Metodología basada en Minería de Datos para la detección de usuarios Influencers en Twitter - Córdova Sáenz, Carlos Abel (2019)
Viral marketing is one of the techniques most used by companies to increase their reach_x000D_ and improve their pro ts. This technique is carried out through the Influencers, people who_x000D_ because of their ability to persuade very high on their followers, are in charge of viralizing_x000D_ what they want to promote. However, knowing which Influencers are the ideals for each_x000D_ target audience is not a trivial process, since determining those users is subject to a set of_x000D_ criteria beyond the simple popularity of them. One way that organizations currently use is the suscription to online Influencers detection services, however, these services have a high cost and are not very transparent to users, who do not know the criteria under which they are being determined. This thesis proposes a Methodology for the Detection of In_x000D_ uencers on Twitter,_x000D_ which follows the fundamental process of Data Mining, makes use of diverse techniques of data collection (Web Scraping, APIs) and uses a set of influence metrics to obtain a score of influence for each user, which allows achieving a ranking of the most in_x000D_ uential users for a speci c target audience. The proposed methodology includes a very basic starting point, such as the de nition of the search topics of candidate users for being Influencers, allowing any interested person to make use of it, even coupling new criteria on which to obtain the influence score. The methodology was executed on the case study: Lava Jato (Odebrecht) Peru, obtaining the ranking of Influencers based on the data collected between the months of january to march 2019