  In this paper we present our approach for detecting signs of depression from
social media text. Our model relies on word unigrams, part-of-speech tags,
readabilitiy measures and the use of first, second or third person and the
number of words. Our best model obtained a macro F1-score of 0.439 and ranked
25th, out of 31 teams. We further take advantage of the interpretability of the
Logistic Regression model and we make an attempt to interpret the model
coefficients with the hope that these will be useful for further research on
the topic.
