A Unified Deep Learning Architecture for Abuse Detection (1802.00385v2)

Abstract: Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such diverse abusive behaviors have been manifesting with increased frequency and levels of intensity. This is due to the openness and willingness of popular media platforms, such as Twitter and Facebook, to host content of sensitive or controversial topics. However, these platforms have not adequately addressed the problem of online abusive behavior, and their responsiveness to the effective detection and blocking of such inappropriate behavior remains limited. In the present paper, we study this complex problem by following a more holistic approach, which considers the various aspects of abusive behavior. To make the approach tangible, we focus on Twitter data and analyze user and textual properties from different angles of abusive posting behavior. We propose a deep learning architecture, which utilizes a wide variety of available metadata, and combines it with automatically-extracted hidden patterns within the text of the tweets, to detect multiple abusive behavioral norms which are highly inter-related. We apply this unified architecture in a seamless, transparent fashion to detect different types of abusive behavior (hate speech, sexism vs. racism, bullying, sarcasm, etc.) without the need for any tuning of the model architecture for each task. We test the proposed approach with multiple datasets addressing different and multiple abusive behaviors on Twitter. Our results demonstrate that it largely outperforms the state-of-art methods (between 21 and 45\% improvement in AUC, depending on the dataset).

A Unified Deep Learning Architecture for Abuse Detection

The paper entitled "A Unified Deep Learning Architecture for Abuse Detection" by Founta et al. presents a comprehensive approach to the multifaceted challenge of detecting abusive behavior across online social media platforms. This research suggests an integration of diverse deep learning techniques with metadata processing to create a robust framework capable of identifying various forms of abusive behavior, including hate speech, racism, sexism, cyberbullying, and sarcasm, without requiring task-specific tuning.

Key Contributions and Findings

The paper outlines several significant contributions:

1. Unified Deep Learning Architecture: The proposed architecture integrates both text-based and metadata-based insights to create a unified model for abuse detection. By utilizing deep learning, the model extracts subtle and latent patterns from text data, thus overcoming limitations inherent in traditional machine learning approaches that rely heavily on handcrafted features.
2. Improvement Over State-of-the-Art: Through thorough experimentation, the authors demonstrate that their model outperforms existing methods significantly, achieving improvements in AUC ranging between 21% and 45% on various datasets. This showcases the model's ability to generalize across different types of abusive behaviors while maintaining high accuracy.
3. Optimal Use of Heterogeneous Inputs: By combining text data with available metadata—such as user characteristics and network features—this research highlights the importance of a holistic approach to recognizing abuse. The novel interleaved training method is particularly effective, allowing individual paths within the model to be optimized alternately, therefore maximizing the utility of all available data.
4. Extension to Other Domains: The model's applicability isn't confined to social media; its robustness is underscored by its successful deployment to detect toxic behavior in online gaming environments, thus suggesting versatility across digital ecosystems.

Methodology

The architecture described relies primarily on Recurrent Neural Networks (RNNs) for processing textual data, enhanced by pre-trained word embeddings (GloVe). Metadata, encompassing user-level, tweet-level, and network-level attributes, are processed through dense neural network layers. The integration of these two paths within the model architecture allows for simultaneous learning from both semantic content and contextual features, which augments its detection capabilities.

Implications and Future Work

Practically, this research indicates promising advancements in automating abuse detection with minimal task-specific tuning. The potential for deployment across various platforms could influence the strategic implementation of moderation systems on social media and other user-interactive services. The flexibility of this approach opens up opportunities for adaptation to other domains where abusive or toxic content could pose challenges.

From a theoretical standpoint, this paper advances the understanding of how multimodal machine learning systems can be applied to complex real-world problems. The efficacy of interleaved training paves the way for further exploration into dynamic training regimes for neural networks handling heterogeneous data.

Future research directions may include experimenting with additional modalities, such as audio or video, within this unified framework. Furthermore, exploring the adaptability of this architecture to newer forms of digital communication, such as metaverse interactions or real-time streaming platforms, could further broaden its impact.

Overall, this paper provides a substantial contribution to both the technological and methodological landscapes, highlighting the intricate yet promising task of detecting online abuse through unified deep learning strategies.