Sun Yadong, Shan Yanjie, Xie Jiaqiong, Chen Ke, Hu Jia
Faculty of Social Sciences, Chongqing University, Chongqing, People's Republic of China.
Institute for Advanced Studies in Humanities and Social Science, Chongqing University, Chongqing, People's Republic of China.
Psychol Res Behav Manag. 2024 Jul 24;17:2783-2794. doi: 10.2147/PRBM.S466398. eCollection 2024.
With the development of information technology and various social media, recommendation algorithms have increasingly more influence on users' social media usage. To date, there has been limited research focused on analyzing the impact of recommendation algorithms on social media use and their corresponding role in the development of problematic behaviors. The present study analyzes the impact of recommendation algorithms on college students' information sharing and internalizing, externalizing problem behaviors to address the aforementioned shortcomings.
An online questionnaire survey was conducted among 34,752 college students in China. A latent profile analysis was conducted to explore the various behavioral patterns of Chinese college students' information sharing across the three social media platforms identified for this study. The Bolck-Croon-Hagenaars (BCH) method Regression Mixture Modeling was then used to analyze the differences in internalizing and externalizing problem behaviors among the different subgroups of Chinese college students.
The level of information sharing by college students across different social media platforms could be divided into "WeChat Moments low-frequency information sharing", "middle-frequency comprehensive information sharing", "TikTok high-frequency information sharing", and "Sina Weibo high-frequency information sharing". Significant differences were observed regarding internalizing and externalizing problem behaviors among college students in different information-sharing subgroups.
This study identified four subgroups with different information-sharing characteristics using latent profile analysis. Among them, college students who are in subgroup of social media information sharing influenced by recommendation algorithms exhibit higher frequency of information sharing and higher level of internalizing and externalizing problematic behaviors. These results expand our understanding of college students' social media usage and problem behaviors from a technological perspective. In future, the negative impacts of recommendation algorithms on college students can be reduced by improving their awareness of these algorithms and optimizing the algorithms themselves.
随着信息技术和各种社交媒体的发展，推荐算法对用户的社交媒体使用影响越来越大。迄今为止，针对分析推荐算法对社交媒体使用的影响及其在问题行为发展中相应作用的研究有限。本研究分析推荐算法对大学生信息分享以及内化、外化问题行为的影响，以弥补上述不足。
对中国34752名大学生进行了在线问卷调查。进行了潜在剖面分析，以探索中国大学生在本研究确定的三个社交媒体平台上信息分享的各种行为模式。然后使用博尔克 - 克鲁恩 - 哈格纳尔斯（BCH）方法回归混合模型来分析中国大学生不同亚组在内化和外化问题行为上的差异。
大学生在不同社交媒体平台上的信息分享水平可分为“微信朋友圈低频信息分享”“中频综合信息分享”“抖音高频信息分享”和“新浪微博高频信息分享”。不同信息分享亚组的大学生在内化和外化问题行为方面存在显著差异。
本研究通过潜在剖面分析确定了四个具有不同信息分享特征的亚组。其中，受推荐算法影响的社交媒体信息分享亚组中的大学生表现出更高的信息分享频率以及更高水平的内化和外化问题行为。这些结果从技术角度扩展了我们对大学生社交媒体使用和问题行为 的理解。未来，可以通过提高大学生对这些算法的认识并优化算法本身来减少推荐算法对大学生的负面影响。
