Spontaneous inferences on social media and their implications for ambient awareness

Abstract

Ambient awareness refers to the idea that social media users gain awareness of their online networks, while sifting through the stream of social updates spontaneously, without deliberate effort or intention. Since online networks are large and diverse, an efficient process like ambient awareness has important implications for how people can manage to maintain and profit from them (Donath, 2007; Resnick, 2001).

Despite its growing popularity in social media research, ambient awareness had not been studied systematically and there had been no unequivocal evidence that it can develop peripherally, that is, from fragmented information and in the relative absence of prior acquaintanceship and extensive communication. The objective of this dissertation was to examine the spontaneous inference processes implicated in ambient awareness.

Two exploratory surveys revealed that users of a microblogging site experience ambient awareness and are able to report specific knowledge of people whose updates they follow but whom they had not met in real life. These results strongly suggested that microblogging posts are sufficient for ambient awareness to develop. To test whether ambient awareness can indeed arise spontaneously, I adapted a paradigm from psychological research on spontaneous trait inferences. A series of experiments revealed that after viewing social media posts, people spontaneously formed accurate impressions of the actors who had ostensibly written the posts. These impressions included traits as well as domains of expertise, such as programmer or photographer. Another insight was that the amount of exposure and content had downstream consequences of people's impressions of competence and approachability. That people infer crucial information spontaneously while browsing social media is a premise underlying most reasoning surrounding ambient awareness. These studies are the first to directly test it.

Via social media, people receive a steady stream of updates and notifications from their extended networks, but this information is fragmented and too much to carefully processes and remember. The results of my dissertation suggest that even in such conditions, people are able extract information, which allows to make sense of who is who and who knows what in their vast online networks.