Correction to: Abusive language detection from social media comments using conventional machine learning and deep learning approaches

Akhter, Muhammad Pervez; Zheng, Jiangbin; Naqvi, Irfan Raza; Abdelmajeed, Mohammed; Zia, Tehseen

doi:10.1007/s00530-021-00819-0

Cited by 7 publications

(1 citation statement)

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“…Then the instructional media e-booklet are distributed to the students to be installed on mobile each phones. The e-booklet learning media can be used for appropriate learning media for students, both in learning activities with teachers or learning independently (Akhter et al, 2021).…”

Section: Table 3 the Value Of N-gain In The Experimental Class On Stu...mentioning

confidence: 99%

Development of Learning Media for E-Booklet Human Reproductive System Materials to Improve Cognitive Learning Outcomes of High School Students

Eliana¹,

Sunardi²,

Susanto³

2022

Journal Of Biology Education Research (JBER)

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This study aims to develop a learning media E-Booklet for reproductive system materials to improve students' cognitive learning outcomes in biology subjects for the human reproductive system. This research was conducted from December 2020 to June 2021. The trial in this study consisted of one class using the model one group pretest- posttest design. The results of the validation related to the feasibility of the media E-Booklet obtained a score of 91% in the Very Valid category. The results of the pretest and posttest scores of class XI IPA-1 students in improving students' cognitive learning outcomes. This is indicated by the average value of the pretest before using the learning media, which is 33. While the average posttest after using the learning media is 87 with a Biology minimum completeness criteria value of 72. The N-Gain value has an average of 0.81 or in N Gain% is 81% indicating high category. This shows that the learning media E-Booklet material for the human reproductive system can improve students' cognitive learning outcomes.

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Section: Table 3 the Value Of N-gain In The Experimental Class On Stu...mentioning

confidence: 99%

Development of Learning Media for E-Booklet Human Reproductive System Materials to Improve Cognitive Learning Outcomes of High School Students

Eliana¹,

Sunardi²,

Susanto³

2022

Journal Of Biology Education Research (JBER)

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Deep Learning-Based Cyberbullying Detection in Kurdish Language

Badawi

2024

The Computer Journal

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Cyberbullying is a significant concern in this digital age due to its harmful effects on individuals and society. Sadly, social media platforms have only exacerbated the problem, making it imperative to find effective ways to identify and prevent offensive content. While previous research has extensively focused on English and explored machine learning techniques to tackle this issue. To address this gap, this paper introduces a new hybrid deep learning model called Gray Wolf Algorithm-convolutional neural network (GWA-CNN), explicitly designed to detect cyberbullying in the Kurdish language on Twitter. The proposed model combines the CNN framework with an optimised GWA version to improve CNN’s parameters and reduce training time. We evaluated GWA-CNN thoroughly utilizing the first-ever manually annotated Kurdish dataset of 30k tweets that have been meticulously curated and divided into three categories, namely sexism, racism and neutral expressions, and compared its performance to those of state-of-the-art algorithms such as Naïve Bayes, K-Nearest Neighbors, Recurrent Neural Networks, Gated Recurrent Units and attention-based transformer. The experimental results demonstrate that GWA-CNN exhibited superior performance in all scenarios, outperforming other approaches in detecting cyberbullying on Twitter.

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DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

et al. 2022

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Cyberbullying (CB) has become increasingly prevalent in social media platforms. With the popularity and widespread use of social media by individuals of all ages, it is vital to make social media platforms safer from cyberbullying. This paper presents a hybrid deep learning model, called DEA-RNN, to detect CB on Twitter social media network. The proposed DEA-RNN model combines Elman type Recurrent Neural Networks (RNN) with an optimized Dolphin Echolocation Algorithm (DEA) for finetuning the Elman RNN's parameters and reducing training time. We evaluated DEA-RNN thoroughly utilizing a dataset of 10000 tweets and compared its performance to those of state-of-the-art algorithms such as Bi-directional long short term memory (Bi-LSTM), RNN, SVM, Multinomial Naive Bayes (MNB), Random Forests (RF). The experimental results show that DEA-RNN was found to be superior in all the scenarios. It outperformed the considered existing approaches in detecting CB on Twitter platform. DEA-RNN was more efficient in scenario 3, where it has achieved an average of 90.45% accuracy, 89.52% precision, 88.98% recall, 89.25% F1-score, and 90.94% specificity.

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Correction to: Abusive language detection from social media comments using conventional machine learning and deep learning approaches

Cited by 7 publications

References 0 publications

Development of Learning Media for E-Booklet Human Reproductive System Materials to Improve Cognitive Learning Outcomes of High School Students

Development of Learning Media for E-Booklet Human Reproductive System Materials to Improve Cognitive Learning Outcomes of High School Students

Deep Learning-Based Cyberbullying Detection in Kurdish Language

DEA-RNN: A Hybrid Deep Learning Approach for Cyberbullying Detection in Twitter Social Media Platform

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