This repository contains code for training and using deep learning models for mental disorder detection from social media data.
The required libraries are:
- tensorflow2, keras
- numpy, nltk, sklearn, pandas
The full list of packages and versions I used is found in requirements.txt (may contain some unnecessary ones)
The required data:
- expects a
config.jsonin the root directory (containing paths to resource files) - for generating predictions, expects files with trained model weights and their hyperparameter configurations in
models/ - expects external resources (NRC lexicon, LIWC lexicon, pre-trained embeddings) in
resources/ - expects vocabulary files and a LIWC cache file in
data/
predict_erisk.py illustrates how models can be loaded and used to generate predictions on eRisk data.
predict(run_nr, data_rounds) can be used to obtain predictions (scores and alerts ("decision"s) for each user ("nick"), for each datapoint) from a specific trained model given some data obtained from the eRisk server, across one or more rounds of interaction with the server.
For more control, EriskDataGenerator() (along with model.predict_step()) can be used directly, which allows to incrementally add new datapoints to the generator as they are received from the server (without creating a new loader for each new datapoint).
The code in this repository has been used for experiments published in several papers. If using this resource, please cite the relevant papers:
- Explainability of Depression Detection on Social Media: From Deep Learning Models to Psychological Interpretations and Multimodality, Ana-Sabina Uban, Berta Chulvi, Paolo Rosso, in Early Detection of Mental Health Disorders by Social Media Monitoring, 2022
- Detecting early signs of depression in the conversational domain: The role of transfer learning in low-resource scenarios, Petr Lorenc, Ana-Sabina Uban, Paolo Rosso, Jan Šedivý, NLDB 2022
- Multi-aspect transfer learning for detecting low resource mental disorders on social media, Ana Sabina Uban, Berta Chulvi, Paolo Rosso, LREC 2022
- On the Explainability of Automatic Predictions of Mental Disorders from Social Media Data, Ana Sabina Uban, Berta Chulvi, Paolo Rosso, NLDB 2021
- An emotion and cognitive based analysis of mental health disorders from social media data, Ana-Sabina Uban, Berta Chulvi, Paolo Rosso, Future Generation Computer Systems, 2021
- Upv-symanto at erisk 2021: Mental health author profiling for early risk prediction on the internet, Angelo Basile, Mara Chinea-Rios, Ana-Sabina Uban, Thomas Müller, Luise Rössler, Seren Yenikent, María Alberta Chulvi-Ferriols, Paolo Rosso, Marc Franco-Salvador, CLEF 2021
- Deep learning architectures and strategies for early detection of self-harm and depression level prediction, Ana-Sabina Uban, Paolo Rosso, CLEF 2020