A Quantitative Model and Analysis of Information Confusion in Social Networks

Information consumers in online social networks receive information from multiple information providers, which results in confusion. The amount of confusion depends on three main factors-(a) attributes of the source, (b) characteristics of the consumer and (c) trust relation between the information provider and the consumer. While information confusion has been qualitatively observed in social networks, no quantitative model or analysis was presented. We present the first quantitative model to analyze confusion in the presence of multiple information providers. We address the following fundamental issues-(i) What is a good model for confusion? (ii) How does the quality of information degrade due to confusion? (iii) What are good strategies for the information providers to control the power or the intensity with which the information is transmitted? The scenario is modeled as a non-cooperative game with pricing, whose Nash equilibrium provides the solution to the questions posed above. We use data from Twitter (e.g., on full body scan in airports) and diabetes outreach networks to illustrate the analysis. We use the solution of the non-cooperative game to study the confusion levels of consumers, in terms of the aggressiveness and passiveness of the information providers. Results indicate that confusion levels are high in in networks in which all information providers are equally trusted. In networks where information providers are unequally trusted, the confusion levels are moderate.