DeepGram: Combining Language Transformer and N-Gram based ML Models for YouTube Spam Comment Detection

Abstract

Spam comments on YouTube videos are a persistent issue that can negatively impact the user experience and content creator’s reputation. In this paper, we propose an algorithm called “DeepGram” for detecting YouTube spam comments using a combination of deep learning-based language transformer models and N-gram-based machine learning (ML) models. The algorithm leverages the power of language transformers, which have shown significant success in various natural language processing tasks, along with N-gram-based models that capture local context and patterns in the text data. The proposed algorithm goes through several stages, including data collection, text preprocessing, feature extraction, and model training. The collected YouTube comments are preprocessed by removing special characters, punctuation, and HTML tags and converting them to lowercase. Common stop words are also removed, and stemming or lemmatization is applied to reduce dimensionality. The algorithm then extracts features from the preprocessed comments using a combination of language transformer models and N-gram-based features. Finally, the features are fed into ML models for training and evaluation. Experimental results on a large dataset of YouTube comments show that the DeepGram algorithm achieves high accuracy and robust performance in detecting spam comments. The proposed algorithm can be potentially employed as an effective tool for YouTube content creators and platform moderators to combat spam comments and improve the quality of user interactions on YouTube videos.

Received: 15 April 2023 | Revised: 15 September 2023 | Accepted: 16 November 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are upon request. Requests should be made to anuragsinha257@gmail.com and should include a brief description of the intended use of the data.