Abstract

Social bots are fully automated software programs that perform a variety of tasks on behalf of users. Recently, social bots have become ubiquitous in online communities where they facilitate coding (e.g., GitHub), assist editorial work (e.g., Wikipedia), moderate discussions (e.g., Reddit), and post content (e.g., Twitter). The adoption of social bots have resulted in fundamental changes to users’ knowledge contribution behaviors. Much remains unknown regarding how social bots shape user contribution behaviors. A review of economics and human-computer interaction literatures revealed that our understanding of bots’ role is rather paradoxical:

– The displacement hypothesis argues that bots will substitute for human labor. This predicts a net loss in users’ knowledge contribution behaviors.

– The productivity hypothesis suggests that bots will boost productivity, which predicts a net gain in users’ knowledge contribution behaviors.

– The division of labor hypothesis posits that bots will allow for better division of labor, but the gains may be balanced by increased coordination costs.

Guided by the mixed theoretical predictions introduced above, this project aims to empirically answer: (1) How will bots change humans’ task selection preferences? (2) What is the size of the productivity effect if bots are assumed to facilitate knowledge creation processes?

Principal Investigators