EXPLORING THE IMPACT OF AI TOOLS OF XIAOHONGSHU IN DEVELOPING CREDIBLE CONTENTS SHARING BEHAVIOR AMONG CHINESE POPULATION
Keywords:
Artificial Intelligence (AI), Trusted content sharing, XiaoHongShu, User-generated content (UGC), Algorithmic bias, Media effectiveness, Socio-cultural factors, Chinese social mediaAbstract
With the rapid development of artificial intelligence (AI) technology, social media platforms are increasingly reliant on AI tools in content creation, recommendation, and sharing. This study uses Xiaohongshu, a representative social e-commerce platform in China, as a case study to explore the role and impact of AI tools in promoting users' credible content sharing behavior. The research focuses on how AI-driven technologies such as natural language processing, machine learning, sentiment analysis, and banned word detection enhance content authenticity and user trust, and analyzes their effectiveness in addressing issues such as fake reviews, algorithmic bias, and information opacity. Considering China's unique cultural background, user characteristics (primarily Millennials and Generation Z), and government regulatory policies, this study systematically examines the influence mechanisms of variables such as users' perceived credibility of AI tools, media effectiveness, and socio-cultural factors on content sharing behavior. By addressing six research questions and six research objectives, this paper aims to reveal the potential and challenges of AI tools in building a credible content ecosystem, providing theoretical and practical references for platform governance, AI ethical design, and digital trust construction.References
[1] Gunning D, Aha D W. DARPA's Explainable Artificial Intelligence (XAI) program. AI Magazine, 2019, 40(2): 44-58.
[2] Adadi A, Berrada M. Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 2018, 6: 52138-52160.
[3] Feher K. Generative AI, Media, and Society. Routledge. 2025. DOI: 10.4324/9781003591023.
[4] Yu Z. Xiaohongshu: Modernizing social commerce in China's digital landscape. InCases on Chinese Unicorns and the Development of Startups. IGI Global, 2025: 327-346.
[5] Han HY, Bang, Y. How Does Social Media's Labeling Affect Users' Believability and Engagement? The Moderating Role of Regulatory Focus.Asia pacific journal of information systems,2024, 34(1): 91-113.
[6] Bryanov K, Vziatysheva V. Determinants of individuals’ belief in fake news: A scoping review determinants of belief in fake news.PLoS one,2021, 16(6): e0253717.
[7] Chen L, Shi Y. Enhancing media literacy through explainable AI: The role of educational applications. Journal of Educational Technology Development and Exchange, 2020, 13(2): 45-58.
[8] Chiu CM, Chen HY, Chen CH. The role of self-efficacy and social influence on individuals' sharing behavior of misinformation in social media. Journal of Information Science, 2021, 47(5): 635-648.
[9] Depoux A, Martin S, Karafillakis E, et al. The pandemic of social media panic travels faster than the COVID-19 outbreak. Journal of Travel Medicine, 2020, 27(3): taaa031.
[10] Feng S, Shi Y. Media control in China: A model of media control and propaganda dissemination. Journal of Chinese Political Science, 2020, 25(1): 1-20.
[11] Guess A, Nyhan B, Reifler J. Exposure to untrustworthy websites in the 2016 US election. Nature Human Behaviour, 2019, 3(5): 472-480.
[12] Helm RK, Nasu H. Regulatory responses to 'fake news' and freedom of expression: Normative and empirical evaluation. Human Rights Law Review, 2021, 21(2): 302-328.
[13] Kim S, Dennis AR. Says who? The effects of presentation format and source rating on fake news in social media. MIS Quarterly, 2019, 43(3): 1025-1039.
[14] Miller B. Should we trust our feeds? Social media, misinformation, and the epistemology of testimony. Topoi, 2024, 43(3): 1-12.
[15] Pan J, Zhou Y. The authoritarian's dilemma: Censorship, information control, and public response in China. Journal of Political Studies, 2018, 26(4): 500-518.
[16] Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 2019, 1(5): 206-215.
[17] Shen F, Lu Y, Guo Z, et al. News media use, perceived credibility, and efficacy: An analysis of media participation intention in China. Chinese Journal of Communication, 2011, 4(4): 475-495.
[18] Shu K, Wang S, Liu H. Beyond news contents: The role of social context for fake news detection. ACM Transactions on Information Systems, 2020, 38(3): 1-20.
[19] Tandoc EC, Lim ZW, Ling R. Defining "fake news": A typology of scholarly definitions. Digital Journalism, 2018, 6(2): 137-153.
[20] Zhou S. Media literacy education in China: A national approach to combating misinformation. Journal of Media Literacy Education, 2020, 12(1): 56-68.
[21] Zhou S. The role of youth in the dissemination of digital content in China. Journal of Media Studies, 2020, 12(3): 45-58.
[22] Zhou X. Misinformation, social media, and the demand for credible information in times of crisis. Journal of Communication, 2020, 70(5): 646-665.