Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis.

BACKGROUND: In health care research, patient-reported opinions are a critical element of personalized medicine and contribute to optimal health care delivery. The importance of integrating natural language processing (NLP) methods to extract patient-reported opinions has been gradually acknowledged over the past years. One form of NLP is sentiment analysis, which extracts and analyses information by detecting feelings (thoughts, emotions, attitudes, etc) behind words. Sentiment analysis has become particularly popular following the rise of digital interactions. However, NLP and sentiment analysis in the context of intrafamilial communication for genetic cancer risk is still unexplored. Due to privacy laws, intrafamilial communication is the main avenue to inform at-risk relatives about the pathogenic variant and the possibility of increased cancer risk. OBJECTIVE: The study examined the role of sentiment in predicting openness of intrafamilial communication about genetic cancer risk associated with hereditary breast and ovarian cancer (HBOC) syndrome. METHODS: We used narratives derived from 53 in-depth interviews with individuals from families that harbor pathogenic variants associated with HBOC: first, to quantify openness of communication about cancer risk, and second, to examine the role of sentiment in predicting openness of communication. The interviews were conducted between 2019 and 2021 in Switzerland and South Korea using the same interview guide. We used NLP to extract and quantify textual features to construct a handcrafted lexicon about interpersonal communication of genetic testing results and cancer risk associated with HBOC. Moreover, we examined the role of sentiment in predicting openness of communication using a stepwise linear regression model. To test model accuracy, we used a split-validation set. We measured the performance of the training and testing model using area under the curve, sensitivity, specificity, and root mean square error. RESULTS: Higher "openness of communication" scores were associated with higher overall net sentiment score of the narrative, higher fear, being single, having nonacademic education, and higher informational support within the family. Our results demonstrate that NLP was highly effective in analyzing unstructured texts from individuals of different cultural and linguistic backgrounds and could also reliably predict a measure of "openness of communication" (area under the curve=0.72) in the context of genetic cancer risk associated with HBOC. CONCLUSIONS: Our study showed that NLP can facilitate assessment of openness of communication in individuals carrying a pathogenic variant associated with HBOC. Findings provided promising evidence that various features from narratives such as sentiment and fear are important predictors of interpersonal communication and self-disclosure in this context. Our approach is promising and can be expanded in the field of personalized medicine and technology-mediated communication.

Language, Childhood, and Fire: How We Learned to Love Sharing Stories.

Stories do not fossilize. Thus, exploring tales shared during prehistory, the longest part of human history inevitably becomes speculative. Nevertheless, various attempts have been made to find a more scientifically valid way into our deep human past of storytelling. Following the social brain hypothesis, we suggest including into the theory of human storytelling more fine-grained and evidence-based findings (from archaeology, the cognitive sciences, and evolutionary psychology) about the manifold exaptation and adaptation, genetic changes, and phenotypic plasticity in the deep human past, which all shaped the emergence of storytelling in hominins. We identify three preconditions for humans sharing stories: first, the long evolution of language in the different taxa as one of the preconditions of ostensive signaling; second, the pivotal role of childhood in the evolution of collaborative intentionality; and third, the role of fireside chats in the rise of elaborative (i.e., narrative) sharing of stories. We propose that humans, albeit perhaps no other hominins learned to understand others through sharing stories, not only as intentional agents, but also as mental ones.

Sentiment Analysis of Children and Youth Literature: Is There a Pollyanna Effect?

If the words of natural human language possess a universal positivity bias, as assumed by Boucher and Osgood's (1969) famous Pollyanna hypothesis and computationally confirmed for large text corpora in several languages (Dodds et al., 2015), then children and youth literature (CYL) should also show a Pollyanna effect. Here we tested this prediction applying an unsupervised vector space model-based sentiment analysis tool called SentiArt (Jacobs, 2019) to two CYL corpora, one in English (372 books) and one in German (500 books). Pitching our analysis at the sentence level, and assessing semantic as well as lexico-grammatical information, both corpora show the Pollyanna effect and thus add further evidence to the universality hypothesis. The results of our multivariate sentiment analyses provide interesting testable predictions for future scientific studies of literature.

Wattpad as a resource for literary studies. Quantitative and qualitative examples of the importance of digital social reading and readers' comments in the margins.

The end of deep reading is a commonplace in public debates, whenever societies talk about youth, books, and the digital age. In contrast to this, we show for the first time and in detail, how intensively young readers write and comment literary texts at an unprecedented scale. We present several analyses of how fiction is transmitted through the social reading platform Wattpad, one of the largest platforms for user-generated stories, including novels, fanfiction, humour, classics, and poetry. By mixed quantitative and qualitative methods and scalable reading we scrutinise texts and comments on Wattpad, what themes are preferred in 13 languages, what role does genre play for readers behaviour, and what kind of emotional engagement is prevalent when young readers share stories. Our results point out the rise of a global reading culture in youth reading besides national preferences for certain topics and genres, patterns of reading engagement, aesthetic values and social interaction. When reading Teen Fiction social-bonding (affective interaction) is prevalent, when reading Classics social-cognitive interaction (collective intelligence) is prevalent. An educational outcome suggests that readers who engage in Teen Fiction learn to read Classics and to judge books not only in direct emotional response to character's behaviour, but focusing more on contextualised interpretation of the text.