Measuring Trust in Online Social Networks : The Effects of Network Parameters on the Level of Trust in Trust Games with Incomplete Information

Abstract: The aim of this thesis is to contribute to the methodological foundation of the studies aiming to assess trust between people who interact through Computer Mediated Communication (CMC), specifically those who create a set of online relationships which is commonly called Online Social Networks. The most popular method that is currently employed by researchers in this area is Trust Game, one form of the social dilemma games. The major studies that assess trust in social networks have established results that are mainly formed into hypotheses for the effects of a number of network parameters on the extent to which individuals would place trust on each other. However, hypotheses for the effects of a few number of network parameters is not deducible since the restrictive game-theoretic assumptions that are imposed into the model do not let any such evidence available. In addition, these assumptions inhibit the analysis of the trust situations in a more realistic environment than one in which actors are instructed by the axioms of the Trust Game. One way to relax the game-theoretic assumptions so that the trust situations take place in a more realistic environment is to introduce noise into the context of information transmission. Assuming that the information is not accurately transmitted between different individuals in an online social network makes it possible to argue that the rate of information that is obtained from different sources would influence the level of trust. Here, I conduct a series of computer simulation of a model of Iterated Heterogeneous Trust Games (IHTG), developed by Buskens (1998), adding the assumptions of incomplete information on 6 network structures sampled from Youtube, to investigate the effects of Indegree and Link-strength as the influential network parameters for the noisy environments. The results of regression analysis provide that both Indegree and Link-strength have positive effects on the level of trust, while in the same situation, the positive effects of Link-Strength on trust are more promising and unyielding than those of Indegree. In addition, I argue that the current model by Buskens (1998) carries a deficiency when it is applied to the noisy environments, since it can be fooled by inactive users (i.e. those who have a very low Outdegree compared to a high Indegree) to consider them as influential on the level of trust.