Clickbait Detection for YouTube Videos

Gothankar, Ruchira; Troia, Fabio Di; Stamp, Mark

doi:10.1007/978-3-030-97087-1_11

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…We can create a table to summarize the findings and explain the results. The table presents the results of the transformer-based and deep learningbased spam comment detection approaches for different ranges of comment length (≤10, 11-20, >20 words) (Amleshwaram et al, 2013;Concone et al, 2022;Ezpeleta et al, 2018;Gothankar et al, 2022;Teijeiro-Mosquera et al, 2014;Vidros et al, 2017;Wang, 2010) as shown in Figure 3 and Table 2. "Total Comments" column shows the total number of comments in each category.…”

Section: Resultsmentioning

confidence: 99%

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

Agarwal,

Nikitha,

Ramkumar

et al. 2023

JDSIS

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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 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 machine learning 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.

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Section: Resultsmentioning

confidence: 99%

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

Agarwal,

Nikitha,

Ramkumar

et al. 2023

JDSIS

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“…This research is done without human annotated data and provide a novel loss function and auto tuned reinforcement learning. More research was done [14] by implementing various deep learning algorithms to detect clickbait on Youtube. The features used are then grouped into 3.…”

Section: Introductionmentioning

confidence: 99%

Clickbait Classification Model on Online News with Semantic Similarity Calculation Between News Title and Content

Ahmadi

Chowanda

2023

bits

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Clickbait is a sensational title that makes us click internet links to an article, image, or video. Online content providers use clickbait to gain user traffic, that leads to increasing income from the placed ads in their page. To attract more and more traffic, online content providers write sensational and hyperbolic titles, and even misleading and not telling the whole story. This can give us, the internet consumer, wrong perspective, and half-truth. And nowadays, clickbait titles are worse than ever. Modern clickbait titles are not hyperbolic nor ambiguous enough, and sometimes very hard to identify. This paper aims to classify clickbait titles, to help humans identify clickbait and stop sharing more online content that contains clickbait and misleading titles. This model classifies clickbait by calculating semantic similarity between the article title and the summary of the article content. The article content is summarized by T5 (Text-to-text Transfer Transformer) model. IndoBERT is then used to calculate semantic similarity score between generated summary and the article title. The article title, content, summary, and semantic similarity score are used for clickbait classification with various algorithms. The result shows that by adding article content alongside article title in the classification process improves F1-score by 7% when classified with IndoBERT. In another future research, this model can be integrated with another application such as twitter or telegram bot to send us warning every time a user consumes online content with clickbait title. Thus, it can prevent online communities from sharing misleading information caused by clickbait

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