Hypergraph Text Classification for Mental Health Misleading Advice

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.