  The new coronavirus outbreak has been officially declared a global pandemic
by the World Health Organization. To grapple with the rapid spread of this
ongoing pandemic, most countries have banned indoor and outdoor gatherings and
ordered their residents to stay home. Given the developing situation with
coronavirus, mental health is an important challenge in our society today. In
this paper, we discuss the investigation of social media postings to detect
signals relevant to depression. To this end, we utilize topic modeling features
and a collection of psycholinguistic and mental-well-being attributes to
develop statistical models to characterize and facilitate representation of the
more subtle aspects of depression. Furthermore, we predict whether signals
relevant to depression are likely to grow significantly as time moves forward.
Our best classifier yields F-1 scores as high as 0.8 and surpasses the utilized
baseline by a considerable margin, 0.173. In closing, we propose several future
research avenues.
