YouTube Spam Comment Detection

Antony, A. Santhi Mary; Rajendran, Anusha; Deepa, G.

doi:10.1007/978-981-99-1410-4_32

Cited by 1 publication

(2 citation statements)

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“…The accuracy performance results discussed earlier are based on the theoretical principles of each classification method. Neural network methods, as used in the study [6], excel in extracting intricate features from text data, enabling the recognition of patterns indicative of spam comments. Random forest, as outlined 3317 in [11], relies on ensemble methods, combining decisions from individual decision trees to handle data variations effectively.…”

Section: Resultsmentioning

confidence: 99%

“…These hybrid models effectively address the complexities and temporal patterns commonly found in text data, including spam comments, resulting in impressive accuracy levels, as demonstrated in the evaluation. Classifier Accuracy (%) [6] Neural network 91.65 [11] Random forest 90.57 [10] Naive Bayes 87.21 Logistic regression 85.29 [13] Support vector machine 74.40 K-nearest neighbor 56.70 [15] Markov…”

Section: Resultsmentioning

confidence: 99%

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Hybrid deep learning model for YouTube spam comment detection

Sam'an,

Imaddudin

2024

IJECE

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Social media platforms, including YouTube and Facebook, allow users to interact through comments and videos. However, the openness of these platforms also makes them susceptible to spammers engaging in phishing, malware distribution, and advertisement dissemination. In response, our study introduces an innovative technique for detecting features indicative of spam within comments associated with shared videos. The initial phase involves data collection from the University of California, Irvine (UCI) machine learning repository and preprocessing using tokenization and lemmatization. Subsequently, a rigorous feature selection process is executed, and experiments are conducted with various proposed classification models. The performance evaluation demonstrates outstanding accuracy in identifying spam comments on YouTube: convolutional neural network with gated recurrent unit (CNN-GRU) at 95.92%, convolutional neural network with long short-term memory (CNN-LSTM) at 95.41%, convolutional neural network with bidirectional long short-term memory (CNN-biLSTM) at 96.43%, gated recurrent unit (GRU) at 95.41%, long short-term memory (LSTM) at 94.13%, and bidirectional long short-term memory (biLSTM) at 96.94% and convolutional neural network (CNN) at 94.64%. These results highlight the substantial contribution of our approach to spam detection and the fortification of online security.

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Section: Resultsmentioning

confidence: 99%