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Cited by 56Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningMental Health via Writing

Cyberbullying detection from tweets using deep learning

Bharti, Shubham, Yadav, Arun Kumar, Kumar, Mohit, Yadav, Divakar·2022·Kybernetes
DOI 10.1108/k-01-2021-0061
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Abstract

Purpose With the rise of social media platforms, an increasing number of cases of cyberbullying has reemerged. Every day, large number of people, especially teenagers, become the victim of cyber abuse. A cyberbullied person can have a long-lasting impact on his mind. Due to it, the victim may develop social anxiety, engage in self-harm, go into depression or in the extreme cases, it may lead to suicide. This paper aims to evaluate various techniques to automatically detect cyberbullying from tweets by using machine learning and deep learning approaches. Design/methodology/approach The authors applied machine learning algorithms approach and after analyzing the experimental results, the authors postulated that deep learning algorithms perform better for the task. Word-embedding techniques were used for word representation for our model training. Pre-trained embedding GloVe was used to generate word embedding. Different versions of GloVe were used and their performance was compared. Bi-directional long short-term memory (BLSTM) was used for classification. Findings The dataset contains 35,787 labeled tweets. The GloVe840 word embedding technique along with BLSTM provided the best results on the dataset with an accuracy, precision and F1 measure of 92.60%, 96.60% and 94.20%, respectively. Research limitations/implications If a word is not present in pre-trained embedding (GloVe), it may be given a random vector representation that may not correspond to the actual meaning of the word. It means that if a word is out of vocabulary (OOV) then it may not be represented suitably which can affect the detection of cyberbullying tweets. The problem may be rectified through the use of character level embedding of words. Practical implications The findings of the work may inspire entrepreneurs to leverage the proposed approach to build deployable systems to detect cyberbullying in different contexts such as workplace, school, etc and may also draw the attention of lawmakers and policymakers to create systemic tools to tackle the ills of cyberbullying. Social implications Cyberbullying, if effectively detected may save the victims from various psychological problems which, in turn, may lead society to a healthier and more productive life. Originality/value The proposed method produced results that outperform the state-of-the-art approaches in detecting cyberbullying from tweets. It uses a large dataset, created by intelligently merging two publicly available datasets. Further, a comprehensive evaluation of the proposed methodology has been presented.

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  • Bharti et al. [150] found Bi-LSTM outperformed other deep learning models on Twitter dataset with accuracy, precision, and F1 scores of 92.60%, 96.60%, and 94.20%.

    “Bharti et al. [150] applied different deep learning models over the Twitter dataset and found that Bi-LSTM outperformed other deep learning models with accuracy, precision, and F1 scores of 92.60%, 96.60%, and 94.20%, respectively.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [150]

  • Several deep-learning-based cyberbullying detection methods have been conducted using individual models and presenting performance with popular evaluation metrics.

    “Several deep-learning-based cyberbullying detection methods have been conducted by using individual models and presenting the performance of the models with popular evaluation metrics [21,134,149,150].”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [150]

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Related

Authors
Bharti, S., Yadav, A. K., Kumar, M., & Yadav, D.
Affiliations
National Institute of Technology Hamirpur (BD)
ORCID
Venue
Kybernetes
Publisher
Emerald
Published
2021-07-13
Volume
51
Issue
9
Pages
2695-2711
ISSN
0368-492X (ISSN-L)
DOI
10.1108/k-01-2021-0061
OpenAlex
W3181048959
Cited by
56
References
40
Citations by year
2026: 7 · 2025: 10 · 2024: 13 · 2023: 20 · 2022: 6
FWCI
3.45
Type
journal article
Language
en
Open access
closed
Licence
www.emerald.com/insight/site-policies (tdm)
Locations
Kybernetes (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Keywords
cyberbullying detection, tweets, GloVe embeddings, bidirectional LSTM, text classification, out-of-vocabulary words, character embeddings, social media abuse, adolescent mental health

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