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This paper introduces DeMHeM, a novel multitask framework designed for the descriptive classification of bipolar and related mental health topics on online platforms like Reddit. The model distinguishes between different mental health categories and also models the correlation among them by categorizing each post into potentially multiple mental health categories. DeMHeM leverages both the shared latent and task-specific semantic feature space by integrating sentence-level and topic-level embeddings. It further incorporates Focal Loss for joint learning, inter-task parameter sharing, and regularization decay to optimize the prediction for the naturally skewed imbalanced dataset. Through extensive experiments and a comprehensive ablation study, we demonstrate the effectiveness of our model, with results outperforming existing baselines. Furthermore, a case study is conducted to analyze the entirety of the "r/bipolar" subreddit to understand the specific nuances in the discussion of bipolar disorder by applying our model to data collected from January 2020 to December 2021 and extracting the top keywords of each predicted category. Our analysis shows that DeMHeM can be used to understand the multi-faceted discussion of mental health topics for a given community. 

This paper presents an innovative framework developed to identify, analyze, and generate memorable experiences in the hotel industry. People prefer memorable experiences over traditional services or products in today’s ever-changing consumer world. As a result, the hospitality industry has shifted its focus toward creating unique and unforgettable experiences rather than just providing essential services. Despite the inherent subjectivity and difficulties in quantifying experiences, the quest to capture and understand these critical elements in the hospitality context has persisted. However, traditional methods have proven inadequate due to their reliance on objective surveys or limited social media data, resulting in a lack of diversity and potential bias. Our framework addresses these issues, offering a holistic solution that effectively identifies and extracts memorable experiences from online customer reviews, discerns trends on a monthly or yearly basis, and utilizes a local LLM to generate potential, unexplored experiences. As the first successfully deployed, fast, and accurate product of its kind in the industry, This framework significantly contributes to the hotel industry’s efforts to enhance services and create compelling, personalized experiences for its customers. 

This paper introduces HyperMAD, a novel Hypergraph Convolutional Network model designed for the multiclass classification of mental health advice in Arabic tweets. The model distinguishes between misleading and valid advice, further categorizing each tweet into specific classes of advice. HyperMAD leverages high-order relations between words in short texts, captured through the definition of four types of hyperedges that represent local and global contexts as well as semantic similarity. Extensive experiments demonstrate the effectiveness of HyperMAD, with results outperforming those from existing baselines. The study also includes an ablation study to investigate the significance and contribution of each hyperedge type. The paper presents a case study analyzing the accuracy and types of Arabic mental health advice on Twitter, revealing that about 9% of the advice in response to mental health expressions on Twitter was accurate in general. The paper concludes with the hope that the application of HyperMAD can be utilized in flagging misleading responses on social media, providing the correct resources for those who choose to share their mental health struggles online.