将可解释的人工智能方法应用于通过社交网络数据诊断个人特征和认知能力的模型
Anastasia S Panfilova1, Denis Yu Turdakov2
1Institute of Psychology of the Russian Academy of Science, Laboratory of Psychology and Psychophysiology of Creativity, Moscow, Russia. panfilova87@gmail.com.
社交媒体的帖子.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 社交媒体分析 社交媒体分析
背景情况:
- 了解在线行为与心理特征之间的联系至关重要.
- 以前的研究往往侧重于相关性,而不是预测指标.
研究的目的:
- 分析社交媒体用户在VK的行为中的心理属性.
- 确定这些属性对用户交互模型的影响.
- 识别关键的社交媒体行为,表明人格和认知特征.
主要方法:
- 利用人工智能 (AI) 来分析来自1358名VK用户的753,252条帖子.
- 集成的五大性格特征和智力测试结果.
- 使用集成梯度,部分依赖图和Shapley值进行特征分析.
主要成果:
- 帖子中的情感色调预测了外向,可取和开放.
- 社会参与度指标与逻辑思维相关.
- 高度神经症与挑性内容相关;宗教主题与意识和可接受性有关.
结论:
- 社交媒体的行为,特别是情绪调和参与度,为心理特征提供了重要的见解.
- 人工智能驱动的分析可以识别人格和认知诊断的行为指标.
- 这项研究将重点从相关性转移到社交网络中的预测性行为指标.
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