Improved Model for Identifying the Cyberbullying based on Tweets of Twitter
Open accessAbstract
The surge of cyberbullying on social media platforms is a major concern in today's digital age, with its prevalence escalating alongside advancements in technology. Thus, devising methods to detect and eliminate cyberbullying has become a crucial task. This research meticulously presents a refined model for identifying instances of cyberbullying, building on previous methodologies. The process of devising the model involved a thorough literature review, object-oriented design, and decision tree methodologies to shape the labelling procedure and build the classifier. Data pre-processing was executed using RapidMiner, considering six intrinsic components. The final model successfully classified Indonesian-language tweets into five distinct categories: animal, psychology and stupidity, disabled person, attitude, and general bullying, with an accuracy rate of 99.56%.
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The review identified weaknesses of machine learning classifiers in cyberbullying detection, including lack of automatic feature extraction, inability to perform real-time detection, and difficulty processing large volumes of data.
“The review identified weaknesses of machine learning classifiers in cyberbullying detection, including the lack of automatic feature extraction methods, inability to perform real-time detection, and difficulty processing large volumes of data [95,122,123].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [123]
Access restrictions on high-quality data constrain the utility of cutting-edge techniques, thereby limiting their applicability.
“Access restrictions on high-quality data constrain the utility of cutting-edge techniques, thereby limiting their applicability [123].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [123]
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References 16 · Cited by 5
Research works printed but not linked: 1
- Chen, Caixia, Liwei Geng, and Sheng Zhou (2021). "Design and implementation of bank CRM system based on decision tree algorithm". In: Neural Computing and Applications 33.14, pp. 8237-8247.
- Authors
- Samalo, D., Martin, R., & Utama, D. N.
- Affiliations
- Binus University (ID)
- Venue
- Informatica
- Publisher
- Slovenian Association Informatika
- Published
- 2023-06-12
- Volume
- 47
- Issue
- 6
- ISSN
- 0350-5596 (ISSN-L)
- 1854-3871
- OpenAlex
- W4380087776
- Cited by
- 5
- References
- 16
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 1 · 2023: 1
- FWCI
- 0.47
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Informatica (publishedVersion, cc-by)
- Informatica (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- cyberbullying detection, Twitter, tweets, Indonesian language, text classification, decision tree classification, social media abuse
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