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02232nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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FT8876 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251215165138.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251215b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
QA76.9 A43 D37 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Daquigan, Jeffrey M.; Marbella, Gorel Kaiser G. |
| 245 ## - TITLE STATEMENT |
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| Title |
Enhancement of profanity filtering and hate speech detection algorithm applied in Minecraft chats |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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Undergraduate Thesis: Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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text |
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text |
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text |
| 337 ## - MEDIA TYPE |
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unmediated |
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unmediated |
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unmediated |
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volume |
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volume |
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volume |
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ABSTRACT: This study addresses critical limitations in an existing profanity-filtering algorithm: insufficient context interpretation, absence of leetspeak detection, and lack of customization capabilities. First, the researchers integrated the BERT transformer model to improve context-sensitive filtering, achieving a 99.1% accuracy rate and a 10.4% increase in correctly censoring 1,000 chat results from the Minecraft-Server-Chat dataset. Toxicity scoring with Toxic-BERT allowed precise filtering, distinguishing between friendly and harmful content words. Second, the researchers incorporated a reverse mapping function to identify leetspeak, significantly improving censorship accuracy. In the dataset of 1,000 chats in Minecraft-Server-Chat dataset, 108 leetspeak inputs were analyzed. The Enhanced Algorithm demonstrates an 82.4% censorship success rate for leetspeak-masked inputs, reducing the error rate to 2.8% compared to the Existing Algorithm’s 10.2%. Furthermore, a customization function was introduced, allowing users to add and remove profane words, ensuring adaptability to shifting language trends and cultural nuances. It was found that the Enhanced algorithm had a performance improvement of 8.3% over the existing algorithm. These advancements make the Enhanced Algorithm a robust, context-aware, accuracy in leetspeaks, and user-centric tool for moderating Minecraft chats, fostering a safer and more inclusive online environment. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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Filipiniana |
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academic writing |
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| Item type |
Thesis/Dissertation |