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Cited by 30Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningSpam and Phishing Detection

A Multitask Multimodal Framework for Sentiment and Emotion-Aided Cyberbullying Detection

Maity, Krishanu, Kumar, Abhishek, Saha, Sriparna·2022·IEEE Internet Computing
DOI 10.1109/mic.2022.3158583
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Abstract

Cyberbullying has become more widespread, especially among teens with the growth of the digital sphere and advancement of technology. This article is the first attempt in investigating the role of sentiment and emotion information for identifying cyberbullying in the Indian scenario. From Twitter, a benchmark Hind–English code-mixed corpus called BullySentEmo has been developed as there was no dataset available labeled with bully, sentiment, and emotion. The developed dataset consists of both the modalities, tweet- text and emoji. In India, the majority of communication on different social media platforms is based on Hindi and English, and language switching is a common practice in digital communication. A multitask multimodal framework called MT-MM-Bert+VecMap based on BERT and VecMap embedding schemes with emoji modality, has been developed. Our proposed multitask-multimodal framework outperforms all the single task and unimodal baselines with the highest accuracy values of 82.05(+/- 1.36)%, 77.87(+/- 1.93)%, and 58.05(+/-2.78)% for the cyberbully detection task, sentiment analysis task, and emotion recognition task, respectively.

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Crossref · 10.1109/mic.2022.3158583

Cites

32 of 32 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The gated Recurrent Units with Attention (GRU-Att) model has demonstrated exciting results in detecting bullying behavior in social media text.

    “The gated Recurrent Units with Attention (GRU-Att) model has also demonstrated exciting results in detecting bullying behavior in a social media text.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [124]

  • GRU-Att can more accurately interpret and categorize the behavior of cyberbullying on social media platforms.

    “This makes it possible for the model to more accurately interpret and categorize the behavior of cyberbullying on social media platforms [71,123,124].”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [124]

  • GRU-Att can capture long-term dependencies in sequential data faster than the LSTM model while capturing the important part of the input text.

    “The model can capture long-term dependencies in sequential data faster than the LSTM model while capturing the important part of the input text by combining Gated Recurrent Units (GRUs) with attention mechanisms.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [124]

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Related

Authors
Maity, K., Kumar, A., & Saha, S.
Affiliations
Indian Institute of Technology Patna (IN)
ORCID
Venue
IEEE Internet Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-07-01
Volume
26
Issue
4
Pages
68-78
ISSN
  • 1089-7801 (ISSN-L)
  • 1941-0131
DOI
10.1109/mic.2022.3158583
OpenAlex
W4226429277
Cited by
30
References
32
Citations by year
2026: 3 · 2025: 11 · 2024: 3 · 2023: 10 · 2022: 3
FWCI
2.22
Type
journal article
Language
en
Open access
closed
Licence
Locations
IEEE Internet Computing (publishedVersion)
Funders
  • Electronics and IT, Ministry of Electronics and Information Technology
  • Department of Home Affairs
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 › Information Systems › Spam and Phishing Detection
Keywords
cyberbullying detection, sentiment analysis, emotion recognition, multimodal learning, multi-task learning, Hindi-English code-mixing, Twitter, BERT

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