  We consider in this paper the problem of predicting the ability of a startup
to attract investments using freely, publicly available data. Information about
startups on the web usually comes either as unstructured data from news, social
networks, and websites or as structured data from commercial databases, such as
Crunchbase. The possibility of predicting the success of a startup from
structured databases has been studied in the literature and it has been shown
that initial public offerings (IPOs), mergers and acquisitions (M\&A) as well
as funding events can be predicted with various machine learning techniques. In
such studies, heterogeneous information from the web and social networks is
usually used as a complement to the information coming from databases. However,
building and maintaining such databases demands tremendous human effort. We
thus study here whether one can solely rely on readily available sources of
information, such as the website of a startup, its social media activity as
well as its presence on the web, to predict its funding events. As illustrated
in our experiments, the method we propose yields results comparable to the ones
making also use of structured data available in private databases.
