Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e. the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting, representing and understanding mindsets' structure, in Latin "forma mentis", from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts, without supervision, in benchmark texts. Once validated, TFMNs were applied to the case study of the gender gap in science, which was strongly linked to distorted mindsets by recent studies. Focusing over social media perception and online discourse, this work analysed 10,000 relevant tweets. "Gender" and "gap" elicited a mostly positive perception, with a trustful/joyous emotional profile and semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of "woman" highlighted discussion about sexual harassment and stereotype threat (a form of implicit cognitive bias) relative to women in science "sacrificing personal skills for success". The reconstructed perception of "man" highlighted social users' awareness of the myth of male superiority in science. No anger was detected around "person", suggesting that gap-focused discourse got less tense around genderless terms. No stereotypical perception of "scientist" was identified online, differently from real-world surveys. The overall analysis identified the online discourse as promoting a mostly stereotype-free, positive/trustful perception of gender disparity, aware of implicit/explicit biases and projected to closing the gap. TFMNs opened new ways for investigating perceptions in different groups, offering detailed data-informed grounding for policy making.

中文翻译：

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文本挖掘形式网络重建公众对社交媒体中 STEM 性别差距的看法

心态重建映射个人如何构建和感知知识，通过调查语言及其在人类思维中的认知反映，即心理词典，在此展开的地图。文本格式网络 (TFMN) 是引入的玻璃盒子，用于从文本数据中提取、表示和理解思维定势的结构（拉丁语“格式”）。TFMNs 结合网络科学、心理语言学和大数据，在没有监督的情况下成功地识别了基准文本中的相关概念。一旦得到验证，TFMNs 就被应用于科学性别差距的案例研究，这与最近的研究扭曲的心态密切相关。这项工作侧重于社交媒体感知和在线话语，分析了 10,000 条相关推文。“性别”与“差距” 引发了一种主要是积极的看法，具有信任/快乐的情感特征和语义关联：庆祝成功的女科学家，将性别差距与工资差异联系起来，并希望未来有解决方案。对“女性”的看法突出了关于科学中“为了成功而牺牲个人技能”的女性的性骚扰和刻板印象威胁（一种隐性认知偏见的形式）。对“男人”的重构感知凸显了社会用户对科学中男性优越论神话的认识。在“人”周围没有发现愤怒，这表明以差距为中心的话语在无性别术语上变得不那么紧张。网上没有发现对“科学家”的刻板印象，这与现实世界的调查不同。总体分析表明，在线讨论促进了对性别差异的基本无刻板印象、积极/信任的看法，意识到隐性/显性偏见并预计将缩小差距。TFMN 为调查不同群体的看法开辟了新途径，为政策制定提供了详细的数据依据。