Detecting Hate Speech on Twitter Network using Ensemble Machine Learning

Mutanga, Raymond T; Naicker, Nalindren; Olugbara, Oludayo O.

doi:10.14569/ijacsa.2022.0130341

Cited by 8 publications

(4 citation statements)

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“…SVM has excellent performance in processing fairly high-dimensional data, as well as the ability to generalize better [28]. The LR algorithm is used to train a multi-level classification to separate speech that contains hate, offensive sentences, and those that do not contain hate elements [41]. These performance results can be observed in Table 2.…”

Section: Results Of Model Evaluationmentioning

confidence: 99%

Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm

Damayanti,

Prameswari,

Puspita

et al. 2024

J. Inf. Syst. Explor. Res.

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This research was conducted to increase accuracy in classifying sentences containing hate speech and non-hate speech on Twitter. This is important to do because, as technology develops, it also comes with negative impacts, one of which is hate speech. This classification is carried out using a combination of Logistic Regression (LR) and Support Vector Machine (SVM) methods. This combination is based on the ease of implementation and speed of LR as well as SVM's ability to handle more complex and non-linear data. In this context, LR is used to model the probability that a comment on Twitter contains hate elements or not. The model can then provide probability predictions for each class, and a threshold can be set to determine the final class. This research shows that combining these methods can build a good classification model with an accuracy of 96%.

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Section: Results Of Model Evaluationmentioning

confidence: 99%

Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm

Damayanti,

Prameswari,

Puspita

et al. 2024

J. Inf. Syst. Explor. Res.

View full text Add to dashboard Cite

show abstract

“…They obtained the best results with a support vector machine (SVM). Mutanga, and Naicker [10] employed ensemble machine methods (decision trees and SVM) for automatic detection of hate speech in tweets. They found that these methods outperform classical machine learning methods that suffer from high variance.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Social sensing with big data: Detecting hate speech in social media

Umar Ibrahim,

Usman Lawal Gulma,

Ishaq Abdullahi Lawal

2024

Int. J. Sci. Res. Arch.

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The internet's accessibility and social media platforms, like Facebook and Twitter, have accelerated the spread of hate speech and fake news, both of which can be detrimental to society's overall well-being. Identifying and tracking hate speech is becoming increasingly difficult for the public, private citizens, legislators, and academics. Despite efforts to leverage automatic detection and monitoring techniques, their performances are still far from satisfactory. This study employs Natural Language Processing (NLP) and Machine Learning (ML) approaches to detect hate speech for decision-making. The result showed that the Support Vector Machine (SVM) algorithm has the best performance with an accuracy of 0.86 compared to the Random Forest with 0.8 accuracy. The manual evaluation of the performance of our algorithm yielded an inter-annotator agreement Cronbach’s alpha (α = .775).

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“…In particular, manually engineered features fail to effectively capture the semantic and domain-specific representations of text documents [10][11][12]. Furthermore, individual classical algorithms have been criticised for their susceptibility to high variance, thereby negatively impacting predictive efficacy [13].…”

Section: Introductionmentioning

confidence: 99%

“…To address the aforementioned challenges associated with individual classical algorithms, other scholars have investigated the technique of ensemble learning for hate speech detection. Mutanga, et al [13] combined Logistic Regression, Decision Trees, and Support Vector Machines using Voting to detect hate speech on Twitter. Their proposed ensemble approach outperformed Individual algorithms trained on the same dataset.…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis of Deep Learning for Social Media Hate Speech Detection

Mutanga,

Olugbara,

Naicker

2023

J. Inf. Syst. Informatics

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Social media has become an important web technology for creating and sharing information plus enhancing business reputations worldwide. However, the anonymity accorded by social media platforms has been cryptically vituperated to spread horrendous content such as hate speech. Recently, researchers have been progressively gravitating towards the use of deep learning techniques to address the problem of social media hate speech detection. This study provides bibliometric analysis and mapping of the existing literature on hate speech detection using deep learning algorithms. The study used articles published between 2016 and 2022 from the Scopus database, while Vos Viewer, Biblioshiny, and Panda’s software tools were employed for the bibliometric analysis. The research explored the yearly trajectory of recent publications, dominant countries, collaborative institutions, sources of primary studies that have employed deep learning for hate speech detection, and the intellectual and social structures of the research constituents. It has been observed that the literature on hate speech detection is rapidly growing, but research output and collaborations from the developing countries of the world are still limited. The findings of this study provide insights into the intellectual structure and advancements in deep learning applications for hate speech detection while identifying research gaps for future work.

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Detecting Hate Speech on Twitter Network using Ensemble Machine Learning

Cited by 8 publications

References 0 publications

Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm

Classification of Hate Comments on Twitter Using a Combination of Logistic Regression and Support Vector Machine Algorithm

Social sensing with big data: Detecting hate speech in social media

Bibliometric Analysis of Deep Learning for Social Media Hate Speech Detection

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