  Mental disorders pose a global challenge, aggravated by the shortage of
qualified mental health professionals. Mental disorder prediction from social
media posts by current LLMs is challenging due to the complexities of
sequential text data and the limited context length of language models. Current
language model-based approaches split a single data instance into multiple
chunks to compensate for limited context size. The predictive model is then
applied to each chunk individually, and the most voted output is selected as
the final prediction. This results in the loss of inter-post dependencies and
important time variant information, leading to poor performance. We propose a
novel framework which first compresses the large sequence of chronologically
ordered social media posts into a series of numbers. We then use this time
variant representation for mental disorder classification. We demonstrate the
generalization capabilities of our framework by outperforming the current SOTA
in three different mental conditions: depression, self-harm, and anorexia, with
an absolute improvement of 5% in the F1 score. We investigate the situation
where current data instances fall within the context length of language models
and present empirical results highlighting the importance of temporal
properties of textual data. Furthermore, we utilize the proposed framework for
a cross-domain study, exploring commonalities across disorders and the
possibility of inter-domain data usage.
