Haohao Zhu

Haohao Zhu, Xiaokun Zhang, Junyu Lu et al.

Textual personality detection aims to identify personality traits by analyzing user-generated content. To achieve this effectively, it is essential to thoroughly examine user-generated content from various perspectives. However, previous studies have struggled with automatically extracting and effectively integrating information from multiple perspectives, thereby limiting their performance on personality detection. To address these challenges, we propose the Multi-view Mixture-of-Experts Model for Textual Personality Detection (MvP). MvP introduces a Multi-view Mixture-of-Experts (MoE) network to automatically analyze user posts from various perspectives. Additionally, it employs User Consistency Regularization to mitigate conflicts among different perspectives and learn a multi-view generic user representation. The model's training is optimized via a multi-task joint learning strategy that balances supervised personality detection with self-supervised user consistency constraints. Experimental results on two widely-used personality detection datasets demonstrate the effectiveness of the MvP model and the benefits of automatically analyzing user posts from diverse perspectives for textual personality detection.

Haohao Zhu, Xiaokun Zhang, Junyu Lu et al.

Textual personality detection aims to identify personality characteristics by analyzing user-generated content on social media platforms. Extensive psychological literature highlights that personality encompasses both long-term stable traits and short-term dynamic states. However, existing studies often concentrate only on either long-term or short-term personality representations, neglecting the integration of both aspects. This limitation hinders a comprehensive understanding of individuals' personalities, as both stable traits and dynamic states are vital. To bridge this gap, we propose a Dual Enhanced Network (DEN) to jointly model users' long-term and short-term personality traits. In DEN, the Long-term Personality Encoding module models stable long-term personality traits by analyzing consistent patterns in the usage of psychological entities. The Short-term Personality Encoding module captures dynamic short-term personality states by modeling the contextual information of individual posts in real-time. The Bi-directional Interaction module integrates both aspects of personality, creating a cohesive and comprehensive representation of the user's personality. Experimental results on two personality detection datasets demonstrate the effectiveness of the DEN model and underscore the importance of considering both stable and dynamic aspects of personality in textual personality detection.

Zewen Bai, Shengdi Yin, Junyu Lu et al.

The proliferation of hate speech has caused significant harm to society. The intensity and directionality of hate are closely tied to the target and argument it is associated with. However, research on hate speech detection in Chinese has lagged behind, and existing datasets lack span-level fine-grained annotations. Furthermore, the lack of research on Chinese hateful slang poses a significant challenge. In this paper, we provide a solution for fine-grained detection of Chinese hate speech. First, we construct a dataset containing Target-Argument-Hateful-Group quadruples (STATE ToxiCN), which is the first span-level Chinese hate speech dataset. Secondly, we evaluate the span-level hate speech detection performance of existing models using STATE ToxiCN. Finally, we conduct the first study on Chinese hateful slang and evaluate the ability of LLMs to detect such expressions. Our work contributes valuable resources and insights to advance span-level hate speech detection in Chinese.

Haohao Zhu, Junyu Lu, Zeyuan Zeng et al.

Humor recognition aims to identify whether a specific speaker's text is humorous. Current methods for humor recognition mainly suffer from two limitations: (1) they solely focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor; and (2) they typically overlook the critical role of speaker individuality, which is essential for a comprehensive understanding of humor expressions. To bridge these gaps, we introduce the Commonality and Individuality Incorporated Network for Humor Recognition (CIHR), a novel model designed to enhance humor recognition by integrating multifaceted humor commonalities with the distinctive individuality of speakers. The CIHR features a Humor Commonality Analysis module that explores various perspectives of multifaceted humor commonality within user texts, and a Speaker Individuality Extraction module that captures both static and dynamic aspects of a speaker's profile to accurately model their distinctive individuality. Additionally, Static and Dynamic Fusion modules are introduced to effectively incorporate the humor commonality with speaker's individuality in the humor recognition process. Extensive experiments demonstrate the effectiveness of CIHR, underscoring the importance of concurrently addressing both multifaceted humor commonality and distinctive speaker individuality in humor recognition.