This paper investigates the impact of using Multi-Task Learning (MTL) to predict mood changes over time for each individual (social media user). The presented models were developed as a part of the Computational Linguistics and Clinical Psychology (CLPsych) 2022 shared task. Given the limited number of Reddit social media users, as well as their posts, we decided to experiment with different multi-task learning architectures to identify to what extent knowledge can be shared among similar tasks. Due to class imbalance at both post and user levels and to accommodate task alignment, we randomly sampled an equal number of instances from the respective classes and performed ensemble learning to reduce prediction variance. Faced with several constraints, we managed to produce competitive results that could provide insights into the use of multi-task learning to identify mood changes over time and suicide ideation risk.

Mental illness detection in social media can be considered a complex task, mainly due to the complicated nature of mental disorders. In recent years, this research area has started to evolve with the continuous increase in popularity of social media platforms that became an integral part of people’s life. This close relationship between social media platforms and their users has made these platforms to reflect the users’ personal life with different limitations. In such an environment, researchers are presented with a wealth of information regarding one’s life. In addition to the level of complexity in identifying mental illnesses through social media platforms, adopting supervised machine learning approaches such as deep neural networks have not been widely accepted due to the difficulties in obtaining sufficient amounts of annotated training data. Due to these reasons, we try to identify the most effective deep neural network architecture among a few of selected architectures that were successfully used in natural language processing tasks. The chosen architectures are used to detect users with signs of mental illnesses (depression in our case) given limited unstructured text data extracted from the Twitter social media platform.

In this paper, we propose a novel deep-learning architecture for text classification, named cross segment-and-concatenate multi-task learning (CSC-MTL). We use CSC-MTL to improve the performance of cyber-aggression detection from text. Our approach provides a robust shared feature representation for multi-task learning by detecting contrasts and similarities among polarity and neutral classes. We participated in the cyber-aggression shared task under the team name uOttawa. We report 59.74% F1 performance for the Facebook test set and 56.9% for the Twitter test set, for detecting aggression from text.

We propose a novel attentive hybrid GRU-based network (SAHGN), which we used at SemEval-2018 Task 1: Affect in Tweets. Our network has two main characteristics, 1) has the ability to internally optimize its feature representation using attention mechanisms, and 2) provides a hybrid representation using a character level Convolutional Neural Network (CNN), as well as a self-attentive word-level encoder. The key advantage of our model is its ability to signify the relevant and important information that enables self-optimization. Results are reported on the valence intensity regression task.