been defined to automatically state rankings of the users. In this article, we define the MilestonesRank, a new measure to detect opinion leaders, an important type of influential users focused on specific topics. This measure considers two parameters that can be freely adjusted depending on the needs of the analyst, namely, the interest and the exclusivity of the users regarding some specific topic. Every topic is bounded by a list of milestones over a period of time of several weeks or even months. We compare this measure with other classic measures to find opinion leaders in a real case study using the Twitter network. Our experiments show that the new measure allows us to find relevant opinion leaders that other measures are not able to detect.
Using centrality measures to improve the classification performance of tweets during natural disastersUsando medidas de centralidad para mejorar la clasificación de tweets durante desastres naturales
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