Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Abstract

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.

Cognitive Network Science Reconstructs How Experts, News Outlets and Social Media Perceived the COVID-19 Pandemic

“…This manuscript adopted the recent framework of forma mentis networks-as already introduced in previous studies-that used automatic text processing for reconstructing how social media discussed the gender gap in science [5] and for exploring online perceptions and emotions in Italy after the release of national lockdown [13]. On the one hand, [5] also showed that textual forma mentis networks in annotated texts are successful in determining the topic of a text. On the other hand, [13] showed that textual forma mentis networks are sensitive enough to highlight flickering emotions over time in the social discourse around specific topics and hashtags.…”

“…Here, the methodology of textual forma mentis networks is reported in a concise yet self-contained manner. Based on cognitive data and text processing, forma mentis networks are knowledge graphs [5] representing the mental lexicon, a cognitive system storing linguistic information and driving word acquisition and use [6,9]. Representing conceptual associations between words as a network of nodes (words) and links (syntactic/semantic associations), textual forma mentis networks (TFMNs) extract both knowledge and affective patterns, as embedded in the text and are representative of a given author's mindset.…”

“…Several successful approaches also used syntactic relationships in order to investigate how words tend be assembled in sentences in order to minimise dependency crossings and thus, potential confusions about understanding knowledge and its meaning (for a review, see [19]). Here, TFMNs focus on associative knowledge, linking together concepts according to meaning specifications provided in language (see [5]). When we read sentences, we do not explicitly observe such a conceptual network, but we mentally reconstruct it in order to understand meaning.…”

“…This manuscript outlines how recent tools from computational cognitive science [5,6] can crucially reconstruct how experts, news media and social media perceived and discussed the COVID-19…”

“…For instance, in a text if "pandemic" is conceptually related to other jargon like "disease", "spreading" and "pathogen" then the text author(s) framed "pandemics" as a phenomenon related to disease diffusion. Conceptual relationships might be of different types, like word co-occurrences (see also the interesting historical investigation of "risk" in [10]) or syntactic/semantic links (like words specifying each other in a sentence or possessing the same meaning, see also [5]). Semantic frames arise from the associative structure of language and contain both semantic and emotional content [9,11].…”