  Depression and anxiety are psychiatric disorders that are observed in many
areas of everyday life. For example, these disorders manifest themselves
somewhat frequently in texts written by nondiagnosed users in social media.
However, detecting users with these conditions is not a straightforward task as
they may not explicitly talk about their mental state, and if they do,
contextual cues such as immediacy must be taken into account. When available,
linguistic flags pointing to probable anxiety or depression could be used by
medical experts to write better guidelines and treatments. In this paper, we
develop a dataset designed to foster research in depression and anxiety
detection in Twitter, framing the detection task as a binary tweet
classification problem. We then apply state-of-the-art classification models to
this dataset, providing a competitive set of baselines alongside qualitative
error analysis. Our results show that language models perform reasonably well,
and better than more traditional baselines. Nonetheless, there is clear room
for improvement, particularly with unbalanced training sets and in cases where
seemingly obvious linguistic cues (keywords) are used counter-intuitively.
