Dangerous ideas: at the cutting edge of our knowledge
Online social networks have significantly changed global communication dynamics and, in general, the way we interact. Along with these profound effects on our society, they also made manifest that human cognitive abilities are a limited resource. Every day our brain is flooded by information coming from a plethora of sources and, in the age of cheap information— cheap to produce, to manipulate, to disseminate—, this cognitive bottleneck translates into hyper-competition for a scarce resource: our attention. This pressure, in turn, pushes information producers to mutualistically interact with specific memes and actors, seeking the virality of their messages. Finally, ideas’ chances to persist and spread are subject to changes in the communication environment that can focus collective attention on specific actors or topics. The fact that all these terms --competition for resources, mutualism and environmental changes-- are reminiscent of another research field --ecology--, and suggests that, at least at a coarse-grained level, these “information” ecosystems and natural ecosystems present strong similarities that can be exploited to understand their behavior. In this talk, building on these analogies, I will show how theories and tools developed for the study of ecological communities help to gain insights on online social interactions and to make sense of the huge quantity of digital traces they produce. In the first part, I will introduce an analytical formalism --based on the neutral theory of ecology--, capable of capturing the competition for attention and explaining several emergent patterns observed in information ecosystems. I will, then, show how this framework can be used to quantify the diversity of ideas and estimate the “health” of the communication process. In the second part, I will focus on the structure of information ecosystems showing that the underlying architecture of empirical actor-meme networks evolves towards self-similar nested arrangements like the ones found in natural mutualistic assemblages.