屏幕背后的你是谁？利用人工智能进行隐式 MBTI 与性别检测
Who Are You Behind the Screen? Implicit MBTI and Gender Detection Using Artificial Intelligence
AI总结：
本文利用 Telegram 对话数据微调 RoBERTa，从语言模式隐式识别 MBTI 与性别，人格分类准确率达 86.16%，性别分类达 74.4%。
摘要：
在个性化技术和心理学研究中，从数字交互中精确检测人口统计特征和人格特质正变得日益重要。本研究考察隐式分类，即直接从 Telegram 对话数据中的语言模式推断人格与性别变量，而传统人格预测技术大多依赖明确的自我报告标签。我们改进了一个基于 Transformer 的语言模型（RoBERTa），以捕捉能够表明人格特质和性别差异的复杂语言线索；所用数据集包含来自 1,602 名标注有 MBTI 类型用户的 138,866 条消息，以及来自 2,598 名标注有性别用户的 195,016 条消息。置信度水平有助于将模型准确率大幅提升至 86.16%，从而证明 RoBERTa 能够从对话文本数据中稳定识别隐式人格类型。我们的结果凸显了 Transformer 拓扑结构在隐式人格和性别分类中的实用性，强调了其效率，也强调了现实对话环境中准确率与覆盖率之间的重要权衡。在性别分类方面，该模型取得了 74.4% 的准确率，因此捕捉到了特定性别的语言模式。人格维度分析表明，具有内向和直觉偏好的人在基于文本的交互中尤其更加活跃。本研究强调，在基于 Transformer 的模型展现出从对话文本执行隐式人格和性别预测任务的效率时，平衡准确率与数据覆盖率所面临的实际问题。
英文摘要：
In personalized technology and psychological research, precisely detecting demographic features and personality traits from digital interactions becomes ever more important. This work investigates implicit categorization, inferring personality and gender variables directly from linguistic patterns in Telegram conversation data, while conventional personality prediction techniques mostly depend on explicitly self-reported labels. We refine a Transformer-based language model (RoBERTa) to capture complex linguistic cues indicative of personality traits and gender differences using a dataset comprising 138,866 messages from 1,602 users annotated with MBTI types and 195,016 messages from 2,598 users annotated with gender. Confidence levels help to greatly raise model accuracy to 86.16\%, hence proving RoBERTa's capacity to consistently identify implicit personality types from conversational text data. Our results highlight the usefulness of Transformer topologies for implicit personality and gender classification, hence stressing their efficiency and stressing important trade-offs between accuracy and coverage in realistic conversational environments. With regard to gender classification, the model obtained an accuracy of 74.4\%, therefore capturing gender-specific language patterns. Personality dimension analysis showed that people with introverted and intuitive preferences are especially more active in text-based interactions. This study emphasizes practical issues in balancing accuracy and data coverage as Transformer-based models show their efficiency in implicit personality and gender prediction tasks from conversational texts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
