[Paper Review] Deep Architectures for Content Moderation and Movie Content Rating

Fatih Çağatay Akyön, Alptekin Temizel|arXiv (Cornell University)|Dec 8, 2022

Video Analysis and Summarization4 citations

TL;DR

This paper surveys deep learning approaches for automated movie content rating and content moderation, focusing on multi-modal architectures that combine video, audio, and text features using CNNs, RNNs, and transformers. It highlights limitations in existing methods—particularly the lack of attention mechanisms and modern vision/audio transformers—and positions the work as a comprehensive review to guide future research in scalable, accurate, and multi-modal automated film censorship systems.

ABSTRACT

Rating a video based on its content is an important step for classifying video age categories. Movie content rating and TV show rating are the two most common rating systems established by professional committees. However, manually reviewing and evaluating scene/film content by a committee is a tedious work and it becomes increasingly difficult with the ever-growing amount of online video content. As such, a desirable solution is to use computer vision based video content analysis techniques to automate the evaluation process. In this paper, related works are summarized for action recognition, multi-modal learning, movie genre classification, and sensitive content detection in the context of content moderation and movie content rating. The project page is available at https://github.com/fcakyon/content-moderation-deep-learning.

Motivation & Objective

• To provide a comprehensive review of deep learning techniques for automated movie content rating and content moderation.
• To identify gaps in current approaches, particularly the underuse of attention mechanisms and modern vision/audio transformers.
• To synthesize findings from existing datasets and models to guide future research in scalable, multi-modal video content analysis.
• To highlight the limitations of single-modality and non-attention-based models in sensitive content detection.
• To support the development of end-to-end, multi-label, multi-modal systems for real-world deployment in content filtering and age-based classification.

Proposed method

• Systematically reviews 15+ datasets across image, video, and text modalities for content moderation and movie rating.
• Analyzes 12 key papers using CNNs, RNNs (LSTM, GRU), SVMs, and hybrid models for action recognition and sensitive content detection.
• Evaluates multi-modal fusion strategies combining video (CNN + LSTM), audio (MFCC, Wav2Vec), and text (BERT, GloVe, TextCNN) embeddings.
• Examines the use of pre-trained models such as VGG16, ResNet, Inception, MobileNet, and ViT for feature extraction in video and image classification.
• Highlights the absence of self-attention mechanisms and modern transformer-based encoders (e.g., ViT, Data2Vec, Wav2Vec) in most existing content rating systems.
• Compares performance and availability of datasets, noting that only a few (e.g., Movie script severity dataset, MM-Trailer) are publicly accessible.

Experimental results

Research questions

• RQ1 What are the dominant deep learning architectures used in automated movie content rating and content moderation?
• RQ2 How do multi-modal fusion strategies (video, audio, text) improve performance in detecting sensitive content?
• RQ3 Why are modern attention-based models like Vision Transformers and Wav2Vec underutilized in current content rating systems?
• RQ4 What are the key limitations of existing datasets in terms of modality coverage, labeling granularity, and public availability?
• RQ5 How can end-to-end, multi-label, multi-modal models be designed to improve scalability and accuracy in automated film censorship?

Key findings

• Most existing content moderation and movie rating systems rely on hand-crafted features (e.g., HOG, DCT) or shallow models (e.g., SVM), limiting performance.
• While CNNs and RNNs (LSTM, GRU) are widely used, only a few studies incorporate attention mechanisms or modern vision/audio transformers.
• The Violent Scenes Dataset [12] and VSD2014 [13] are among the most cited video datasets for violence detection, but they lack multi-label or multi-modal annotations.
• Only a small fraction of datasets (e.g., Movie script severity dataset [72]) are publicly available, limiting reproducibility and benchmarking.
• Multi-modal models using BERT, DeepMoji, and MFCC features show promise, but most are trained on private or restricted-access data.
• The lack of standardized, publicly available, multi-label, multi-modal datasets for sensitive content (violence, nudity, profanity) remains a major barrier to progress.