| Journal of Information and Communications Technology:
Algorithms, Systems and Applications
Received: 20 April 2025; Revised: 10 June 2025; Accepted: 24 June 2025; Published Online: 25 June 2025.
J. Inf. Commun. Technol. Algorithms Syst. Appl., 2025, 1(1), 25307 | Volume 1 Issue 1 (June 2025) | DOI: https://doi.org/10.64189/ict.25307
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
A Comparative Analysis of Machine Learning
Techniques for Cyberbullying Detection
Minal Barhate,* Parikshit N. Mahalle, Vrushal Patil, Rahul Yargop, Shivani Yanpallewar, Tanmay Zade and Vedant
Kothari
Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, Maharashtra,
411037, India
*Email: minal.barhate@vit.edu (Minal Barhate)
Abstract
A major worry in the current digital era is cyberbullying, which primarily affects young people who use Web
2.0-powered social media platforms. It usually entails threatening, degrading, or emotionally harming people
via online tools and platforms, which frequently results in major psychological consequences like anxiety,
sadness, and low self-esteem. This study uses both publicly available Twitter data and data that has been
scraped from the platform to examine how machine learning algorithms can be used to detect and categorize
instances of cyberbullying. After the text data is cleaned and processed using vectorization techniques, it is
analyzed using a variety of supervised learning algorithms. These include Naive Bayes, Random Forest, Support
Vector Machines, Logistic Regression, and ensemble models like AdaBoost and Bagging. Each model is assessed
using metrics like accuracy, precision, recall, F1-score, and performance efficiency. The study highlights the
importance of fine-tuning for practical application by comparing model results. Along with figuring out how to
best identify cyberbullying, the goal is to promote the creation of increasingly sophisticated technologies that
can help create a safer online environment. This work helps ongoing efforts to use natural language processing
and machine learning to alleviate the negative effects of cyberbullying.
Keywords: Cyberbullying; Machine learning; Text classification; Social media; Twitter.
1. Introduction
The rise of digital technologies and social media has changed how people, especially young people,
communicate. While platforms like Facebook, Twitter, Instagram, and YouTube help connect people worldwide,
they have also become spaces where cyberbullying occurs. Cyberbullying is when someone uses phones, social
networks, or messaging apps to repeatedly threaten or harass others online.
[1]
Serious mental health problems
like anxiety, depression, and low self-esteem can result from this type of online abuse.
[2-4]
As more people rely on the Internet to interact, it has become both a helpful tool and a potential danger,
especially for young users. Unfortunately, traditional methods of spotting and stopping cyberbullying, like
manual content review, are not enough to handle the massive amount of content posted every day.
[5]
Every year the minimum age in which a child has mobile is decreasing and with the introduction to mobile in
lesser age there pose a risk of the child getting exposed to cyberbullying. Introduction to bullying in its
development age poses risk of child development in the wrong direction. To prevent this, we need a strong
model to prevent bullying via digital services.
In this sudy, we have reviewed to identify cyberbullying through the use of machine learning (ML) and natural
language processing (NLP). To determine which supervised learning model is most effective in detecting
hazardous texts, we will construct and evaluate several models. Helping to safeguard users and make the
internet a safer place is our aim. In addition, we are evaluating several machine learning algorithms to see which
one is most appropriate for the task.
As we can clearly see in Fig. 1, every year number cyberbullying victims increases. The data shows victims of
cyberbullying in percentage of internet users in age group of 1018-year-old.
1.1 Literature survey
A major problem these days is cyberbullying, especially with the growth of social media and online messaging
Fig. 1: Estimated global victimization rate. (Data source: https://www.vpnranks.com/resources/cyberbullying-
statistics/)
services. As more conversations move online, there's a growing need for systems that can automatically detect
and address harmful messages. Researchers have approached this problem from several angles, exploring both
how to represent text data and how to classify it effectively.
[6]
One of the key challenges that we faced is how to turn raw text into something a machine can understand.
Techniques similar to the Count Vectorizer and TF-IDF are commonly used to convert words into numerical
features.
[7,8]
While both have their merits, some studies have found that Count Vectorizer can slightly
outperform than TF-IDF when it comes to accuracyespecially in informal, mixed-language texts like Hinglish
(a blend of Hindi and English often seen in online chats).
[9]
Even little gains in performance can have a significant
impact when handling delicate matters like identifying bullying.
[10]
For cyberbullying detection systems to be effective, selecting the right algorithm is very essential.
[11]
After
gradient descent (SGD) classifier.
[12]
These effective techniques are ideal for real world challenges where speed
and scalability are crucial because they provide high accuracy along with that they run rapidly. By combining
various approaches, some studies have investigated more innovative solutions. Combining fuzzy logic with the
multinomial Naïve Bayes classifier is one of the prime examples.
[13,14]
Naïve Bayes is a straightforward but
reliable technique for text classification,
[15]
while fuzzy logic enhances the system's comprehension of
ambiguous and might be emotionally charged content.
[16]
The hybrid approach where we evaluate a message
severity level or emotional concern along with it whether it constitutes bullying which provides deep
comprehension of the content.
[17]
Efficient SVM and probabilistic models like Naive Bayes had been combined in few of the more advance
cyberbullying detection software.
[18-20]
This strategy of combing both this technique provides important features
speed and simplicity. The final product is a
framework that can detect cyberbullying more accurately. Statistics from various surveys shows that higher
number of people are cyberbullied as compared to in-person bullying, which is one of the key reasons behind
developing such systems. As a result, efforts are made to create such system specially for dynamic environments
like chat application and game development too. While using classifiers like Naïve Bayes to identify potentially
harmful messages based on previously learned patterns, these tools usually start by preprocessing the input
text-removing noise like special characters, duplicates, or irrelevant content.
[21,22]
Finally, starting from basic text classification to more complex that considers the subtleties of language, context
and user behaviour too. The development of such safe, cyberbullying free environments by deploying more
intelligent, accurate AI-driven technologies is highly promising if this field of study is pursued further.
2. Methodology
Creating an efficient detection system has become more crucial as digital platforms continue to expand, given
the rise in cyberbullying, which primarily targets younger users. To tackle this, we started by utilizing a
substantial dataset of 35,787 tweets that were obtained from Kaggle in CSV format. The text of the tweet was
included in each entry, along with a label designating it as either offensive (1) or non-offensive (0). After
performing analysis on available dataset, we found out that 23,547 tweets were recognized as offensive along
with these 12,239 tweets were recognized as non-offensive.
The raw data from tweet needed to be cleaned and prepared before we move to next steps. To confirm that the
dataset was prepared for precise analysis, this preprocessing step was very important. We used NLP technique
like NLTK, which allowed us to transform the unstructured data more user-friendly format. This involved
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
2019 2021 2023 2025
(projected)
breaking up the tweets into smaller, more manageable chunks using tokenization and eliminating frequently
used, semantically weak words (also called stop words) that don't add much to the study.
We came to conclusion to use Count Vectorizer to convert the processed text into numerical features that our
ML model could learn more easily. This tool was specially designed to deal with the casual and frequently erratic
language found on social media. We initiated by cleaning the text, removing stop words and converting all
characters to lowercase in order to get the data ready for model training. This made feature extraction more
consistent and helped standardize the input. Using PunktSentenceTokenizer, we first divided each tweet into
sentences as part of our preprocessing. Whitespace, WordPunct, TreebankWord, and PunktWord tokenizers
were the four methods we used to tokenize these sentences into words. After tokenization, converting all text
to lowercase was a crucial normalization step that guaranteed consistency and increased the dependability of
the features used to train our models.
We used 75% of the dataset for training and the remaining 25% for testing after it had undergone extensive
preprocessing. Using this configuration, we trained a range of machine learning models and assessed their
performance to find out how well they could identify offensive or non-offensive tweets.
2.1 Machine learning models used
2.1.1 Bagging Classifier (BC)
Bagging is type of ensemble learning method, which is intended to increase the stability and accuracy of models.
A number of weaker models are trained on random subsets of dataset, and their predictions are combined-
usually by voting in classification tasks or averaging in case of regression tasks. This method reduces overfitting
and improves the final model's stability and dependability of cyberbullying text classification. Below mentioned
equations are bagging prediction for regression task and classification task.
(1)
(2)
2.2.2 Stochastic Gradient Descent (SGD) Classifier
A very effective optimization technique that works particularly well for large-scale learning applications is
stochastic gradient descent (SGD). Unlike batch methods that process the entire dataset at once, SGD updates
the model parameters one data point at a time. This incremental approach allows for quicker convergence and
makes it ideal for handling massive datasets. The update rule,
θₜ₊₁ = θₜ − η
θL(θₜ; wᵢ, wᵢ) (3)
effectively handles vast and sparse feature spaces, which are typical in text classification issues like
cyberbullying detection, allowing for scalable optimization.
2.2.3 Logistic Regression Classifier (LRC)
One of the fundamental algorithms in statistical learning, logistic regression is frequently applied to challenges
involving binary categorization. It learns a decision boundary (also known as a hyperplane) that efficiently
divides the two classes and estimates the likelihood of an outcome using the logistic (sigmoid) function.
(4)
(5)
(6)
The logistic function estimates the probability of a text being cyberbullying, enabling binary classification with
interpretable outputs.
2.2.4 Decision Tree Classifier (DTC)
Decision trees are easy-to-understand models that classify data by recursively splitting it into branches based
on feature values. They are versatile, performing well even with missing data, and can work with both
categorical and numerical inputs. They are particularly helpful in the context of text classification because they
can reveal intricate decision patterns. For instance, when detecting cyberbullying, the tree can split based on
the presence of specific keywords or linguistic cues. Measures like the Gini Index
(7)
and Entropy
(8)
guide these splits, helping the model focus on the most informative features. By repeatedly splitting on word-
based features, decision trees can effectively learn patterns common in abusive or harmful language.
2.2.5 Linear Support Vector Classifier (LSVC)
Support Vector Machines (SVMs) and Linear Support Vector Classifiers (LSVC) perform very well for linear
classification problems. We incorporated a bi-gram tokenizer along with a minimum word frequency threshold
contender for precisely detecting cyberbullying. The function which decides whether a text is offensive or non-
offensive, works by drawing a optimal margin separation between them is mentioned below:
H(x)="sign" (w^ x+b), (9)
We can achieve better generalization by maximizing the separation between these two classes, which provides
more accuracy for classifying abusive content.
2.2.6 Random Forest Classifier (RFC)
Using random samples and features, Random Forest constructs several decision trees, then uses majority voting
to aggregate the results. This ensemble method balances bias and variance and is known for its robustness and
high accuracy.
(10)
Ensemble of decision trees trained on different word subsets increases accuracy and stability in detecting toxic
online content.
2.2.7 AdaBoost Classifier (ABC)
AdaBoost, a popular boosting algorithm, works by sequentially training classifiers and adjusting the weights of
instances based on their classification accuracy. Misclassified instances are given higher weight in subsequent
rounds, making the model focus more on difficult examples.
(11)
(12)
(13)
Adaptive weighting of misclassified cyberbullying texts enhances model focus on challenging abusive examples.
2.2.8 Multinomial Naïve Bayes Classifier
Jobs requiring the classification of texts and documents commonly employ MNBC. It assumes feature
independence and calculates the probability of a class given the words in the text. Its simplicity and speed make
it especially popular in cyberbullying detection tasks.
(14)
(15)
Word frequency probabilities enable fast and effective identification of harmful language using the Naïve Bayes
assumption.
2.3 Evaluation metrics
Metrics for Evaluation We employed Python-based machine learning tools to test each model's performance,
and the results were analyzed using a confusion matrix, which provides the following information:
• True Negatives (TN): Non-offensive tweets were correctly identified.
• False Positives (FP): Inaccurately labeling non-offensive tweets as offensive.
• False Negatives (FN): Derogatory tweets that the model failed to detect.
• True Positives (TP): recognized abusive tweets with accuracy.
We obtained a number of important performance measures from these values:
Accuracy shows the proportion of tweets that were deemed offensive that were truly offensive:
Accuracy =
(16)
Precision calculates the percentage of tweets that were successfully identified out of all the samples:
Precision=
(17)
Recall Reflects how well the model captured all actual offensive tweets:
Recall=
(18)
F1-score provides a single statistic that considers both precision as well as recall in a balanced way:
F1-score=2×Precision×Recall/(Precision + Recall) (19)
3. Results and discussions
For identifying and classifying instances of cyberbullying in online chatting, this study applies a number of ML
algorithm. Various preprocessing techniques were applied on dataset like tokenization, stop word removal and
stemming to increase the model performance. After this, we trained and evaluated a variety of classifiers to see
how well they identify harmful content.
The SGD Classifier performed the best overall out of all the models that were tested, earning high ratings for
accuracy, precision, recall, and F1-score. The SGD model consistently performed better than other algorithms,
as shown in the confusion matrix (Fig. 2). It worked especially well for categorizing content about bullying
across delicate categories like gender, sexual orientation, and religion. The confusion matrix provided additional
evidence of the model's ability to lower false positives and false negatives. In contrast, the Naive Bayes classifier
did worse, especially when it came to multi-class classification tasks.
The findings suggest that text-based feature engineering, combined with robust classification models, plays a
crucial role in detecting cyberbullying. The results highlight the importance of selecting suitable algorithms and
tuning hyperparameters to improve detection accuracy. These insights can guide the future development of real-
time cyberbullying detection systems on digital platforms.
Fig. 2: Confusion matrix.
Our other aim was to compare multiple algorithms on various parameters. Fig. 3 compares algorithms based on
accuracy, recall, precision, and f1 score. As we can see all algorithms were par with everyone but adaboost
classifier lacked precision so it was very much unsuitable for the task. Fig. 4 shows time complexity of
algorithms and we can see BG and RFC were very much time intensive algorithms. Hence these three were
already out of considerations. The detailed scores of SGD Classifier are as follows:
Accuracy: 92.81%
Precision: 96.97%
Recall: 91.94%
F1-Score: 94.39%
After this we condensed a sav file on this model and stores the vectorizer data in pickle file. These model and
vector files were used to predict any sentence as offensive or non-offensive. We then build a chatting webapp
and deployed this model and using rest api we were able to replicate cyberbullying detection in real time
chatting.
3.1 Issues and challenges
Through the course of study, we faced many challenges but two of them were notable. The first and foremost
issue we faced while conducting the study was data collection, data cleaning, and labelling. As we worked on
more than 35000 sentences for out model, labelling such huge data was a tedious task. We took help from
automated AI bots like ChatGPT but human intervention was necessary. Many times, the LLM model was
hallucinating by giving false positives, due to lack of understanding of conversational slang. So manually
verifying all the single line tweets took a generous amount of time. After this we embedded the data for better
model feeding. Any model works better with numbers rather than text. Embedding the text opened up various
things we can do like clustering the data and figuring out which cluster have most no of cyberbullying violations
and tracing it to actual text.
The second major challenge was while proposing a practical application, we need to take in consideration of
user privacy for detecting cyberbullying in chats or messages. To tackle this, we trained our model on one liner
data so that while detecting cyberbullying in real life scenario, the model will not be requiring complete chatting
context to detect cyberbullying. Instead, it can look up on single message itself. We even tried working on
nclusion because of lack of data and time.
Using encrypted data for detection further enhances its purpose while keeping real life scenarios in mind.
Fig. 3: Classification summary of algorithms.
Fig. 4: Time complexity of algorithms. (Some algorithms do not appear on the graph because they had lower
processing times)
3.2 Future scope
The fight against cyberbullying is ongoing, and this study opens several paths for future improvement. One
promising direction is using deep learning models like LSTM or BERT, which understand the meaning and
context of words better than traditional machine learning methods.
Utilizing information from many social media sites, like Facebook, Instagram, and Reddit, would be an additional
enhancement. This would help the model handle different ways people communicate across platforms and make
it more accurate for different age groups and communities.
Adding real-time detection through streaming APIs can allow the system to act quickly, even before serious
harm occurs. Support for multiple languages is also important, since cyberbullying happens in many languages,
not just English.
Linking the detection system with mental health support services or automated chatbots could help victims get
help immediately. Finally, creating user-friendly toolslike browser extensions or mobile apps-would make the
system easy to use and more accessible to everyone, helping to create a safer online space.
content. We can train models on text embedding and convert the text data in embeddings. These embeddings
could work like encryption, where the model and
cyberbullying detection function. These models could be deployed in a cloud database and using Rest Api
integrated with major online platforms like twitter, Instagram etc. LLMs could also be used, e.g. in every textbox
of online platform, in real time the LLM can process and warn the user about potential cyberbullying he/she
may be performing.
4. Conclusion
This study shows that machine learning classifiers are effective tools for detecting cyberbullying. By analyzing
both the content and context of online messages, these models can accurately identify harmful behavior. After
testing several machine learning models on our dataset, we discovered that the Stochastic Gradient Descent
Classifier (SGDC) worked best. It showed good accuracy and reliability, especially when tested using k-fold
cross-validation, which ensures the model performs well over numerous data samples. Overall, our system has
shown a strong ability to detect cyberbullying and offers a solid starting point for further development. With
future enhancements like deep learning, multilingual support, real-time detection, and integration with support
tools, this work lays the foundation for building safer, more respectful online communities.
Funding Declaration
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-
profit sectors.
Data Availability Statement
The datasets generated and/or analyzed during the current study that support the findings are available from
the corresponding author upon reasonable request.
Artificial Intelligence (AI) Use Disclosure
The authors declare that artificial intelligence (AI)-assisted tools were used only for language refinement,
grammar improvement, and manuscript structuring purposes during the preparation of this work. All technical
content, experimental implementation, results, and interpretations were independently developed and verified
by the authors.
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