Open AccessKnow your customer from Twitter contacts: automatic discrimination of peers contacts from news sources
Author(s) -
A. Munar,
Esteban Chiner
Publication year - 2016
Language(s) - English
Resource type - Conference proceedings
DOI - 10.4995/carma2016.2016.3597
Subject(s) - influencer marketing , computer science , closeness , social media , relation (database) , social network (sociolinguistics) , customer relationship management , context (archaeology) , ranking (information retrieval) , dimension (graph theory) , world wide web , service (business) , data science , data mining , information retrieval , business , relationship marketing , database , marketing , mathematical analysis , paleontology , mathematics , biology , pure mathematics , marketing management
Know your customer is a core element of any customer relationship management system for mass service organizations. The emergence of social networking services has provided a radically new dimension, creating a more personalized, deeper, ubiquitous and almost real time relation with customers. At the same time, some of the more widespread social network platforms seem to be evolving not only as social networks between individuals but also as mass information distribution media. When knowing your customer through social networking services, it may be of interest to disambiguate which part of the customer context in the network relates to his peers from other sources. In this paper we present an algorithmic approach to disambiguate one aspect of such relation, as expressed in the nature of the contacts established in the social network: with peers or with organizations, news media or influencers. We focus in the case of Twitter where a simple supervised linear regression can provide a ranking score, effectively discriminating and ordering by closeness peer and other types of contacts (mass media or influencers). Such discrimination can serve as a preliminary step for deeper analysis or privacy protection of customer interaction and is suitable for implementation in automated Big Data systems.