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All Journal Journal International of Lingua and Technology Journal International Dakwah and Communication
Xiang, Yang
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Education Languange, Linguistic, Communication & Media Social Sciences Other

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Islamic Journalism and the Challenges of Objectivity Ilyas, Sanusi; Xiang, Yang; Wei, Sun
Journal International Dakwah and Communication Vol. 4 No. 2 (2024)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jidc.v4i2.763

Abstract

Islamic journalism operates within a framework that integrates ethical and religious principles, presenting distinct challenges in maintaining objectivity. The tension between upholding journalistic neutrality and adhering to Islamic ethical guidelines raises questions about bias, credibility, and professional standards in Islamic media practices. This study aims to analyze the complexities of objectivity in Islamic journalism by examining its theoretical foundations, ethical boundaries, and practical applications in contemporary media landscapes. Employing a qualitative approach, this research utilizes content analysis and in-depth interviews with journalists from various Islamic media outlets. The findings reveal that while Islamic journalism strives for truthfulness and fairness, it often navigates ideological influences and societal expectations that shape reporting styles. Additionally, structural limitations, editorial policies, and political factors further challenge the realization of absolute objectivity. This study concludes that while complete neutrality may be unattainable, Islamic journalism can enhance credibility by promoting balanced reporting, ethical transparency, and adherence to professional journalism standards. These insights contribute to the discourse on media ethics, highlighting the need for frameworks that align Islamic values with universal journalistic principles.  
Multimodal Sentiment Analysis in Indonesian: A Comparative Study of Deep Learning Models for Hate Speech Detection on Social Media Muhammadiyah, Mas’ud; Xiang, Yang; Na, Li; Nishida, Daiki; Prayudani, Santi
Journal International of Lingua and Technology Vol. 4 No. 1 (2025)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/jiltech.v4i1.824

Abstract

With the rapid expansion of social media, the prevalence of hate speech has become a critical issue, particularly in the context of Indonesian language and culture. The detection of hate speech in social media platforms is a complex task due to the multimodal nature of online communication, where text, images, and videos are often combined to express sentiments. This study aims to explore and compare deep learning models for multimodal sentiment analysis, focusing on their effectiveness in detecting hate speech in Indonesian social media content. By analyzing both textual and visual data, the study seeks to enhance the accuracy of sentiment classification, specifically identifying instances of hate speech. The research employs several state-of-the-art deep learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-based models, to perform sentiment analysis on a multimodal dataset. The dataset includes text and images from Indonesian social media posts, labeled for hate speech detection. The results show that multimodal models outperform text-only models, with the Transformer-based model yielding the highest accuracy and F1-score in detecting hate speech. The inclusion of visual data significantly improved the model’s ability to classify complex and subtle expressions of hate speech. This study concludes that multimodal deep learning models offer a promising solution for detecting hate speech in Indonesian social media, with implications for better content moderation and online safety.
Co-Authors Hui, Zhou Ilyas, Sanusi Muhammadiyah, Mas’ud Na, Li Nishida, Daiki Prayudani, Santi Siti Nur Azizah, Siti Nur Sun Wei