- The paper introduces a novel heterogeneous graph model that integrates community structure and linguistic behavior for improved abusive language detection.
- It utilizes a semi-supervised GCN approach that propagates labels through interconnected tweet and author nodes to build comprehensive profiles.
- Experimental results show that combining GCN-extracted profiles with traditional methods significantly enhances precision, recall, and F1 scores.
Abusive Language Detection with Graph Convolutional Networks
The paper "Abusive Language Detection with Graph Convolutional Networks" presents a novel approach to detecting abusive language on social media, specifically Twitter, by leveraging graph convolutional networks (GCNs). The work is significant in that it addresses the limitations of prior methodologies that primarily relied on community profiling through basic follower-following networks, lacking a nuanced understanding of linguistic interactions within these communities.
Key Contributions
The primary contribution of this research lies in the introduction of a heterogeneous graph model that incorporates both community structure and the linguistic behavior of authors. Unlike traditional methods, which model online communities using homogeneous graphs focused on simple relationships between users, this approach models more complex interactions by treating tweets and authors as distinct but interconnected nodes. This allows the model to simultaneously capture user connectivity and their language use, enabling a more robust representation of community behavior.
Furthermore, the paper introduces a semi-supervised learning approach using GCNs on the extended graph. This method is unique in that it can propagate information from tweets labeled as abusive throughout the community, helping to develop comprehensive author profiles. These profiles integrate both network structure and linguistic context, providing an enriched dataset for training machine learning models aimed at abusive language detection.
Experimental Evaluation
The researchers evaluated their approach using a subset of the Twitter dataset compiled by Waseem and Hovy, focusing on tweets labeled as racist, sexist, or clean. They compared their GCN-based models with several baseline methods, including logistic regression classifiers that use character n-grams and author profiling from node2vec on community graphs.
The results demonstrated that the proposed GCN-based methods notably outperformed the baselines. In particular, the method combining logistic regression with GCN-extracted author profiles achieved the highest precision, recall, and F1 scores across both racism and sexism categories. This highlighted the advantage of incorporating richer author profiles that take linguistic cues and community data into account.
Implications and Future Directions
The implications of this research are manifold. Practically, it provides a more effective mechanism for social media platforms to identify and manage abusive content, contributing to safer online environments. Theoretically, it advances the understanding of how community structures and individual behaviors can be integrated into models that address negative behaviors on social media.
Future developments could focus on enhancing this framework by incorporating additional data sources, such as multimedia content or extended community interactions beyond Twitter. Furthermore, adapting these models to real-time data streams could improve the timeliness and reliability of abuse detection systems. Moreover, addressing the challenges posed by obfuscation techniques used to bypass detection algorithms remains an area ripe for further investigation.
In conclusion, this paper's approach utilizing graph convolutional networks for abusive language detection provides a compelling advance in integrating community structure with language behavior, achieving significant improvements in detecting negative behaviors online and setting the stage for future research in this critical area.