Social Media and Online Islamophobia: A Hate Behavior Detection Model
Social Media and Online Islamophobia: A Hate Behavior Detection Model |
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© 2023 by IJETT Journal | ||
Volume-71 Issue-11 |
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Year of Publication : 2023 | ||
Author : Abdulwahab A. Almazroi, Asad A. Shah, Fathey Mohammed |
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DOI : 10.14445/22315381/IJETT-V71I11P203 |
How to Cite?
Abdulwahab A. Almazroi, Asad A. Shah, Fathey Mohammed, "Social Media and Online Islamophobia: A Hate Behavior Detection Model," International Journal of Engineering Trends and Technology, vol. 71, no. 11, pp. 27-32, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I11P203
Abstract
Since 9/11, the Muslim community has faced a lot of hatred towards them due to the rise in Islamophobia. Taking no measures to control Islamophobia can create fear among the Muslim community while at the same time giving others an open hand to spread hate and toxic remarks toward Muslims. While Muslim leaders and countries are taking measures to stop Islamophobia through awareness and building content to share Islam’s true peaceful and moderate image, it does not help control the spread of Islamophobia on social media platforms. In this regard, this research proposes a framework capable of detecting Islamophobic content. The proposed solution achieves this using natural language and artificial intelligence techniques such as keyword detection, tone analyzer, machine learning, impartiality ratio, and more. The proposed model is also capable of categorizing comments based on their severity and context. The research is hopeful that the proposed framework would allow experts to detect such posts causing Islamophobia early and report them so they can be taken down timely before being widespread. The successful completion of this research will not only have positive implications for the Muslim community but will also allow experts and researchers from other areas to use the same model in combating hateful and toxic speech on other platforms.
Keywords
Social Media, Detection, Islamophobia, Hate.
References
[1] Siti Rohaidah Ahmad et al., “A Review of Feature Selection and Sentiment Analysis Technique in Issues of Propaganda,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 11, pp. 240-245, 2019.
[Google Scholar] [Publisher Link]
[2] Ahmed Al Marouf et al., “Recognizing Language and Emotional Tone from Music Lyrics Using IBM Watson Tone Analyzer,” IEEE International Conference on Electrical, Computer and Communication Technologies, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Fatimah Alkomah, Sanaz Salati, and Xiaogang Ma, “A New Hate Speech Detection System based on Textual and Psychological Features,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 8, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Francisca Onaolapo Oladipo, Ogunsanya Funmilayo Blessing, and Ezendu Ariwa, “Terrorism Detection Model using Naive Bayes Classifier,” SSRG International Journal of Computer Science and Engineering, vol. 7, no. 12, pp. 9-15, 2020.
[CrossRef] [Publisher Link]
[5] V. Uma Maheswari, and R. Priya, “Analysis of Offensive Data over Multi-Source Social Media Environment Using Modified Random Forest Algorithm,” SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 9, pp. 63-71, 2023.
[CrossRef] [Publisher Link]
[6] Qasim Mehmood, Anum Kaleem, and Imran Siddiqi, “Islamophobic Hate Speech Detection from Electronic Media Using Deep Learning,” Mediterranean Conference on Pattern Recognition and Artificial Intelligence, pp. 187-200, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Nirali Arora et al., “Hinglish Profanity Filter and Hate Speech Detection,” International Journal of Computer Trends and Technology, vol. 71, no. 2, pp. 1-7, 2023.
[CrossRef] [Publisher Link]
[8] Rawan Abdullah Alraddadi, and Moulay Ibrahim El-Khalil Ghembaza, “Anti-Islamic Arabic Text Categorization Using Text Mining and Sentiment Analysis Techniques,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 8, pp. 776- 785, 2021.
[Google Scholar] [Publisher Link]
[9] Maha Jarallah Althobaiti, “BERT-based Approach to Arabic Hate Speech and Offensive Language Detection in Twitter: Exploiting Emojis and Sentiment Analysis,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Mohit Chandra et al., ““A Virus Has No Religion”: Analyzing Islamophobia on Twitter during the COVID-19 Outbreak,” Proceedings of the 32nd ACM Conference on Hypertext and Social Media, pp. 67-77, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Rehab Duwairi, Amena Hayajneh, and Muhannad Quwaider, “A Deep Learning Framework for Automatic Detection of Hate Speech Embedded in Arabic Tweets,” Arabian Journal for Science and Engineering, vol. 46, no. 4, pp. 4001-4014, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Felipe González-Pizarro, and Savvas Zannettou, “Understanding and Detecting Hateful Content Using Contrastive Learning,” Proceedings of the Seventeenth International AAAI Conference on Web and Social Media, vol. 17, pp. 257-268, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Jeffrey W. Howard, “Free Speech and Hate Speech,” Annual Review of Political Science, vol. 22, pp. 93-109, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Secunder Kermani, Could a Student's Death Change Pakistan's Blasphemy Laws?, 2017.
[Google Scholar] [Publisher Link]
[15] Heena Khan, and Joshua L. Phillips, “Language Agnostic Model: Detecting Islamophobic Content on Social Medi,” Proceedings of the 2021 ACM Southeast Conference, pp. 229-233, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Sean MacAvaney et al., “Hate Speech Detection: Challenges and Solutions,” PloS One, vol. 14, no. 8, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Toni M. Massaro, “Equality and Freedom of Expression: The Hate Speech Dilemma,” William and Mary Law Review, vol. 32, 1990.
[Google Scholar] [Publisher Link]
[18] Qasim Mehmood, Anum Kaleem, and Imran Siddiqi, “Islamophobic Hate Speech Detection from Electronic Media Using Deep Learning,” Mediterranean Conference on Pattern Recognition and Artificial Intelligence, pp. 187-200, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Raymond T. Mutanga, Nalindren Naicker, and Oludayo O. Olugbara, “Detecting Hate Speech on Twitter Network Using Ensemble Machine Learning,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 3, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Fachrul Kurniawan, Badruddin, and Aji Prasetya Wibawa, “Identification of Islamophobia Sentiment Analysis on Twitter Using Text Mining Language Detection,” Journal of Positive School Psychology, vol. 6, no. 5, pp. 8286-8294, 2022.
[Google Scholar] [Publisher Link]
[21] Statistica, Number of Social Network Users Worldwide from 2017 to 2025, 2022. [Online]. Available: https://www.statista.com/statistics/278414/number-of-worldwide-social-network users/#:~:text=How%20many%20people%20use%20social,almost%204.41%20billion%20in%202025
[22] Luis Enrique Argota Vega et al., “Mineriaunam at Semeval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial Framework,” Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 447-452, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[23] B. Vidgen, Tweeting Islamophobia, University of Oxford, 2019.
[Google Scholar] [Publisher Link]
[24] Bertie Vidgen, and Taha Yasseri, “Detecting Weak and Strong Islamophobic Hate Speech on Social Media,” Journal of Information Technology and Politics, vol. 17, no. 1, pp. 66-78, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Wenjie Yin, and Arkaitz Zubiaga, “Towards Generalisable Hate Speech Detection: A Review on Obstacles and Solutions,” PeerJ Computer Science, vol. 7, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Heena Khan, and Joshua L. Phillips, “Language Agnostic Model: Detecting Islamophobic Content on Social Media,” Proceedings of the 2021 ACM Southeast Conference (ACM SE '21), pp. 229-233, 2021.
[CrossRef] [Google Scholar] [Publisher Link]