Detecting twitter hate speech in COVID-19 era using machine learning and ensemble learning techniques

Khanday, Akib Mohi Ud Din; Rabani, Syed Tanzeel; Khan, Qamar Rayees; Malik, Showkat Hassan

doi:10.1016/j.jjimei.2022.100120

Cited by 34 publications

(20 citation statements)

References 35 publications

(41 reference statements)

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“…Ensemble techniques in the context of HSD represent a sophisticated approach to improving the accuracy and robustness of models used to identify and combat harmful online content focusing on people or groups according to protected traits (Khanday et al, 2022; Ombui et al, 2019). Ensemble techniques are particularly valuable because they combine the predictions of multiple ML models to make collective decisions that are often more accurate and reliable than those of individual models.…”

Section: Discussionmentioning

confidence: 99%

“…The scope of research investigations further encompasses expressions of antagonism targeting marginalized groups, notably immigrants, as documented by the works of Irani et al (2021) MacAvaney et al (2019), Qureshi and Sabih (2021), and Zhou et al (2020), along with discussions relating to women, as outlined by Sharma and Shrivastava (2018), Zhou et al (2020), and Irani et al (2021). Hate speech founded on considerations of race and ethnicity also comes under the purview of these studies, evident in the research of MacAvaney et al (2019), Watanabe et al (2018), Sajid et al (2020), Sharma and Shrivastava (2018), Zhang and Luo (2018), Pronoza et al (2021), and Baydogan and Alatas (2021) and those addressing hate speech within the framework of the COVID‐19 outbreak, such as Khanday et al (2022) and Ahmed and Lin (2022).…”

Section: Discussionmentioning

confidence: 99%

“…Race (Mathew et al, n.d.;Alatawi et al, 2021;Chiu et al, 2021;MacAvaney et al, 2019;Sajid et al, 2020;Shannaq et al, 2022;Sharma & Shrivastava, 2018;Zhang & Luo, 2018) Religion (Sajid et al, 2020;Zhang & Luo, 2018), (Mathew et al, n.d.;Ali et al, 2021;Mozafari et al, 2022;Qureshi & Sabih, 2021) Ethnicity (Ali et al, 2021;Ghosh et al, 2023;Mozafari et al, 2022;Pronoza et al, 2021;Watanabe et al, 2018) Sexist (Al-Hassan & Al-Dossari, 2022;Baydogan & Alatas, 2021;Chiu et al, 2021;MacAvaney et al, 2019;Mozafari et al, 2022;Qureshi & Sabih, 2021;Shannaq et al, 2022) Political (Govers et al, 2023;Oriola & Kotze, 2020;Plaza-Del-Arco et al, 2021;Ribeiro et al, 2017;Wang et al, 2022) Women and immigrants (Irani et al, 2021;MacAvaney et al, 2019;Zhou et al, 2020) Covid-19 (Ahmed & Lin, 2022;Khanday et al, 2022;Su et al, 2023) Gender (Ghosh et al, 2023) Crime (Mathew et al, n.d.) and those addressing hate speech within the framework of the COVID-19 outbreak, such as Khanday et al (2022) and…”

Section: Target Studymentioning

confidence: 99%

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Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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The realm of Natural Language Processing and Text Mining has seen a surge in interest from researchers in hate speech detection, leading to an increase in related studies. This analysis aims to create a valuable resource by summarizing the methods and strategies used to combat hate speech in social media. We perform a detailed review to achieve a deep knowledge of the hate speech detection landscape from 2018 to 2023, revealing global incidents of hate speech in 2022–2023. Sixty‐six relevant articles were selected for this review. Existing studies were analyzed and categorized into five method categories: Machine Learning, Deep Learning, Ensemble models, Graph Neural Networks, and Graph Convolutional Networks. These advancements can aid social networking services in identifying hate messages before being posted, reducing the risk of harassment. The review also covers available hate speech datasets and highlights research challenges, but it is clear that a definitive solution to this problem is yet to be found. Future research directions are recommended to address the ongoing challenges in Hate Speech Detection.This article is categorized under: Applications of Computational Statistics > Computational Linguistics Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Classification and Regression Trees (CART) Statistical Learning and Exploratory Methods of the Data Sciences > Text Mining

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Target Studymentioning

confidence: 99%

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Hate speech detection in social media: Techniques, recent trends, and future challenges

Rawat,

Kumar,

Samant

2024

WIREs Computational Stats

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“…They found that a combination of NLP and ML helps to detect hate speech more accurately. Khanday, and Rabani [12] used manually annotated tweets collected during the COVID-19 pandemic and ensemble machine-learning methods to detect hate speech. They concluded that the Decision Tree classifier is the most effective compared to other ML methods.…”

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 [ 29 ], authors proposed the use of ensemble learning and machine learning techniques for identifying hate speech during the COVID-19 pandemic. Twitter data was collected by using trending hashtags and Twitter Application Programming Interface (API).…”

Section: Related Workmentioning

confidence: 99%

Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches

2023

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Since the spread of the coronavirus flu in 2019 (hereafter referred to as COVID-19), millions of people worldwide have been affected by the pandemic, which has significantly impacted our habits in various ways. In order to eradicate the disease, a great help came from unprecedentedly fast vaccines development along with strict preventive measures adoption like lockdown. Thus, world wide provisioning of vaccines was crucial in order to achieve the maximum immunization of population. However, the fast development of vaccines, driven by the urge of limiting the pandemic caused skeptical reactions by a vast amount of population. More specifically, the people’s hesitancy in getting vaccinated was an additional obstacle in fighting COVID-19. To ameliorate this scenario, it is important to understand people’s sentiments about vaccines in order to take proper actions to better inform the population. As a matter of fact, people continuously update their feelings and sentiments on social media, thus a proper analysis of those opinions is an important challenge for providing proper information to avoid misinformation. More in detail, sentiment analysis (Wankhade et al. in Artif Intell Rev 55(7):5731–5780, 2022. https://doi.org/10.1007/s10462-022-10144-1) is a powerful technique in natural language processing that enables the identification and classification of people feelings (mainly) in text data. It involves the use of machine learning algorithms and other computational techniques to analyze large volumes of text and determine whether they express positive, negative or neutral sentiment. Sentiment analysis is widely used in industries such as marketing, customer service, and healthcare, among others, to gain actionable insights from customer feedback, social media posts, and other forms of unstructured textual data. In this paper, Sentiment Analysis will be used to elaborate on people reaction to COVID-19 vaccines in order to provide useful insights to improve the correct understanding of their correct usage and possible advantages. In this paper, a framework that leverages artificial intelligence (AI) methods is proposed for classifying tweets based on their polarity values. We analyzed Twitter data related to COVID-19 vaccines after the most appropriate pre-processing on them. More specifically, we identified the word-cloud of negative, positive, and neutral words using an artificial intelligence tool to determine the sentiment of tweets. After this pre-processing step, we performed classification using the BERT + NBSVM model to classify people’s sentiments about vaccines. The reason for choosing to combine bidirectional encoder representations from transformers (BERT) and Naive Bayes and support vector machine (NBSVM ) can be understood by considering the limitation of BERT-based approaches, which only leverage encoder layers, resulting in lower performance on short texts like the ones used in our analysis. Such a limitation can be ameliorated by using Naive Bayes and Support Vector Machine approaches that are able to achieve higher performance in short text sentiment analysis. Thus, we took advantage of both BERT features and NBSVM features to define a flexible framework for our sentiment analysis goal related to vaccine sentiment identification. Moreover, we enrich our results with spatial analysis of the data by using geo-coding, visualization, and spatial correlation analysis to suggest the most suitable vaccination centers to users based on the sentiment analysis outcomes. In principle, we do not need to implement a distributed architecture to run our experiments as the available public data are not massive. However, we discuss a high-performance architecture that will be used if the collected data scales up dramatically. We compared our approach with the state-of-art methods by comparing most widely used metrics like Accuracy, Precision, Recall and F-measure. The proposed BERT + NBSVM outperformed alternative models by achieving 73% accuracy, 71% precision, 88% recall and 73% F-measure for classification of positive sentiments while 73% accuracy, 71% precision, 74% recall and 73% F-measure for classification of negative sentiments respectively. These promising results will be properly discussed in next sections. The use of artificial intelligence methods and social media analysis can lead to a better understanding of people’s reactions and opinions about any trending topic. However, in the case of health-related topics like COVID-19 vaccines, proper sentiment identification could be crucial for implementing public health policies. More in detail, the availability of useful findings on user opinions about vaccines can help policymakers design proper strategies and implement ad-hoc vaccination protocols according to people’s feelings, in order to provide better public service. To this end, we leveraged geospatial information to support effective recommendations for vaccination centers.

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Detecting twitter hate speech in COVID-19 era using machine learning and ensemble learning techniques

Cited by 34 publications

References 35 publications

Hate speech detection in social media: Techniques, recent trends, and future challenges

Hate speech detection in social media: Techniques, recent trends, and future challenges

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

Vaccine sentiment analysis using BERT + NBSVM and geo-spatial approaches

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