A recent explosion in startup activity, often linked to reduced experimentation costs, has made it challenging for venture capital (VC) firms to efficiently obtain information and perform due diligence. This paper explores frictions in the process of venture capital information acquisition using microdata from Product Hunt, an online platform covering a large number of technology startups' product launches. On a daily basis, launched products compete for ranking based on user upvotes -- a crowdsourced measure of expected consumer demand. I document that exogenously raising a product by 1 rank improves the underlying firm's funding probability within the next 6 months by 9.2% from the base rate. Launching a highly ranked product is correlated with faster subsequent deal closing, more experienced lead investor, and larger funding amount. The effect of product rank is twice as large for first-time entrepreneurs, and mainly driven by firms located away from venture capital hubs.
We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We combine anonymized data from Facebook, the largest online social network, with housing transaction data, and explore both the structure and the effects of social networks. Individuals whose geographically distant friends experienced larger recent house price increases are more likely to transition from renting to owning. They also buy larger houses and pay more for a given house. Survey data show that these relationships are driven by the effects of social interactions on individuals' housing market expectations.