Institute of Psychology of the Russian Academy of Science, Laboratory of Psychology and Psychophysiology of Creativity, Moscow, Russia.
Ivannikov Institute for System Programming of the Russian Academy of Science, Research Center for Trusted Artificial Intelligence, Moscow, Russia.
Sci Rep. 2024 Mar 4;14(1):5369. doi: 10.1038/s41598-024-56080-8.
This study utilizes advanced artificial intelligence techniques to analyze the social media behavior of 1358 users on VK, the largest Russian online social networking service. The analysis comprises 753,252 posts and reposts, combined with Big Five personality traits test results, as well as assessments of verbal and fluid intelligence. The objective of this research is to understand the manifestation of psychological attributes in social media users' behavior and determine their implications on user-interaction models. We employ the integrated gradients method to identify the most influential feature groups. The partial dependence plot technique aids in understanding how these features function across varying severity degrees of the predicted trait. To evaluate feature stability within the models, we cluster calculated Shapley values. Our findings suggest that the emotional tone (joy, surprise, anger, fear) of posts significantly influences the prediction of three personality traits: Extraversion, Agreeableness, and Openness to Experience. Additionally, user social engagement metrics (such as friend count, subscribers, likes, views, and comments) correlate directly with the predicted level of Logical thinking. We also observe a trend towards provocative and socially reprehensible content among users with high Neuroticism levels. The theme of religion demonstrates a multidirectional relationship with Consciousness and Agreeableness. Further findings, including an analysis of post frequency and key text characteristics, are also discussed, contributing to our understanding of the complex interplay between social media behavior and psychological traits. The study proposes a transition from the analysis of correlations between psychological (cognitive) traits to the analysis of indicators of behavior in a social network that are significant for diagnostic models of the corresponding traits.
本研究利用先进的人工智能技术，分析了 VK（俄罗斯最大的在线社交网络服务）上 1358 名用户的社交媒体行为。分析包括 753252 条帖子和转发，结合了大五人格特质测试结果，以及言语和流体智力评估。本研究的目的是了解心理属性在社交媒体用户行为中的表现，并确定其对用户交互模型的影响。我们采用综合梯度方法识别最具影响力的特征群。偏依赖图技术有助于理解这些特征在不同预测特征严重程度下的作用方式。为了评估模型内特征的稳定性，我们对计算出的 Shapley 值进行聚类。我们的研究结果表明，帖子的情绪基调（喜悦、惊讶、愤怒、恐惧）显著影响外向、宜人性和经验开放性这三个人格特质的预测。此外，用户的社交参与度指标（如好友数、订阅者、点赞、浏览量和评论）与逻辑思维的预测水平直接相关。我们还观察到，具有较高神经质水平的用户发布的挑衅性和社会不可接受的内容呈上升趋势。宗教主题与意识和宜人性呈多向关系。进一步的发现，包括对帖子频率和关键文本特征的分析，也进行了讨论，有助于我们理解社交媒体行为和心理特征之间的复杂相互作用。本研究提出了从心理（认知）特征的相关性分析向社交网络中对诊断模型具有重要意义的行为指标分析的转变。
