Automated personality subtyping is a crucial area of research with diverse applications in psychology, healthcare, and marketing. However, current studies face challenges such as insufficient data, noisy text data, and difficulty in capturing complex personality traits. To address these issues, including empathy, distress, and emotion as auxiliary tasks in automated personality subtyping may enhance accuracy and robustness. This study introduces a Multi-input Multi-task Framework for Personality, Empathy, Distress, and Emotion Detection (MultiPEDE). This framework harnesses the complementary information from empathy, distress, and emotion tasks (auxiliary tasks) to enhance the accuracy and generalizability of automated personality subtyping (the primary task). The model uses a novel deep-learning architecture that captures the interdependencies between these constructs, is end-to-end trainable, and does not rely on ensemble strategies, making it practical for real-world applications. Performance evaluation involves labeled examples of five personality traits, two classes each for personality, empathy, and distress detection, and seven classes for emotion detection. This approach has diverse applications, including mental health diagnosis, improving online services, and aiding job candidate selection.

Due to its growing impact on public opinion, hate speech on social media has garnered increased attention. While automated methods for identifying hate speech have been presented in the past, they have mostly been limited to analyzing textual content. The interpretability of such models has received very little attention, despite the social and legal consequences of erroneous predictions. In this work, we present a novel problem of Distress Identification and Cause Extraction (DICE) from multimodal online posts. We develop a multi-task deep framework for the simultaneous detection of distress content and identify connected causal phrases from the text using emotional information. The emotional information is incorporated into the training process using a zero-shot strategy, and a novel mechanism is devised to fuse the features from the multimodal inputs. Furthermore, we introduce the first-of-its-kind Distress and Cause annotated Multimodal (DCaM) dataset of 20,764 social media posts. We thoroughly evaluate our proposed method by comparing it to several existing benchmarks. Empirical assessment and comprehensive qualitative analysis demonstrate that our proposed method works well on distress detection and cause extraction tasks, improving F1 and ROS scores by 1.95% and 3%, respectively, relative to the best-performing baseline. The code and the dataset can be accessed from the following link: https://www.iitp.ac.in/~ai-nlp-ml/resources.html\#DICE.