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Online social networks have facilitated the interaction and topic discussion. Some of
this content became a rich and important source of information and also strategical for
companies. One of the most popular of such websites is Twitter.
Nowadays, the use of digital influencers became a new strategy in the development and
in the management of marketing campaigns for leading brands and companies. Fashion
industry usually targets them to market products or to diffuse messages. In consequence,
the identification of these persons became a central issue for marketers.
In this dissertation, I propose a state of research in centrality measures, developed in
social network analysis, in order to identify those influencers. Interpretability, robustness
and accuracy, current applications and related work on Twitter will be discussed in order
to select and understand these concepts. Moreover, I propose a new technique to collect
Twitter data with a friendship graph and with a given topic. I perform this research on
fashion industry which has not been treated yet in the literature, and then, I use centrality
measures to identify the most influential users. The experimental evaluation shows that
the presence of reciprocity can be explained by phenomenon of homophily. This finding
valids the extraction process to create a sample composed of users interested and influent
in fashion topics. The application of centrality measures on the sample provides a relevant
ranking of influencers that can be used in a marketing campaign.
Keywords: Twitter, Centrality measures, Social network analysis, Degree centrality,
Closeness centrality, Betweenness centrality, Eigenvector centrality, Influencer, Network
typology, Digital influencer marketing

Sciences économiques & de gestion > Marketing