  Yask is an online social collaborative network for practicing languages in a
framework that includes requests, answers, and votes. Since measuring
linguistic competence using current approaches is difficult, expensive and in
many cases imprecise, we present a new alternative approach based on social
networks. Our method, called Proficiency Rank, extends the well-known Page Rank
algorithm to measure the reputation of users in a collaborative social graph.
First, we extended Page Rank so that it not only considers positive links
(votes) but also negative links. Second, in addition to using explicit links,
we also incorporate other 4 types of signals implicit in the social graph.
These extensions allow Proficiency Rank to produce proficiency rankings for
almost all users in the data set used, where only a minority contributes by
answering, while the majority contributes only by voting. This overcomes the
intrinsic limitation of Page Rank of only being able to rank the nodes that
have incoming links. Our experimental validation showed that the
reputation/importance of the users in Yask is significantly correlated with
their language proficiency. In contrast, their written production was poorly
correlated with the vocabulary profiles of the Common European Framework of
Reference. In addition, we found that negative signals (votes) are considerably
more informative than positive ones. We concluded that the use of this
technology is a promising tool for measuring second language proficiency, even
for relatively small groups of people.
