Micro influencers detector from marketing parameters to Semantic Analysis

Micro influencers detector from marketing parameters to Semantic Analysis We perform an analysis on Twitter social network users, following economics parametrization of special candidates considered micro influencers. Micro influencers are people with a community based on 1k to 20k followers, which have high reputation in a certain topic, a sort of guru, having an extremely high engagement power on other users in the community and that with their posts on social network can push their followers to action. From these understandings, we retrieved tweets and other parameter of user considered potential micro influencers, analyzed their marketing parameters and then performed semantic analysis to derive their big5 [1] and basic human values [2] from tweets text scan through machine learning approach. We perform a social and psychological investigation over these areas. We understand how info’s are propagated in a community by which agents. We inspect the cascading propagation behavior [3]. We determine if a user examined as described before can be defined as micro influencers and then we validate our hypothesis through cross validation and use of Support Vector Machine over the Semantic Values excluding marketing parameters. The research is effective, and we demonstrate how human values and personality trait are correlated with the influencing effect. On one side, we have created a tool for companies to find and contact potential evangelists of their message and brand, and on the other side new job opportunities for social network users that can now monetize their passions. [1] https://en.wikipedia.org/wiki/Big_Five_personality_traits [2] https://en.wikipedia.org/wiki/Theory_of_Basic_Human_Values [3] “Networks, Crowds, and Markets: Reasoning about a Highly Connected World”, Easley and Kleinberg