Networks Webinar - Peter Bossaerts (Cambridge)

Abstract: 

According to theory, network activity can be predicted from network structure. In Social Finance networks, however, activity can by-pass network structure, through top-down or bottom-up content curation. Here, we study functional connectivity (actual network activity) in the largest Social Finance network, StockTwits, to discover what determines influence. Contrary to its appearance as a scale-free network, well-connected nodes enjoy significantly less influence than lesser connected nodes, except if they acquire the status of “Official Accounts.” Message content and sentiment appear to matter as much as location and status for influence. Our findings suggest that the threat of disintermediation in the financial advice industry can in part be circumvented by engaging in an informed way with Social Finance.