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A Spam Detector Extension
Latest Version
Version
1.1
Update
May 13, 2023
Size
4.95MiB
Developer
Category
Users
67 
Rating
5 ★
Manifest Version
3
Web Store Status
Extension ID
Languages Support
Permissions
ActiveTab
Tabs
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Tabs
Spam ∅ Extension
A Spam Detection Tool for YouTube using Naive Bayes Classifier and Youtube data API.
To summarize, the developers developed a machine learning model using a Naive Bayes Classifier Algorithm to detect spam. This model was integrated into a browser extension that allows the user to collect comments from the YouTube Platform. This browser extension uses the YouTube Data API to collect or scrape comments from the website which will then be fed to the Naive Bayes algorithm for classification.
The Naive Bayes Algorithm developed was given training data that was also scraped from the website using the YouTube Data API and was taken from the videos of Popular channels such as Linus Tech Tips and PewDiePie. All these were collected, cleaned, and manually labeled as spam or ham following a set of criteria that determines what makes a spam comment.
This dataset was then evaluated using Evaluation Metrics such as Accuracy, Recall, Precision, and F1 Score. The dataset was made to gain a satisfying score from these metrics to get an accurate model.
Developed by Students of Bicol University
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To summarize, the developers developed a machine learning model using a Naive Bayes Classifier Algorithm to detect spam. This model was integrated into a browser extension that allows the user to collect comments from the YouTube Platform. This browser extension uses the YouTube Data API to collect or scrape comments from the website which will then be fed to the Naive Bayes algorithm for classification.
The Naive Bayes Algorithm developed was given training data that was also scraped from the website using the YouTube Data API and was taken from the videos of Popular channels such as Linus Tech Tips and PewDiePie. All these were collected, cleaned, and manually labeled as spam or ham following a set of criteria that determines what makes a spam comment.
This dataset was then evaluated using Evaluation Metrics such as Accuracy, Recall, Precision, and F1 Score. The dataset was made to gain a satisfying score from these metrics to get an accurate model.
Developed by Students of Bicol University
Source Manifest.json
{"update_url": "https://clients2.google.com/service/update2/crx", "manifest_version": 3, "name": "Spam \u2205", "description": "A Spam Detector Extension", "version": "1.1", "icons": { "16": "/Despic/48.png", "128": "Despic/Icon128.png" }, "action": { "default_icon": { "16": "/Despic/48.png", "128": "Despic/Icon128.png" }, "default_title": "Click to Start Extension", "default_popup": "popup.html" }, "permissions": [ "activeTab", "tabs" ], "content_scripts": [{ "matches": ["http://www.google.com/*"], "js": ["jquery-3.6.1.js", "popup.js","proto.js"] }]}
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