Disruptions in the Cystic Fibrosis Community's Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments.

BACKGROUND: The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods. OBJECTIVE: This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community's experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases. METHODS: We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of "1" was assigned to months in 2020 and "0" otherwise and tested for its statistical significance. RESULTS: A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community's experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period. CONCLUSIONS: There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them.

Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer.

BACKGROUND: Meta-analyses aggregate results of different clinical studies to assess the effectiveness of a treatment. Despite their importance, meta-analyses are time-consuming and labor-intensive as they involve reading hundreds of research articles and extracting data. The number of research articles is increasing rapidly and most meta-analyses are outdated shortly after publication as new evidence has not been included. Automatic extraction of data from research articles can expedite the meta-analysis process and allow for automatic updates when new results become available. In this study, we propose a system for automatically extracting data from research abstracts and performing statistical analysis. MATERIALS AND METHODS: Our corpus consists of 1011 PubMed abstracts of breast cancer randomized controlled trials annotated with the core elements of clinical trials: Participants, Intervention, Control, and Outcomes (PICO). We proposed a BERT-based named entity recognition (NER) model to identify PICO information from research abstracts. After extracting the PICO information, we parse numeric outcomes to identify the number of patients having certain outcomes for statistical analysis. RESULTS: The NER model extracted PICO elements with relatively high accuracy, achieving F1-scores greater than 0.80 in most entities. We assessed the performance of the proposed system by reproducing the results of an existing meta-analysis. The data extraction step achieved high accuracy, however the statistical analysis step achieved low performance because abstracts sometimes lack all the required information. CONCLUSION: We proposed a system for automatically extracting data from research abstracts and performing statistical analysis. We evaluated the performance of the system by reproducing an existing meta-analysis and the system achieved a relatively good performance, though more substantiation is required.

The impact of culture on emotion suppression: Insights from an electrophysiological study of emotion regulation in Japan.

Prior theory and evidence suggest that native East Asians tend to down-regulate their emotional arousal to negatively valenced experiences through expressive suppression, an emotion regulation technique focused on suppressing one's emotional experience. One proposed explanation for this choice of regulation strategy and its efficacy is rooted in their commitment to the cultural value of interdependence with others. However, prior work has not yet thoroughly supported this hypothesis using in vivo neural correlates of emotion regulation. Here, we utilized an established electroencephalogram (EEG) correlate of emotional arousal, the late positive potential (LPP), to examine whether down-regulation of the LPP in native East Asians might be particularly pronounced for those relatively high in interdependent self-construal. In this study, native Japanese participants attempted to suppress their emotional reaction to unpleasant images during EEG recording. In support of the hypothesis that emotion suppression among native East Asians is influenced by the cultural value of interdependence, there was a significant effect of interdependent self-construal on the LPP. Specifically, those relatively high in interdependent (versus independent) self-construal exhibited a smaller LPP in response to unpleasant pictures when instructed to suppress their emotions versus a passive viewing condition. However, this effect was negligible for those relatively low in interdependent self-construal, suggesting that cultural values impact the in vivo efficacy of different emotion regulation techniques. These results demonstrate the importance of identifying correspondence between self-report measures and in vivo correlates of emotion regulation in cross-cultural research.

Extracting Spatio-Temporal Trends in Medical Research Prioritization Through Natural Language Processing of Case Report Abstracts.

Medical research prioritization is an important aspect of decision-making by researchers and relevant stakeholders. The ever-increasing availability of technology and data has opened doors to new discoveries and new questions. This makes it difficult for researchers and relevant stakeholders to make well-informed decisions about the research areas they want to support and the nations they should look for collaborations. It is, therefore, useful to look at the spatio-temporal trends of medical research prioritization to gain insight into popular and neglected areas of research as well as the allocation of prioritization of each nation. In this study, we develop a system that collects, classifies, and summarizes case report abstracts according to the location, time, and disease category of the report. The additional classifications allow us to visualize and monitor the trends in medical research prioritization by location, time, and disease category.

Differing Content and Language Based on Poster-Patient Relationships on the Chinese Social Media Platform Weibo: Text Classification, Sentiment Analysis, and Topic Modeling of Posts on Breast Cancer.

BACKGROUND: Breast cancer affects the lives of not only those diagnosed but also the people around them. Many of those affected share their experiences on social media. However, these narratives may differ according to who the poster is and what their relationship with the patient is; a patient posting about their experiences may post different content from someone whose friends or family has breast cancer. Weibo is 1 of the most popular social media platforms in China, and breast cancer-related posts are frequently found there. OBJECTIVE: With the goal of understanding the different experiences of those affected by breast cancer in China, we aimed to explore how content and language used in relevant posts differ according to who the poster is and what their relationship with the patient is and whether there are differences in emotional expression and topic content if the patient is the poster themselves or a friend, family member, relative, or acquaintance. METHODS: We used Weibo as a resource to examine how posts differ according to the different poster-patient relationships. We collected a total of 10,322 relevant Weibo posts. Using a 2-step analysis method, we fine-tuned 2 Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers (BERT) Pretraining Approach models on this data set with annotated poster-patient relationships. These models were lined in sequence, first a binary classifier (no_patient or patient) and then a multiclass classifier (post_user, family_members, friends_relatives, acquaintances, heard_relation), to classify poster-patient relationships. Next, we used the Linguistic Inquiry and Word Count lexicon to conduct sentiment analysis from 5 emotion categories (positive and negative emotions, anger, sadness, and anxiety), followed by topic modeling (BERTopic). RESULTS: Our binary model (F1-score=0.92) and multiclass model (F1-score=0.83) were largely able to classify poster-patient relationships accurately. Subsequent sentiment analysis showed significant differences in emotion categories across all poster-patient relationships. Notably, negative emotions and anger were higher for the "no_patient" class, but sadness and anxiety were higher for the "family_members" class. Focusing on the top 30 topics, we also noted that topics on fears and anger toward cancer were higher in the "no_patient" class, but topics on cancer treatment were higher in the "family_members" class. CONCLUSIONS: Chinese users post different types of content, depending on the poster- poster-patient relationships. If the patient is family, posts are sadder and more anxious but also contain more content on treatments. However, if no patient is detected, posts show higher levels of anger. We think that these may stem from rants from posters, which may help with emotion regulation and gathering social support.

Happiness Maximization Is a WEIRD Way of Living.

Psychological science tends to treat subjective well-being and happiness synonymously. We start from the assumption that subjective well-being is more than being happy to ask the fundamental question: What is the ideal level of happiness? From a cross-cultural perspective, we propose that the idealization of attaining maximum levels of happiness may be especially characteristic of Western, educated, industrial, rich, and democratic (WEIRD) societies but less so for others. Searching for an explanation for why "happiness maximization" might have emerged in these societies, we turn to studies linking cultures to their eco-environmental habitat. We discuss the premise that WEIRD cultures emerged in an exceptionally benign ecological habitat (i.e., faced relatively light existential pressures compared with other regions). We review the influence of the Gulf Stream on the Northwestern European climate as a source of these comparatively benign geographical conditions. We propose that the ecological conditions in which WEIRD societies emerged afforded them a basis to endorse happiness as a value and to idealize attaining its maximum level. To provide a nomological network for happiness maximization, we also studied some of its potential side effects, namely alcohol and drug consumption and abuse and the prevalence of mania. To evaluate our hypothesis, we reanalyze data from two large-scale studies on ideal levels of personal life satisfaction-the most common operationalization of happiness in psychology-involving respondents from 61 countries. We conclude that societies whose members seek to maximize happiness tend to be characterized as WEIRD, and generalizing this across societies can prove problematic if adopted at the ideological and policy level.

Exploring the use of AI text-to-image generation to downregulate negative emotions in an expressive writing application.

Conventional writing therapies are versatile, accessible and easy to facilitate online, but often require participants to self-disclose traumatic experiences. To make expressive writing therapies safer for online, unsupervised environments, we explored the use of text-to-image generation as a means to downregulate negative emotions during a fictional writing exercise. We developed a writing tool, StoryWriter, that uses Generative Adversarial Network models to generate artwork from users' narratives in real time. These images were intended to positively distract users from their negative emotions throughout the writing task. In this paper, we report the outcomes of two user studies: Study 1 (N = 388), which experimentally examined the efficacy of this application via negative versus neutral emotion induction and image generation versus no image generation control groups; and Study 2 (N = 54), which qualitatively examined open-ended feedback. Our results are heterogeneous: both studies suggested that StoryWriter somewhat contributed to improved emotion outcomes for participants with pre-existing negative emotions, but users' open-ended responses indicated that these outcomes may be adversely modulated by the generated images, which could undermine the therapeutic benefits of the writing task itself.

Generalized morality culturally evolves as an adaptive heuristic in large social networks.

Why do people assume that a generous person should also be honest? Why do we even use words like "moral" and "immoral"? We explore these questions with a new model of how people perceive moral character. We propose that people vary in the extent to which they perceive moral character as "localized" (varying along many contextually embedded dimensions) versus "generalized" (varying along a single dimension from morally bad to morally good). This variation might be partly the product of cultural evolutionary adaptations to different kinds of social networks. As networks grow larger, perceptions of generalized morality are increasingly valuable for predicting cooperation during partner selection, especially in novel contexts. Our studies show that social network size correlates with perceptions of generalized morality in United States and international samples (Study 1) and that East African hunter-gatherers with greater exposure outside their local region perceive morality as more generalized compared to those who have remained in their local region (Study 2). We support the adaptive value of generalized morality in large and unfamiliar social networks with an agent-based model (Study 3), and in experiments where we manipulate partner unfamiliarity (Study 4). Our final study shows that perceptions of morality have become more generalized over the last 200 years of English-language history, which suggests that it may be coevolving with rising social complexity and anonymity in the English-speaking world (Study 5). We discuss the implications of this theory for the cultural evolution of political systems, religion, and taxonomical theories of morality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study.

Background: The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy. Objective: This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia. Methods: We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country. Results: We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets. Conclusions: We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed.

Between Fact and Fabrication: How Visual Art Might Nurture Environmental Consciousness.

Previous studies have highlighted the communicative limitations of artistic visualizations, which are often too conceptual or interpretive to enhance public understanding of (and volition to act upon) scientific climate information. This seems to suggest a need for greater factuality/concreteness in artistic visualization projects, which may indeed be the case. However, in this paper, we synthesize insights from environmental psychology, the psychology of art, and intermediate disciplines like eco-aesthetics, to argue that artworks-defined by their counterfactual qualities-can be effective for stimulating elements of environmental consciousness. We also argue that different artworks may yield different effects depending on how they combine counter/factual strategies. In so doing, we assert that effective artistic perceptualization-here expressed as affectivization-exceeds the faithful translation of facts from one mode to another, and cannot be encapsulated in a single example of un/successful art.

The relationship between Hikikomori risk factors and social withdrawal tendencies among emerging adults-An exploratory study of Hikikomori in Singapore.

Introduction: Once a localized Japanese phenomenon, Hikikomori-type social withdrawal has since been observed globally in increasing numbers. However, there is a lack of research about Hikikomori in Singapore. Consequently, local variations of Hikikomori may differ from past research in Japan. Drawing on associations found in international and Japanese Hikikomori research, we explored some variables relevant and generalizable to the Singaporean context. Specifically, we examined the relationships between (1) Hikikomori risk factors, (2) social withdrawal tendencies, (3) depression and anxiety, (4) connections with family and friends, and (5) employment status. Methods: In a cross-sectional survey study (N = 416; M age = 24.90, SD age = 4.79; females = 236, males = 177, undisclosed = 3), participants were provided a Qualtrics link and asked to complete a questionnaire comprising the NHR scale, LSNS-6, DASS-21, ERQ, and HQ-25. Results: We found that (a) Hikikomori risk factors positively correlated with social withdrawal tendencies and depression and anxiety but negatively correlated with support from family and friends, (b) high Hikikomori risk factors predicted high social withdrawal tendencies, (c) support from friends (one of the psychosocial factors) predicted social withdrawal tendencies together with the Hikikomori risk factors, and (d) social withdrawal tendencies moderated the relationship between Hikikomori risk factors and depression among the emerging adults in Singapore. Conclusion: The current research findings serve as a basis for future Hikikomori research in Singapore.

Measuring concerns about the COVID-19 vaccine among Japanese internet users through search queries.

With the increasing availability of the COVID-19 vaccines, vaccination has been rapidly promoted globally as a countermeasure against the spread of COVID-19. In Japan, vaccination was first introduced in February 2021. However, the amount of concern towards vaccination differs between individuals, and topics of concern include adverse reactions and side effects. This study investigated attitudes toward vaccines or vaccination during the COVID-19 pandemic across different Japanese prefectures, using Yahoo! JAPAN search queries. We first defined a vaccine concern index (VCI) by aggregating the search counts of vaccine-related queries from Yahoo! JAPAN users before examining VCI across all Japanese prefectures, accounting for gender and age. Our results demonstrated that VCI tended to be lower in more populated areas, and VCI was higher in their 20s to 40s than older people, especially in female users. Furthermore, there was a significant positive correlation (Spearman's Rank correlation coefficient [Formula: see text] = 0.60, [Formula: see text]) between VCI and prefectural vaccination rate, suggesting that web searching of adverse vaccine reactions may precede actual vaccination. This could reflect the information-seeking behavior of individuals who are accepting of vaccinations.

Real and Imagined Smellscapes.

The smellscape is the olfactory environment as perceived and understood, consisting of odours and scents from multiple smell sources. To what extent can audiovisual information evoke the smells of a real, complex, and multimodal environment? To investigate smellscape imagination, we compared results from two studies. In the first, onsite participants (N = 15) made a sensory walk through seven locations of an open-air market. In the second, online participants (N = 53) made a virtual walk through the same locations reproduced with audio and video recordings. Responses in the form of free-form verbal annotations, ratings with semantic scales, and a 'smell wheel', were analysed for environmental quality, smell source type and strength, and hedonic tone. The degree of association between real and imagined smellscapes was measured through canonical correlation analysis. Hedonic tone, as expressed through frequency counts of keywords in free-form annotations was significantly associated, suggesting that smell sources might generally be correctly inferred from audiovisual information, when such imagination is required. On the other hand, onsite ratings of olfactory quality were not significantly associated with online ratings of audiovisual reproductions, when participants were not specifically asked to imagine smells. We discuss findings in the light of cross-modal association, categorisation, and memory recall of smells.

Cultural differences in music features across Taiwanese, Japanese and American markets.

BACKGROUND: Preferences for music can be represented through music features. The widespread prevalence of music streaming has allowed for music feature information to be consolidated by service providers like Spotify. In this paper, we demonstrate that machine learning classification on cultural market membership (Taiwanese, Japanese, American) by music features reveals variations in popular music across these markets. METHODS: We present an exploratory analysis of 1.08 million songs centred on Taiwanese, Japanese and American markets. We use both multiclass classification models (Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron (MLP)), and binary classification models, and interpret their results using variable importance measures and Partial Dependence Plots. To ensure the reliability of our interpretations, we conducted a follow-up study comparing Top-50 playlists from Taiwan, Japan, and the US on identified variables of importance. RESULTS: The multiclass models achieved moderate classification accuracy (GBDT = 0.69, MLP = 0.66). Accuracy scores for binary classification models ranged between 0.71 to 0.81. Model interpretation revealed music features of greatest importance: Overall, popular music in Taiwan was characterised by high acousticness, American music was characterised by high speechiness, and Japanese music was characterised by high energy features. A follow-up study using Top-50 charts found similarly significant differences between cultures for these three features. CONCLUSION: We demonstrate that machine learning can reveal both the magnitude of differences in music preference across Taiwanese, Japanese, and American markets, and where these preferences are different. While this paper is limited to Spotify data, it underscores the potential contribution of machine learning in exploratory approaches to research on cultural differences.

Estimation of Psychological Distress in Japanese Youth Through Narrative Writing: Text-Based Stylometric and Sentiment Analyses.

BACKGROUND: Internalizing mental illnesses associated with psychological distress are often underdetected. Text-based detection using natural language processing (NLP) methods is increasingly being used to complement conventional detection efforts. However, these approaches often rely on self-disclosure through autobiographical narratives that may not always be possible, especially in the context of the collectivistic Japanese culture. OBJECTIVE: We propose the use of narrative writing as an alternative resource for mental illness detection in youth. Accordingly, in this study, we investigated the textual characteristics of narratives written by youth with psychological distress; our research focuses on the detection of psychopathological tendencies in written imaginative narratives. METHODS: Using NLP tools such as stylometric measures and lexicon-based sentiment analysis, we examined short narratives from 52 Japanese youth (mean age 19.8 years, SD 3.1) obtained through crowdsourcing. Participants wrote a short narrative introduction to an imagined story before completing a questionnaire to quantify their tendencies toward psychological distress. Based on this score, participants were categorized into higher distress and lower distress groups. The written narratives were then analyzed using NLP tools and examined for between-group differences. Although outside the scope of this study, we also carried out a supplementary analysis of narratives written by adults using the same procedure. RESULTS: Youth demonstrating higher tendencies toward psychological distress used significantly more positive (happiness-related) words, revealing differences in valence of the narrative content. No other significant differences were observed between the high and low distress groups. CONCLUSIONS: Youth with tendencies toward mental illness were found to write more positive stories that contained more happiness-related terms. These results may potentially have widespread implications on psychological distress screening on online platforms, particularly in cultures such as Japan that are not accustomed to self-disclosure. Although the mechanisms that we propose in explaining our results are speculative, we believe that this interpretation paves the way for future research in online surveillance and detection efforts.

Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study.

BACKGROUND: COVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas. OBJECTIVE: This infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries. METHODS: We measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19-related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices-the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)-to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization. RESULTS: Our results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures' level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas). CONCLUSIONS: We quantitatively defined Japanese Yahoo users' concerns about COVID-19 by using the search counts of COVID-19-related search queries. Our results also showed that the LCI and LCIPP have external validity.

Cross-Modal Perception of Noise-in-Music: Audiences Generate Spiky Shapes in Response to Auditory Roughness in a Novel Electroacoustic Concert Setting.

Noise has become integral to electroacoustic music aesthetics. In this paper, we define noise as sound that is high in auditory roughness, and examine its effect on cross-modal mapping between sound and visual shape in participants. In order to preserve the ecological validity of contemporary music aesthetics, we developed Rama, a novel interface, for presenting experimentally controlled blocks of electronically generated sounds that varied systematically in roughness, and actively collected data from audience interaction. These sounds were then embedded as musical drones within the overall sound design of a multimedia performance with live musicians, Audience members listened to these sounds, and collectively voted to create the shape of a visual graphic, presented as part of the audio-visual performance. The results of the concert setting were replicated in a controlled laboratory environment to corroborate the findings. Results show a consistent effect of auditory roughness on shape design, with rougher sounds corresponding to spikier shapes. We discuss the implications, as well as evaluate the audience interface.