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 and understanding mindsets’ structure (in Latin forma mentis) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals’ opinions at the beginning of posts. “Gender” and “gap” elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of “woman” highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science “sacrificing personal skills for success”. The semantic frame of “man” highlighted awareness of the myth of male superiority in science. No anger was detected around “person”, suggesting that tweets got less tense around genderless terms. No stereotypical perception of “scientist” was identified online, differently from real-world surveys. This analysis thus identified that Twitter discourse mostly starting conversations promoted a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users’ replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground 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


心态重建描绘了个体如何构建和感知知识，这是通过研究语言及其在人类思维中的认知反映（即心理词典）而展开的地图。文本形式网络（TFMN）是为了从文本数据中提取和理解思维方式结构（拉丁语形式）而引入的玻璃盒。 TFMN 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识别了基准文本中的相关概念。一旦得到验证，TFMN 就被应用于有关科学领域性别差距的扭曲心态的案例研究。这项研究以社交媒体为重点，分析了 10,000 条推文，这些推文大多代表了帖子开头的个人观点。 “性别”和“差距”引发了一种积极、信任和快乐的认知，其语义关联是：赞扬成功的女科学家，将性别差距与工资差异联系起来，并希望未来能得到解决。对“女性”的看法突显了科学界女性“为了成功而牺牲个人技能”的性骚扰和刻板印象威胁（一种隐性认知偏见）的行话。 “人”的语义框架凸显了人们对科学中男性优越神话的认识。没有检测到围绕“人”的愤怒，这表明推文中关于无性别术语的紧张程度有所减轻。与现实世界的调查不同，网上没有发现对“科学家”的刻板印象。因此，这项分析发现，推特话语主要是发起对话，促进了对性别差异的基本无刻板印象、积极/信任的看法，旨在缩小差距。因此，未来针对歧视性语言的监控应重点关注对话的其他部分，例如用户的回复。 TFMN 提供了监控集体在线思维的新方法，为政策制定提供了数据依据。