Food Communication Sentiment Analysis on Free Nutritious Meal Program's: From Negative Bias to Policy Legitimacy

Authors

  • Rila Setyaningsih Universitas Mercu Buana Yogyakarta, Indonesia
  • Didik Haryadi Santoso Universitas Mercu Buana Yogyakarta, Indonesia
  • Noor Afy Shovmayanti Universitas Muhammadiyah Klaten, Indonesia

Keywords:

Digital food communication;, MBG program’s;, polarization;, social media;, social network analysis

Abstract

The rapid development of digital technology, particularly Artificial Intelligence (AI), has significantly transformed communication practices, including the field of da’wah. This study aims to examine the integration of Islamic communication ethics in the utilization of AI for da’wah in the digital era. Employing a qualitative approach with a literature review method, this research analyzes various scholarly sources related to Islamic communication ethics, digital da’wah, and AI technology. The findings reveal that while AI offers substantial opportunities in expanding the reach, efficiency, and personalization of da’wah messages, it also presents ethical challenges such as the risk of misinformation, distortion of religious messages, loss of humanistic values, and weakened scholarly authority. In response, Islamic communication ethics grounded in principles such as qaulan sadidan (truthfulness), qaulan layyinan (gentleness), qaulan balighan (effectiveness), and tabayyun (verification) serve as essential guidelines to ensure that AI-based da’wah remains accurate, ethical, and aligned with Islamic values. The study concludes that the integration of ethical principles with technological innovation is crucial, where AI functions as a supportive tool rather than a replacement for human da’i. This integration not only enhances the effectiveness of da’wah but also preserves its moral and spiritual integrity in the digital age.

References

Ahmed, Cherry; Elkorany, Abeer; Sayed, Eman El. (2022). Prediction of Customer’s Perception in Social Networks by Integrating Sentiment Analysis and Machine Learning. (2022). https://doi.org/10.21203/rs.3.rs-1576053/v1

Alnasrawi, A. M., alzubaidi, A. M. N., & Al-Moadhen, A. A. (2024). Improving sentiment analysis using text network features within different machine learning algorithms. Buletin Teknik Elektro Dan Informatika. https://doi.org/10.11591/eei.v13i1.5576

Brugnoli, E., Cinelli, M., Quattrociocchi, W., & Scala, A. (2019). Recursive patterns in online echo chambers. arXiv: Physics and Society. https://doi.org/10.1038/S41598-019-56191-7

Citrawijaya, O. R., & Jannah, R. (2025). The Influence of Social Media Influencers on Public Opinion Formation Regarding Environmental and Climate Change Issues. 2(6), 856–863. https://doi.org/10.71364/wyxy4607

Gamage, A. N. K. K. (2025). A comparative analysis of qualitative and mixed methods research: Strengths, limitations, and practical applications. World Journal Of Advanced Research and Reviews, 25(3), 2040–2046. https://doi.org/10.30574/wjarr.2025.25.3.0947

Germani, F., & Biller-Andorno, N. (2022). How to counter the anti-vaccine rhetoric: Filling information voids and building resilience. Human Vaccines & Immunotherapeutics, 18(6). https://doi.org/10.1080/21645515.2022.2095825

Gommeh, E. (2022). Processed food dream or nightmare? Influential online sentiment coalitions. NJAS: Impact in Agricultural and Life Sciences, 94(1), 80–111. https://doi.org/10.1080/27685241.2022.2108731

Hassani, H., Komendantova, N., Rovenskaya, E. A., & Yeganegi, M. R. (2024). Unveiling the Waves of Mis and Disinformation from Social Media. International Journal of Modeling, Simulation, and Scientific Computing. https://doi.org/10.1142/s1793962324500338

Jiang, T. (2022). Studying opinion polarization on social media. Social Work and Social Welfare, 4(2), 232–241. https://doi.org/10.25082/swsw.2022.02.003

Latif, D. A., Samad, M. A., Rinawulandari, R., & Abd Kadir, S. (2024). Social Media in Shaping Public Opinion Roles and Impact: A Systematic Review. Jurnal Komunikasi: Malaysian Journal of Communication, 40(2), 205–223. https://doi.org/10.17576/jkmjc-2024-4002-12

Mann, A. (2017). Hashtag activism and the right to food in Australia (pp. 168–184). Routledge. https://doi.org/10.4324/9781315109930-9

Matagi, S. O. (2024). Combating Public Health Infodemics: Strategies for Misinformation Control and Evidence-based Communication. Journal of Advances in Medicine and Medical Research. https://doi.org/10.9734/jammr/2024/v36i105582

McBeth, M. K., Shanahan, E. A., Arrandale Anderson, M. C., & Rose, B. (2012). Policy Story or Gory Story? Narrative Policy Framework Analysis of Buffalo Field Campaign’s YouTube Videos. Policy & Internet, 4, 159–183. https://doi.org/10.1002/POI3.15

Niederdeppe, J., Boyd, A. D., King, A. J., & Rimal, R. N. (2024). Strategies for Effective Public Health Communication in a Complex Information Environment. Annual Review of Public Health. https://doi.org/10.1146/annurev-publhealth-071723-120721

Pereira, Bruna Abreu E Lima Guedes. (2023). A influência das redes sociais no comportamento alimentar dos consumidores: análise do papel dos influenciadores digitais e das comunidades virtuais (The influence of social media on consumer eating behavior: an analysis of the role of digital influencers and virtual communities). https://doi.org/10.17771/pucrio.acad.65440

Rahman, M. A., Hossan, M. Z., Arif, M., & Biswas, M. N. (2024). Predicting Customer Sentiment in Social Media Interactions: Analyzing Amazon Help Twitter Conversations Using Machine Learning. International Journal of Advanced Science Computing and Engineering, 6(2), 52–56. https://doi.org/10.62527/ijasce.6.2.211

Rozin, P., & Royzman, E. B. (2001). Negativity Bias, Negativity Dominance, and Contagion. Personality and Social Psychology Review, 5(4), 296–320. https://doi.org/10.1207/S15327957PSPR0504_2

Sanli, C., & Lambiotte, R. (2015). Temporal Pattern of Online Communication Spike Trains in Spreading a Scientific Rumor: How Often, Who Interacts with Whom? Frontiers of Physics in China, 3, 79. https://doi.org/10.3389/FPHY.2015.00079

Sciberras, Marvic; Dingli, Alexiei. (2023). Research Approach—Mixed-Methods Approach (pp. 29–30). Springer eBooks. https://doi.org/10.1007/978-3-031-19900-4_8

Smyth, K. (2023). Challenging male homophily and bias in academic research and practice. Journal of Perspectives in Applied Academic Practice, 11(1), 18–20. https://doi.org/10.56433/jpaap.v11i1.550

Soroka, S., & McAdams, S. (2015). News, Politics, and Negativity. Political Communication, 32(1), 1–22. https://doi.org/10.1080/10584609.2014.881942

Turetsky, K. M., & Riddle, T. (2018). Porous Chambers, Echoes of Valence and Stereotypes: A Network Analysis of Online News Coverage Interconnectedness Following a Nationally Polarizing Race-Related Event. Social Psychological and Personality Science, 9(2), 163–175. https://doi.org/10.1177/1948550617733519

Wahab, Md. I. (2024). Weaponization of Social Media – Challenges and Responses. International Journal For Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2024.v06i05.27555

Zhang, J. (2025). A Data Analysis Framework for a Topic on Social Media. P '24: Proceedings of the 2024 3rd International Conference on Artificial Intelligence and Intelligent Information Processing Pages 111 – 116. https://doi.org/10.1145/3707292.3707351

Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., Caldarelli, G., & Quattrociocchi, W. (2015). Emotional Dynamics in the Age of Misinformation. PLOS ONE, 10(9). https://doi.org/10.1371/JOURNAL.PONE.0138740

Downloads

Published

2026-04-29