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In this paper we present our approach for detecting signs of depression fromsocial media text. Our model relies on word unigrams, part-of-speech tags,readabilitiy measures and the use of first, second or third person and thenumber of words. Our best model obtained a macro F1-score of 0.439 and ranked25th, out of 31 teams. We further take advantage of the interpretability of theLogistic Regression model and we make an attempt to interpret the modelcoefficients with the hope that these will be useful for further research onthe topic.

KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text