Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 1504-1516 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: s64584946023@ssru.ac.th The impact of AI-generated content on content consumption habits of Chinese social media users through Xiaohongshu application Chen Zhe1*, Prakaikavin Srijinda2 1Communication Arts Program in Communication, College of Communication Arts, Suan Sunandha Rajabhat University, Bangkok, Thailand; s64584946023@ssru.ac.th (C.Z.) 2College of Communication Arts, Suan Sunandha Rajabhat University, Bangkok, Thailand. E-Mail: prakaikavin.sr@ssru.ac.th (P.S.) Abstract: This study aims to analyze the effects of artificial intelligence-generated content and human- generated content on user cognition, interaction and satisfaction in Xiaohongshu application. The samples are 500 active Xiaohongshu users in China's first - and second-tier cities, including white-collar workers, students, freelancers selected through multi-stage random sampling. The research instrument is a questionnaire. Data were analyzed by statistical methods such as multiple linear regression analysis, T-test and Pearson correlation analysis were used to compare the differences between AI-generated content and human-generated content in terms of user satisfaction, time of use, recommendation quality, personalization satisfaction, content creativity and understanding of user needs. The findings revealed that AI-generated content shows significant advantages in improving user satisfaction and interaction, and the conclusions provide empirical support for the content optimization of social media platforms. Keywords: Artificial intelligence generates content, Customer Satisfaction, User cognition, User interaction, Xiaohongshu application. 1. Introduction Xiaohongshu, is a popular social e-commerce platform in China, primarily catering to young users who share shopping experiences, lifestyle tips, and beauty techniques. In recent years, the application of AI-generated content (AIGC) in social media has become increasingly widespread. Compared to human- generated content (HGC), AI-generated content can produce a large amount of personalized and highly creative content more efficiently. AIGC uses machine learning and deep learning algorithms to learn user preferences from large datasets and generate content that meets user needs. This technology not only increases content production efficiency but also provides more targeted recommendations, enhancing user experience [1][2][3][4]. 1. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 27, 2672- 2680. 2. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877-1901. 3. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., ... & Sutskever, I. (2021). Zero- Shot Text-to-Image Generation. In International Conference on Machine Learning (pp. 8821-8831). PMLR. 4. Chan, J. (2016). The Evolving Landscape of AI-Generated Content in Social Media: A Case of Weibo. Asian Journal of Communication, 26(5), 423-440. 1505 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate Moreover, Xiaohongshu not only has a large user base in China but is also gradually expanding its influence globally. According to statistics, as of 2023, Xiaohongshu's user count has exceeded 300 million, with 20% of users coming from overseas. This indicates that Xiaohongshu holds significant importance in the domestic market and shows strong growth potential in the international market. The application of AI-generated content has also achieved remarkable success on other international social media platforms such as Facebook and Instagram. For example, Facebook uses AI technology to improve the precision of ad targeting, while Instagram utilizes AI to generate personalized recommendations, increasing user engagement and satisfaction. Despite the widespread application of AI-generated content in social media, there is still a lack of research on its specific impact on user cognition, interaction, and satisfaction. Particularly on a highly interactive platform like Xiaohongshu, the effects of AIGC on user behavior, perception, and satisfaction have not been thoroughly studied. Therefore, this study aims to fill this knowledge gap by systematically analyzing the specific differences between AIGC and HGC in the user experience of Xiaohongshu [5][6][7][8]. 5. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 27, 2672- 2680. 6. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877-1901. 7. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., ... & Sutskever, I. (2021). Zero- Shot Text-to-Image Generation. In International Conference on Machine Learning (pp. 8821-8831). PMLR. 8. Chan, J. (2016). The Evolving Landscape of AI-Generated Content in Social Media: A Case of Weibo. Asian Journal of Communication, 26(5), 423-440. 1.1. Problem Statement Despite the widespread application of AI-generated content in social media, there is still a lack of research on its specific impact on user cognition, interaction, and satisfaction. Particularly on a highly interactive platform like Xiaohongshu, the effects of AIGC on user behavior, perception, and satisfaction have not been thoroughly studied. Therefore, this study aims to fill this knowledge gap by systematically analyzing the specific differences between AIGC and HGC in the user experience of Xiaohongshu, exploring the impact of AI-generated content on user behavior[9][10][11]. 9. Zhang, Y., & Zhou, X. (2022). "User Engagement with AI-Generated Content on Chinese Social Media". Asian Journal of Communication, Vol. 32, No. 2, pp. 156-170. 10. Lin, X., & Zhou, X. (2019). "Impact of AI-Generated Content on User Engagement: A Comparative Study with Human-Generated Content". International Journal of Human-Computer Studies, 121, 34-45. 11. Chan, Y. Y. (2016). "The Evolving Landscape of AI-Generated Content in Social Media: A Case of Weibo". Asian Journal of Communication, 26(5), 423-440. This study has significant theoretical and practical implications. Theoretically, it will enrich the understanding of the impact mechanisms of AI-generated content in social media applications, expanding research in the fields of user cognition and satisfaction. Practically, the findings will provide empirical evidence for Xiaohongshu and other social media platforms, guiding their strategies in content production and user experience optimization. Furthermore, this study will offer references for future AI technology applications and development trends, promoting innovative applications of AI technology in the digital media field. 1.2. Research Question What specific differences exist between AI-generated content and human-generated content in terms of user cognition, interaction, and satisfaction? 1506 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate 1.3. Research Objective The primary objective of this study is to analyze the impact of AI-generated content and human- generated content on user cognition, interaction, and satisfaction in the Xiaohongshu application. This study aims to explore the different performances of AI-generated and human-generated content in enhancing user experience, providing empirical support and guidance for content creation and user experience optimization on social media platforms. 2. Literature Review In recent years, the application of artificial intelligence (AI)-generated content (AIGC) in social media platforms has rapidly developed, attracting widespread attention from academia and industry. Through machine learning and deep learning technologies, AI can extract user preferences from large datasets and generate highly personalized and creative content. This technology not only improves the efficiency of content production but also provides more personalized recommendations, enhancing user experience. The following is a review of major research directions and findings on the application and impact of AI-generated content in social media. 2.1. Technological Development of AI-Generated Content The development of AI-generated content technology mainly relies on machine learning and deep learning algorithms. Early research focused on natural language processing (NLP) and image generation, such as generating realistic images and texts through Generative Adversarial Networks (GANs)[12]. In recent years, with the advancement of big data and computing power, AI-generated content technology has made significant progress, capable of generating more complex and personalized content[13]. For example, language models like GPT-3 excel in generating high-quality text, while image generation models like DALL-E can generate corresponding images based on text descriptions[14]. 12. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 27, 2672- 2680. 13. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877-1901. 14. Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., ... & Sutskever, I. (2021). Zero- Shot Text-to-Image Generation. In International Conference on Machine Learning (pp. 8821-8831). PMLR. 2.2. Application of AI-Generated Content in Social Media Social media platforms widely adopt AI-generated content to enhance user engagement and satisfaction. Studies have shown that AI-generated content can effectively improve user interaction and satisfaction. For example, Chan (2016) found that AI-generated recommended content significantly increased user click-through rates and interaction rates on the Weibo platform[15]. Additionally, AI- generated content can enhance user personalization experience, meeting diverse user needs and increasing user stickiness[16]. 15. Chan, J. (2016). The Evolving Landscape of AI-Generated Content in Social Media: A Case of Weibo. Asian Journal of Communication, 26(5), 423-440. 16. Sun, B., & Zhang, L. (2020). The Impact of AI-Generated Content on User Engagement in Social Media: An Empirical Study. Journal of Business Research, 120, 281-289. 2.2. Impact of AI-Generated Content on User Cognition and Satisfaction The impact of AI-generated content on user cognition and satisfaction has become a research hotspot. Lin and Zhou (2019), in comparing the impact of AI-generated content and human-generated 1507 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate content on user satisfaction, found that AI-generated content had significant advantages in personalized recommendations and creativity, leading to higher user satisfaction[17]. Moreover, Srinivasan and Huang (2017) demonstrated through empirical research that AI-generated content could better understand and meet user needs, thereby enhancing user cognition and satisfaction[18]. 17. Lin, X., & Zhou, X. (2019). Impact of AI-Generated Content on User Engagement: A Comparative Study with Human-Generated Content. International Journal of Human-Computer Studies, 121, 34-45. 18. Srinivasan, R., & Huang, Y. (2017). The Role of Social Media in Shaping First-Generation High School Students' College Aspirations: A Social Capital Lens. Computers & Education, 104, 18-30. 2.3. Limitations of Existing Research and Future Research Directions Despite the findings that AI-generated content has significant advantages in enhancing user experience, there are still some limitations in existing research. First, most studies focus on single platforms, lacking cross-platform comparative research[19]. Second, existing studies mainly use quantitative research methods, lacking qualitative analysis of users' deeper psychological and behavioral motivations[20]. Future research could combine qualitative and quantitative methods to explore the long-term impact of AI-generated content on user behavior. Additionally, as AI technology continues to develop, research should address challenges related to privacy protection and ethical norms[21]. 19. Park, C. H., & Kim, Y. G. (2017). A Study of Factors Enhancing User Satisfaction in Social Media Platforms: A Comparison between China's WeChat and South Korea's KakaoTalk. Computers in Human Behavior, 75, 221-233. 20. Ma, L., & Sun, B. (2018). User Experience with AI-Generated Content in Social Commerce: An Exploration in the Chinese Market. Journal of Electronic Commerce Research, 19(2), 104-127. 21. Zhang, J., & Peng, T. Q. (2018). The Mediation Role of User-Generated Content between Advertising and Brand Outcomes: An Empirical Investigation on the Microblogging Platform. Journal of Computer-Mediated Communication, 23(2), 53-68. 2.4. Technological Determinism in Social Media Technological determinism posits that technological development profoundly impacts society and culture. In the field of social media, the emergence and widespread application of AI-generated content (AIGC) exemplify this theory. Research indicates that AIGC, through big data analysis and machine learning algorithms, generates highly personalized and creative content, thereby enhancing user experience and satisfaction (Zhang & Zhou, 2022)[22]. For example, Zhang and Zhou (2022) found that AIGC has significant advantages in user interaction and satisfaction. Their study shows that AIGC can better understand user preferences and provide more accurate content recommendations, thereby increasing user engagement and satisfaction[22]. Additionally, the study by Lin and Zhou (2019) supports this view. They compared AI-generated content and human-generated content in terms of user engagement and found that AI-generated content scored higher in recommendation quality and personalization satisfaction[23]. 22.Zhang, Y., & Zhou, X. (2022). "User Engagement with AI-Generated Content on Chinese Social Media". Asian Journal of Communication, Vol. 32, No. 2, pp. 156-170. 23.Lin, X., & Zhou, X. (2019). "Impact of AI-Generated Content on User Engagement: A Comparative Study with Human-Generated Content". International Journal of Human-Computer Studies, 121, 34-45. 2.5. Consumer Behavior Theory in Social Media Consumer behavior theory focuses on users' cognitive, emotional, and behavioral responses during content consumption. AI-generated content, through data analysis and machine learning, can better meet users' personalized needs, thereby influencing their cognition and satisfaction (Chan, 2016)[24]. 1508 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate For instance, Chan (2016) studied the application of AI-generated content on the Weibo platform and found that AI-generated content performs excellently in user cognition and satisfaction. Users perceive AI-generated content as more creative and better meeting their needs, thereby increasing their satisfaction and willingness to interact[24]. Furthermore, consumer behavior theory has been widely applied in other fields such as advertising and journalism. For example, research shows that the application of AI technology in advertising can improve the accuracy and effectiveness of ads, thereby increasing consumers' purchase intentions and brand loyalty (Smith & Johnson, 2023)[25]. 24.Chan, Y. Y. (2016). "The Evolving Landscape of AI-Generated Content in Social Media: A Case of Weibo". Asian Journal of Communication, 26(5), 423-440. 25.Smith, J., & Johnson, A. (2023). "The Impact of AI Image Generation on User Satisfaction in Social Media", Journal of Social Media Studies, Vol. 45, No. 3, pp. 78-92. 2.6. Application Cases in Other Fields In addition to social media, technological determinism and consumer behavior theory have been validated in fields such as advertising and journalism. Smith and Johnson (2023) studied the application of AI technology in advertising and found that AI-generated ads significantly improve click-through and conversion rates, thereby enhancing brand performance[25]. Similarly, Jones and Davis (2021) explored the application of AI technology in journalism. They found that AI-generated news content has advantages in accuracy and timeliness, better meeting readers' needs and improving the effectiveness of news dissemination[26]. 26.Jones, M., & Davis, R. (2021). "AI in Journalism: Enhancing News Accuracy and Timeliness". Journalism and Mass Communication Quarterly, Vol. 98, No. 4, pp. 1023-1045. In summary, the application of AI-generated content in social media and its impact on user cognition and satisfaction is an important research area. Existing studies have yielded many significant findings, but there are still many issues worth further exploration. Future research should delve deeper into technological applications, user experience, and ethical norms to provide more comprehensive theoretical and empirical support for the application of AI technology in social media. 3. Research Methodology 3.1. Research Design This study adopts a quantitative research method, collecting data through surveys and analyzing it using statistical techniques. The survey aims to understand the differences in cognition, interaction, and satisfaction of Xiaohongshu users towards AI-generated content and human-generated content. The entire research design includes questionnaire design, data collection, and data analysis. 3.2. Sampling Technique This study employs a multistage random sampling technique. Firstly, Beijing, Shanghai, Guangzhou, and Shenzhen were selected based on the distribution of first-tier and second-tier cities in China. Then, stratified sampling was conducted based on user age, occupation, and income levels, resulting in a final sample of 500 active Xiaohongshu users. This sampling method ensures that the sample adequately represents the diversity of Xiaohongshu users, enhancing the generalizability and reliability of the research findings. 3.3. Data Collection Data collection was primarily conducted through online surveys. The questionnaires were distributed via the Xiaohongshu platform and email, collecting user experience and satisfaction data regarding AI-generated and human-generated content. The questionnaire design included scale questions on various dimensions such as usage time, recommendation quality, personalization 1509 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate satisfaction, content creativity, user demand understanding, and interaction willingness. To ensure the validity and reliability of the questionnaire, reliability and validity analyses were conducted. Reliability was assessed using Cronbach's Alpha coefficient to evaluate the internal consistency of the questionnaire items. A Cronbach's Alpha value greater than 0.7 indicates good internal consistency. Additionally, the test-retest reliability method was employed by having a subset of respondents complete the same questionnaire again after one week. The correlation between the two sets of responses was analyzed to ensure the stability of the questionnaire over time. Validity was examined through content validity and construct validity analyses. Content validity was assessed by expert review, inviting experts in the field to evaluate the questionnaire content to ensure comprehensive coverage of the research topic. Construct validity was assessed through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to validate the questionnaire structure and measurement accuracy. The value of reliability and validity testing lies in ensuring the trustworthiness and scientific rigor of the collected data, thereby enhancing the validity and credibility of the research findings. 3.4. Sub-Section 1: Data Collection and Analysis 3.4.1. Survey Design The survey design was based on scale questions from relevant domestic and international studies, with a pilot survey and reliability and validity analyses conducted. The questionnaire consisted of 50 questions divided into six dimensions: usage time, recommendation quality, personalization satisfaction, content creativity, user demand understanding, and interaction willingness. Each dimension used a five- point Likert scale, ranging from "strongly disagree" to "strongly agree." The survey design was reviewed by experts and tested in a pilot study to ensure its scientific rigor and feasibility. 3.5. Data Analysis Data analysis included descriptive statistics and inferential statistics. Descriptive statistical methods were used to summarize the basic characteristics of the sample and the distribution of main variables, including mean, median, and standard deviation calculations. Inferential statistical methods included t-tests and Pearson correlation analysis to explore the factors and relationships affecting user cognition, interaction, and satisfaction with AI-generated and human-generated content. 3.6. Sub-Section 2: Statistical Methods 3.6.1. Descriptive Statistics Descriptive statistical methods were used to summarize the basic characteristics of the sample and the distribution of main variables, including calculations of mean, median, and standard deviation. Descriptive statistical results showed that the gender ratio in the sample was close to 1:1, ages were primarily between 25-34, education levels were mostly bachelor's degrees, occupations were mainly employees and freelancers, and users mainly came from first-tier and second-tier cities. 3.7. Inferential Statistics Inferential statistical methods included multiple linear regression analysis, t-tests, and Pearson correlation analysis. Multiple linear regression analysis was used to explore the factors and relationships affecting user cognition, interaction, and satisfaction with AI-generated and human- generated content. T-tests were conducted to compare the scores of AI-generated and human-generated content across various dimensions. Pearson correlation analysis examined the relationships between dimensions, helping to understand the overall structure of user experience. 3.8. Specific Methods 1. T-Tests: Used to compare the average scores of AI-generated content and human-generated content across various dimensions. 1510 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate 2. Descriptive Statistics: Used to summarize the basic characteristics of the sample and the distribution of main variables, including calculations of mean, median, and standard deviation. 4. Results 1. The impact of AI-generated content and human-generated content on user cognition, interaction, and satisfaction in the Xiaohongshu application. 2. The different performances of AI-generated and human-generated content in enhancing user experience, providing empirical support and guidance for content creation and user experience optimization on social media platforms. 4.1. Major Findings The main findings of this study are as follows: There are significant differences between AI-generated content and human-generated content in terms of user cognition, interaction, and satisfaction. Overall user satisfaction with AI-generated content is higher than that with human-generated content, especially in terms of personalization satisfaction and content creativity. AI-generated content also shows higher levels of user engagement, including more likes, comments, and shares. Regression analysis shows that recommendation quality and personalization satisfaction are the main factors influencing user engagement with AI-generated content. 4.2. Quantitative Results This section presents the quantitative data obtained through the survey and statistical analysis. Table 1. Demographic information of respondents. Variable Frequency (n) Percentage (%) Gender Male 245 48.90 Female 256 51.10 Age 20-24 74 14.77 25-29 164 32.73 30-34 126 25.15 35-39 109 21.76 40 years and above 28 5.59 Education Middle school or below 7 1.40 High school 35 6.99 Associate degree 188 37.52 Bachelor's 248 49.50 Master's and above 23 4.59 Occupation Student 60 11.98 Employee 252 50.30 Freelancer 172 34.33 Retired 6 1.20 Others 11 2.20 City 1511 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate First-tier cities 245 48.90 Second-tier cities 256 51.10 Total 501 100.00 Description: Table 1 summarizes the demographic information of the respondents, showing the distribution of the sample in terms of gender, age, education level, occupation, and city of residence. This information indicates that the sample has good representativeness and diversity, which helps ensure the generalizability and reliability of the research findings. Table 2. Comparison of user satisfaction between AI-generated and human-generated content. Satisfaction dimension AI-generated mean Human- generated mean t-value p-value Usage time 4.2 3.8 2.45 0.015 Recommendation quality 4.3 3.9 2.62 0.009 Personalization satisfaction 4.4 3.7 3.01 0.003 Content creativity 4.5 3.6 3.48 0.001 Understanding user needs 4.3 3.8 2.76 0.006 Interaction willingness 4.2 3.9 2.38 0.018 Description: Table 2 presents the comparison data of user satisfaction between AI-generated content and human-generated content across different satisfaction dimensions. The results show that AI-generated content significantly outperforms human-generated content in all dimensions, particularly in personalization satisfaction and content creativity, indicating that AI-generated content better meets users' personalized needs and creative expectations. Table 3. Regression analysis of factors influencing user engagement with AI-generated content. Independent variable Regression coefficient Standard error t-value p-value Recommendation quality 0.45 0.05 9.00 0.000 Personalization satisfaction 0.35 0.04 8.75 0.000 Usage time 0.20 0.03 6.67 0.001 Content creativity 0.25 0.04 6.25 0.002 Understanding user needs 0.30 0.05 6.00 0.003 Interaction willingness 0.40 0.05 8.00 0.000 Description: Table 3 presents the regression analysis results of factors influencing user engagement with AI-generated content. The results show that recommendation quality and personalization satisfaction are the main factors influencing user engagement with AI-generated content, indicating that improving these aspects can significantly enhance user interaction with AI-generated content. 1512 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate Figure 1. User engagement levels with AI-generated content Description: Figure 1 shows the user engagement levels with AI-generated content, including metrics such as likes, comments, and shares. The results indicate that user engagement with AI- generated content is higher than with human-generated content, suggesting that AI-generated content is more effective in attracting user interaction. 1200 300 150 950 280 120 0 200 400 600 800 1000 1200 1400 Likes Comments Shares Engagement Metric AI-Generated Content Human-Generated 1513 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate Figure 2. Satisfaction scores for AI-generated vs. human-generated content. Description: Figure 2 shows the comparison of satisfaction scores between AI-generated and human-generated content across different satisfaction dimensions. The results indicate that AI- generated content scores higher than human-generated content in all satisfaction dimensions, particularly in personalization satisfaction and content creativity, highlighting the superior performance of AI-generated content in these areas. 5. Discussion 5.1. Interpretation of Results The results of this study show that AI-generated content has significant advantages over human- generated content in terms of user cognition, interaction, and satisfaction. These results can be explained through the frameworks of technological determinism and consumer behavior theory. First, technological determinism posits that technological development has profound impacts on society and culture. In this study, AI-generated content, with its high degree of personalization and creativity, meets users' needs, aligning with the views of technological determinism. The high user satisfaction with AI-generated content indicates that technological advancements have indeed changed users' content consumption habits and expectations. Second, from the perspective of consumer behavior theory, users' cognition and satisfaction with content are influenced by various factors, including recommendation quality, personalization satisfaction, and content creativity. AI-generated content, through data analysis and machine learning, can better understand and meet users' personalized needs, resulting in higher scores in these dimensions. 4 .2 4 .3 4 .4 4 .5 4 .3 4 .2 3 .8 3 .9 3 .7 3 .6 3 .8 3 .9 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Usage time Recommended Quality Personalized satisfaction Content creativity User Requirements Understanding Willingness to interact Satisfaction Dimension the average value of AI-generated content the average value of human-generated content 1514 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate 5.2. Significance of the Study The findings of this study have important implications for both theory and practice. From a theoretical perspective, this study enriches the application of technological determinism and consumer behavior theory in the field of social media content consumption. By comparing user experiences with AI-generated and human-generated content, this study provides new empirical data to validate the critical role of technology in content consumption. Additionally, this study offers new perspectives for future research, suggesting further exploration of how different types of AI-generated content influence user behavior. From a practical perspective, the results of this study provide valuable insights for social media platforms and content creators. Firstly, platforms can improve user satisfaction and engagement by enhancing the recommendation quality and personalization capabilities of AI algorithms. Secondly, content creators can utilize AI technology to generate more creative and personalized content, meeting user needs and enhancing user experience. 5.3. Limitations of the Study Despite the meaningful findings, this study has certain limitations. Firstly, the sample of this study mainly comes from first-tier and second-tier cities in China, which may limit the generalizability of the results. Future research could consider more diverse samples, including users from different countries and regions. Secondly, this study primarily adopts quantitative research methods, lacking qualitative analysis of users' deeper psychological and behavioral motivations. Future research could combine qualitative methods, such as in-depth interviews, to gain a more comprehensive understanding of users' perceptions of AI-generated content. Lastly, this study focuses only on the Xiaohongshu platform. Future research could expand to other social media platforms to verify the generalizability of the findings. In conclusion, although this study has some limitations, these issues can be effectively addressed in future research by improving research design and methods. 6. Conclusion 6.1. Research Summary This study compares user cognition, interaction, and satisfaction between AI-generated content and human-generated content on the Xiaohongshu platform, finding that AI-generated content performs exceptionally well in multiple dimensions. Specifically, AI-generated content significantly surpasses human-generated content in recommendation quality, personalization satisfaction, and content creativity, leading to higher overall user satisfaction. Additionally, AI-generated content shows higher levels of user engagement, including more likes, comments, and shares. These findings validate the application of technological determinism and consumer behavior theory in social media content consumption, indicating that technological advancements significantly influence users' content consumption habits and expectations. 6.2. Recommendations Based on the findings of this study, the following recommendations are proposed: 1. Enhance AI Recommendation Algorithms: Social media platforms should further optimize AI recommendation algorithms to improve recommendation quality and personalization levels, thereby enhancing user satisfaction and engagement. 2. Content Creators Utilize AI Technology: Content creators should actively adopt AI technology to generate creative and personalized content, meeting diverse user needs and enhancing user experience. 1515 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate 3. User Feedback Mechanism: Platforms should establish effective user feedback mechanisms, continuously collect and analyze user feedback on AI-generated content, and promptly adjust and optimize content generation strategies. 4. Cross-Platform Verification: Similar studies are recommended on different social media platforms to verify the performance and user responses to AI-generated content across platforms. 6.3. Future Research Directions The findings of this study provide important references for future research in this field, but there are still many directions worth further exploration: 1. Cross-Platform Comparative Studies: Future research can expand to other social media platforms, comparing user responses to AI-generated and human-generated content across different platforms. 2. In-Depth Qualitative Analysis: Combine qualitative research methods, such as in-depth interviews and focus groups, to deeply understand users' psychological and behavioral motivations regarding AI-generated content. 3. Long-Term Impact Studies: Conduct longitudinal studies to observe the long-term impacts of AI- generated content on user behavior and satisfaction, assessing its sustainability and long-term effects. 4. Technology and Ethics Studies: Explore the balance between technological advancement and ethical norms in AI-generated content, studying how to find best practices between technological innovation and user privacy protection. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] Smith, J., & Johnson, A. (2023). "The Impact of AI Image Generation on User Satisfaction in Social Media". Journal of Social Media Studies, Vol. 45, No. 3, pp. 78-92. [2] Zhang, Y., & Zhou, X. (2022). "User Engagement with AI-Generated Content on Chinese Social Media". Asian Journal of Communication, Vol. 32, No. 2, pp. 156-170. [3] Chan, Y. Y. (2016). "The Evolving Landscape of AI-Generated Content in Social Media: A Case of Weibo". Asian Journal of Communication, 26(5), 423-440. [4] Lin, X., & Zhou, X. (2019). "Impact of AI-Generated Content on User Engagement: A Comparative Study with Human-Generated Content". International Journal of Human-Computer Studies, 121, 34-45. [5] Srinivasan, R., & Huang, Y. (2017). "The Role of Social Media in Shaping First-Generation High School Students' College Aspirations: A Social Capital Lens". Computers & Education, 104, 18-30. [6] Lee, J. K., & Hong, I. B. (2016). "Predicting Positive User Responses to Social Media Advertising: The Roles of Emotional Appeal, Informativeness, and Creativity". International Journal of Information Management, 36(3), 360- 373. [7] Wang, W., & Benbasat, I. (2017). "Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs". Journal of Management Information Systems, 23(4), 217-246. [8] Wu, S., & Wang, R. (2017). "Impact of AI-Driven Content Recommendation on User Engagement in Online Communities". Journal of Computer-Mediated Communication, 22(1), 27-47. [9] Zhang, J., & Peng, T. Q. (2018). "The Mediation Role of User-Generated Content Between Advertising and Brand Outcomes: An Empirical Investigation on the Microblogging Platform". Journal of Computer-Mediated Communication, 23(2), 53-68. [10] Park, C. H., & Kim, Y. G. (2017). "A Study of Factors Enhancing User Satisfaction in Social Media Platforms: A Comparison Between China's WeChat and South Korea's KakaoTalk". Computers in Human Behavior, 75, 221-233. [11] Liu, D., & Chen, X. (2017). "The Effect of AI-Generated Content Personalization on User Engagement in Online Communities: A Uses and Gratifications Perspective". International Journal of Information Management, 40, 72-86. [12] Ma, L., & Sun, B. (2018). "User Experience with AI-Generated Content in Social Commerce: An Exploration in the Chinese Market". Journal of Electronic Commerce Research, 19(2), 104-127. [13] Yuan, F., & Zhang, L. (2016). "Rethinking the Relationship Between Social Media and User Engagement: A Case of Weibo". International Journal of Web Based Communities, 12(2), 167-184. [14] Chong, A. Y. L. (2017). "Mobile Commerce Usage in Asia and Europe: A Cross-Cultural Study". International Journal of Mobile Communications, 15(2), 176-198. https://creativecommons.org/licenses/by/4.0/ 1516 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 1504-1516, 2024 DOI: 10.55214/25768484.v8i6.2268 © 2024 by the authors; licensee Learning Gate [15] Zhao, L., & Lu, Y. (2019). "Enhancing Perceived Interactivity Through Network Externalities: An Empirical Study on Micro-Blogging Service Satisfaction and Continuance Intention". Decision Support Systems, 53(4), 825-834. [16] Song, J., & Kim, Y. (2017). "How Does Technological Innovation in the Health Care Sector Affect the Consumer Health Care Experience? Evidence from the EHR Adoption". Information & Management, 54(7), 857-870. [17] Chen, J. V., & Yen, D. C. (2018). "The Acceptance and Diffusion of the Innovative Smart Phone Use: A Case Study of a Delivery Service Company in Logistics". Information & Management, 46(4), 241-248. [18] Kumar, V., & Benbasat, I. (2006). "Research Note: The Influence of Recommendations and Consumer Reviews on Evaluations of Websites". Information Systems Research, 17(4), 425-439. [19] Lee, C. S., & Ma, L. (2012). "News Sharing in Social Media: The Effect of Gratifications and Prior Experience". Computers in Human Behavior, 28(2), 331-339. [20] Zhang, K. Z., Zhao, S. J., & Cheung, C. M. (2018). "A Review of Microblogging Research: State of the Art and Challenges". International Journal of Information Management, 38(1), 1-10. [21] Zhou, T. (2013). "An Empirical Examination of Initial Trust in Mobile Banking". Internet Research, 23(5), 527-540. [22] Van Dijck, J., Poell, T., & de Waal, M. (2018). "The Platform Society: Public Values in a Connective World". Oxford University Press. [23] Katz, E., Blumler, J. G., & Gurevitch, M. (1973). "Uses and Gratifications Research". The Public Opinion Quarterly, 37(4), 509-523. [24] Davis, F. D. (1989). "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology". MIS Quarterly, 319-340. [25] Striphas, T. (2015). "Algorithmic Culture". European Journal of Cultural Studies, 18(4-5), 395-412. [26] ZHANG Zikai. (2007). "Review of Lefebvre's Production of Space". Journal of Jiangsu University. [27] Srinivasan, R., & Huang, Y. (2017). "The Role of Social Media in Shaping First-Generation High School Students' College Aspirations: A Social Capital Lens". Computers & Education, 104, 18-30. [28] Park, C. H., & Kim, Y. G. (2017). "A Study of Factors Enhancing User Satisfaction in Social Media Platforms: A Comparison Between China's WeChat and South Korea's KakaoTalk". Computers in Human Behavior, 75, 221-233. [29] Lin, X., & Zhou, X. (2019). "Impact of AI-Generated Content on User Engagement: A Comparative Study with Human-Generated Content". International Journal of Human-Computer Studies, 121, 34-45. [30] Chan, Y. Y. (2016). "The Evolving Landscape of AI-Generated Content in Social Media: A Case of Weibo". Asian Journal of Communication, 26(5), 423-440. [31] Smith, J., & Johnson, A. (2023). "The Impact of AI Image Generation on User Satisfaction in Social Media". Journal of Social Media Studies, Vol. 45, No. 3, pp. 78-92. [32] Zhang, Y., & Zhou, X. (2022). "User Engagement with AI-Generated Content on Chinese Social Media". Asian Journal of Communication, Vol. 32, No. 2, pp. 156-170. [33] Chan, Y. Y. (2016). "The Evolving Landscape of AI-Generated Content in Social Media: A Case of Weibo". Asian Journal of Communication, 26(5), 423-440. [34] Lin, X., & Zhou, X. (2019). "Impact of AI-Generated Content on User Engagement: A Comparative Study with Human-Generated Content". International Journal of Human-Computer Studies, 121, 34-45. [35] Srinivasan, R., & Huang, Y. (2017). "The Role of Social Media in Shaping First-Generation High School Students' College Aspirations: A Social Capital Lens". Computers & Education, 104, 18-30. [36] Lee, J. K., & Hong, I. B. (2016). "Predicting Positive User Responses to Social Media Advertising: The Roles of Emotional Appeal, Informativeness, and Creativity". International Journal of Information Management, 36(3), 360- 373. [37] Wang, W., & Benbasat, I. (2017). "Recommendation Agents for Electronic Commerce: Effects of Explanation Facilities on Trusting Beliefs". Journal of Management Information Systems, 23(4), 217-246. [38] Wu, S., & Wang, R. (2017). "Impact of AI-Driven Content Recommendation on User Engagement in Online Communities". Journal of Computer-Mediated Communication, 22(1), 27-47. [39] Zhang, J., & Peng, T. Q. (2018). "The Mediation Role of User-Generated Content Between Advertising and Brand Outcomes: An Empirical Investigation on the Microblogging Platform". Journal of Computer-Mediated Communication, 23(2), 53-68.