Nursing in the spotlight: Talk about nurses and the nursing profession on Twitter during the early COVID-19 pandemic.

BACKGROUND: Nurses comprise the largest portion of healthcare workers and are integral to the COVID-19 response. Twitter has become a popular platform for the public, including nurses, to engage in pandemic-related discourse. PURPOSE: We sought to analyze the representation of the nursing profession and characterize nurses' experiences during the pandemic from tweets published in April 2020. METHODS: We analyzed tweets using natural language processing, Word Adjacency Graph (WAG) Modeling, and thematic analysis. Authors independently reviewed 10% of raw tweets in each WAG-generated topic, qualitatively analyzed tweets, and identified emerging themes. FINDINGS: Six themes emerged: Support and Recognition of Nurses, Military Metaphors, Superhuman/Spiritual Metaphors, Advocacy, Personal Experiences with Nurses, and Social/Political Commentary. Public perception of nurses was positive, but nurses conveyed harsh realities of their work. DISCUSSION: Findings highlight discrepancies in nursing experiences and public perceptions of nursing. Further research should accurately identify and convey the complexities of the nursing profession.

Big Data and Dysmenorrhea: What Questions Do Women and Men Ask About Menstrual Pain?

BACKGROUND: Menstrual pain is highly prevalent among women of reproductive age. As the general public increasingly obtains health information online, Big Data from online platforms provide novel sources to understand the public's perspectives and information needs about menstrual pain. The study's purpose was to describe salient queries about dysmenorrhea using Big Data from a question and answer platform. MATERIALS AND METHODS: We performed text-mining of 1.9 billion queries from ChaCha, a United States-based question and answer platform. Dysmenorrhea-related queries were identified by using keyword searching. Each relevant query was split into token words (i.e., meaningful words or phrases) and stop words (i.e., not meaningful functional words). Word Adjacency Graph (WAG) modeling was used to detect clusters of queries and visualize the range of dysmenorrhea-related topics. We constructed two WAG models respectively from queries by women of reproductive age and bymen. Salient themes were identified through inspecting clusters of WAG models. RESULTS: We identified two subsets of queries: Subset 1 contained 507,327 queries from women aged 13-50 years. Subset 2 contained 113,888 queries from men aged 13 or above. WAG modeling revealed topic clusters for each subset. Between female and male subsets, topic clusters overlapped on dysmenorrhea symptoms and management. Among female queries, there were distinctive topics on approaching menstrual pain at school and menstrual pain-related conditions; while among male queries, there was a distinctive cluster of queries on menstrual pain from male's perspectives. CONCLUSIONS: Big Data mining of the ChaCha® question and answer service revealed a series of information needs among women and men on menstrual pain. Findings may be useful in structuring the content and informing the delivery platform for educational interventions.

Effects of Epilepsy on Language Functions: Scoping Review and Data Mining Findings.

Purpose: This study involved a scoping review to identify possible gaps in the empirical description of language functioning in epilepsy in adults. With access to social network data, data mining was used to determine if individuals with epilepsy are expressing language-related concerns. Method: For the scoping review, scientific databases were explored to identify pertinent articles. Findings regarding the nature of epilepsy etiologies, patient characteristics, tested language modalities, and language measures were compiled. Data mining focused on social network databases to obtain a set of relevant language-related posts. Results: The search yielded 66 articles. Epilepsy etiologies except temporal lobe epilepsy and older adults were underrepresented. Most studies utilized aphasia tests and primarily assessed single-word productions; few studies included healthy control groups. Data mining revealed several posts regarding epilepsy-related language problems, including word retrieval, reading, writing, verbal memory difficulties, and negative effects of epilepsy treatment on language. Conclusion: Our findings underscore the need for future specification of the integrity of language in epilepsy, particularly with respect to discourse and high-level language abilities. Increased awareness of epilepsy-related language issues and understanding the patients' perspectives about their language concerns will allow researchers and speech-language pathologists to utilize appropriate assessments and improve quality of care.

Epilepsy-related romantic and sexual relationship problems and concerns: Indications from Internet message boards.

The objective of this study was to, utilizing a Big Data set and innovative methods, explore romantic and sexual relationship-related concerns among people with epilepsy and their partners. We applied Word Adjacency Graph modeling to more than 2000 message board posts, and five distinct categories of romantic and sexual relationship-related concerns emerged. We conclude that persons with epilepsy are at particular risk for the experience of decrements in their romantic and sexual relationships, which can negatively impact their self-management and overall health.

Menopause and big data: Word Adjacency Graph modeling of menopause-related ChaCha data.

OBJECTIVE: To detect and visualize salient queries about menopause using Big Data from ChaCha. METHODS: We used Word Adjacency Graph (WAG) modeling to detect clusters and visualize the range of menopause-related topics and their mutual proximity. The subset of relevant queries was fully modeled. We split each query into token words (ie, meaningful words and phrases) and removed stopwords (ie, not meaningful functional words). The remaining words were considered in sequence to build summary tables of words and two and three-word phrases. Phrases occurring at least 10 times were used to build a network graph model that was iteratively refined by observing and removing clusters of unrelated content. RESULTS: We identified two menopause-related subsets of queries by searching for questions containing menopause and menopause-related terms (eg, climacteric, hot flashes, night sweats, hormone replacement). The first contained 263,363 queries from individuals aged 13 and older and the second contained 5,892 queries from women aged 40 to 62 years. In the first set, we identified 12 topic clusters: 6 relevant to menopause and 6 less relevant. In the second set, we identified 15 topic clusters: 11 relevant to menopause and 4 less relevant. Queries about hormones were pervasive within both WAG models. Many of the queries reflected low literacy levels and/or feelings of embarrassment. CONCLUSIONS: We modeled menopause-related queries posed by ChaCha users between 2009 and 2012. ChaCha data may be used on its own or in combination with other Big Data sources to identify patient-driven educational needs and create patient-centered interventions.

Word Adjacency Graph Modeling: Separating Signal From Noise in Big Data.

There is a need to develop methods to analyze Big Data to inform patient-centered interventions for better health outcomes. The purpose of this study was to develop and test a method to explore Big Data to describe salient health concerns of people with epilepsy. Specifically, we used Word Adjacency Graph modeling to explore a data set containing 1.9 billion anonymous text queries submitted to the ChaCha question and answer service to (a) detect clusters of epilepsy-related topics, and (b) visualize the range of epilepsy-related topics and their mutual proximity to uncover the breadth and depth of particular topics and groups of users. Applied to a large, complex data set, this method successfully identified clusters of epilepsy-related topics while allowing for separation of potentially non-relevant topics. The method can be used to identify patient-driven research questions from large social media data sets and results can inform the development of patient-centered interventions.

Finding the Patient's Voice Using Big Data: Analysis of Users' Health-Related Concerns in the ChaCha Question-and-Answer Service (2009-2012).

BACKGROUND: The development of effective health care and public health interventions requires a comprehensive understanding of the perceptions, concerns, and stated needs of health care consumers and the public at large. Big datasets from social media and question-and-answer services provide insight into the public's health concerns and priorities without the financial, temporal, and spatial encumbrances of more traditional community-engagement methods and may prove a useful starting point for public-engagement health research (infodemiology). OBJECTIVE: The objective of our study was to describe user characteristics and health-related queries of the ChaCha question-and-answer platform, and discuss how these data may be used to better understand the perceptions, concerns, and stated needs of health care consumers and the public at large. METHODS: We conducted a retrospective automated textual analysis of anonymous user-generated queries submitted to ChaCha between January 2009 and November 2012. A total of 2.004 billion queries were read, of which 3.50% (70,083,796/2,004,243,249) were missing 1 or more data fields, leaving 1.934 billion complete lines of data for these analyses. RESULTS: Males and females submitted roughly equal numbers of health queries, but content differed by sex. Questions from females predominantly focused on pregnancy, menstruation, and vaginal health. Questions from males predominantly focused on body image, drug use, and sexuality. Adolescents aged 12-19 years submitted more queries than any other age group. Their queries were largely centered on sexual and reproductive health, and pregnancy in particular. CONCLUSIONS: The private nature of the ChaCha service provided a perfect environment for maximum frankness among users, especially among adolescents posing sensitive health questions. Adolescents' sexual health queries reveal knowledge gaps with serious, lifelong consequences. The nature of questions to the service provides opportunities for rapid understanding of health concerns and may lead to development of more effective tailored interventions.