Bullshit in a network structure: the two-sided influence of self-generated signals.

Abstract

In today’s social network age, information flowing in networks does not derive solely from external sources; people in the network also independently generate signals. These self-generated signals may not be deliberate lies, but they may not bear any relationship with the truth, either. Following the philosopher Harry G. Frankfurt, we refer to such self-generated signals as bullshit. We present an information diffusion model that allows nodes which hold no value to spread information, capturing the diffusion of bullshit information. The presence of self-generated signals (i.e., bullshit) increases the amount of information available for transmission in the network. However, participants in the spread process respond to the existence of such self-generated information by receiving data from internal sources with caution. These two contradictory forces—the increase in information transmission on the one hand, and in suspicion on the other—result in a two-sided effect of bullshit on the total spread time. We first take a numerical approach, simulating our model on Watts–Strogatz networks and building a decision tree to characterize the effects of bullshit given different network structures. We find that increasing the rate of self-generated information may have either a monotonic or non-monotonic effect on the rumor spread time, depending on the network structure and rate of non-self-generated internal communications. Then, taking an analytical approach, we analyze the spread behavior for cliques, and identify the conditions for monotonic behavior in a 2-clique network.