Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage

Abstract: Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, in Online Social Platforms. These platforms make use of a plethora of tools to detect and manage malicious information; however, malicious actors also improve their skills, developing strategies to surpass these barriers and continuing to spread misleading information. Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems. In response to this recent ongoing issue, this paper presents an innovative approach to address this linguistic trend in social networks through the simulation of different content evasion techniques and a multilingual Transformer model for content evasion detection. In this way, we share with the rest of the scientific community a multilingual public tool, named "pyleetspeak" to generate/simulate in a customizable way the phenomenon of content evasion through automatic word camouflage and a multilingual Named-Entity Recognition (NER) Transformer-based model tuned for its recognition and detection. The multilingual NER model is evaluated in different textual scenarios, detecting different types and mixtures of camouflage techniques, achieving an overall weighted F1 score of 0.8795. This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content on social networks, making the fight against information disorders more effective.

• The paper presents a Transformer-based multilingual method for simulating and detecting various word camouflage techniques effectively.
• It introduces ‘pyleetspeak’, a Python package that generates synthetic evasion data using techniques like leetspeak, punctuation insertion, and word inversion.
• The evaluation using NER models, including MPNET-ideal, demonstrates robust performance and adaptability across multiple languages and text genres.

Countering Malicious Content Moderation Evasion: Simulation and Detection of Word Camouflage

Introduction

The rise of social media platforms has necessitated robust content moderation systems to counteract the proliferation of harmful user-generated content. These systems often employ tools designed to detect and filter unacceptable content like hate speech, terrorism, and misinformation. Malicious actors, however, continually adapt, developing strategies to bypass such moderation. Among their techniques, word camouflage—altering keywords to evade automatic detection—is prevalent. This paper proposes a methodology utilizing a Transformer-based multilingual approach to simulate and detect these evasive techniques effectively.

Simulation and Methodology

To replicate real-world evasion tactics, the research introduces a publicly accessible Python package, "pyleetspeak," capable of simulating text modifications using various techniques. These modifications include leetspeak, punctuation insertion, and word inversion, providing a customizable framework that can generate evasion data in over 20 languages.

Figure 1: Named Entity Recognition data generation diagram. $p_L$, $p_I$ and $p_P$ represent the probability of applying leetspeak, inversion, and punctuation camouflage techniques. $p_M$ represents the probability of mixing these techniques.

Dataset and NER Model Training

Utilizing pyleetspeak, the research generates a synthetic multilingual dataset by applying camouflage techniques to existing corpora, such as News-Commentary and WikiMatrix. The paper meticulously details the methodology for creating a training set rich in diverse evasion examples. The Named Entity Recognition (NER) task is integral to detecting camouflaged entities, categorizing them into leetspeak, punctuation, inversion, or mixed methods.

A suite of models, including MPNET variations and BLOOMz, are trained on this dataset using SpaCy, with hyperparameters fine-tuned for multilingual efficiency. The model architecture leverages pre-training on multilingual Semantic Textual Similarity datasets, enhancing its capability to generalize across languages and contexts.

Experimental Results

Table 1 showcases the performance metrics, with particular emphasis on the weighted and macro F1 scores across multiple datasets. The models demonstrate robust detection capabilities, with MPNET-ideal achieving a superior weighted F1 score overall.

The comparative analysis with monolingual baselines reveals the multilingual model's proficiency, highlighting its adaptability and performance gains across different languages and datasets.

Confusion Matrix Analysis

The confusion matrix analysis further elucidates the detection precision across varying camouflage types. The study notes that distinguishing between mixed techniques and pure forms (e.g., leetspeak alone vs. mixed) remains a challenge, yet the models exhibit strong detection efficacy, particularly in formal text scenarios like News Commentary.

Conclusion

The paper contributes significantly to the field of content moderation by providing robust tools and datasets for countering text-based evasion techniques. The multilingual NER model not only improves detection accuracy across languages but also illustrates the potential for integrating semantic similarity pre-training in enhancing model performance.

Future work will explore the application of these methodologies to real-time moderation systems and evaluate the models' robustness against evolving evasion tactics. The "pyleetspeak" tool and dataset are positioned as foundational resources for further research and development in automated content moderation systems.