scholarly journals Large-scale social media language analysis reveals emotions and behaviours associated with nonmedical prescription drug use

2021 ◽  
Author(s):  
Mohammed Ali Al-Garadi ◽  
Yuan-Chi Yang ◽  
Yuting Guo ◽  
Sangmi Kim ◽  
Jennifer S. Love ◽  
...  
Keyword(s):  
Language Processing ◽  
Prescription Drugs ◽  
Large Scale ◽  
Positive Emotions ◽  
Health Concern ◽  
Control Group ◽  
The Past ◽  
Prescription Drug Use ◽  
Language Analysis ◽  
Twitter Users

AbstractNonmedical use of prescription drugs (NMPDU) is a global health concern. The extent of, behaviors and emotions associated with, and reasons for NMPDU are not well-captured through traditional instruments such as surveys, prescribing databases and insurance claims. Therefore, this study analyses ∼130 million public posts from 87,718 Twitter users in terms of expressed emotions, sentiments, concerns, and potential reasons for NMPDU via natural language processing. Our results show that users in the NMPDU group express more negative emotions and less positive emotions, more concerns about family, the past and body, and less concerns related to work, leisure, home, money, religion, health and achievement, compared to a control group (i.e., users who never reported NMPDU). NMPDU posts tend to be highly polarized, indicating potential emotional triggers. Gender-specific analysis shows that female users in the NMPDU group express more content related to positive emotions, anticipation, sadness, joy, concerns about family, friends, home, health and the past, and less about anger, compared to males. The findings of the study can enrich our understanding of NMPDU.

scholarly journals From Text to Thought: How Analyzing Language Can Advance Psychological Science

2020 ◽  
Author(s):  
Joshua Conrad Jackson ◽  
Joseph Watts ◽  
Johann-Mattis List ◽  
Ryan Drabble ◽  
Kristen Lindquist
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Statistical Power ◽  
Culturally Diverse ◽  
Large Scale ◽  
Human Cognition ◽  
Comparative Linguistics ◽  
Psychological Science ◽  
Language Analysis ◽  
The Mind

Humans have been using language for thousands of years, but psychologists seldom consider what natural language can tell us about the mind. Here we propose that language offers a unique window into human cognition. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—comparative linguistics and natural language processing—are already contributing to how we understand emotion, creativity, and religion, and overcoming methodological obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods, and highlight the best way to combine language analysis techniques with behavioral paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

scholarly journals Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter

2020 ◽  
pp. 1-8
Author(s):  
Sameh N. Saleh ◽  
Christoph U. Lehmann ◽  
Samuel A. McDonald ◽  
Mujeeb A. Basit ◽  
Richard J. Medford
Keyword(s):  
Language Processing ◽  
Public Perception ◽  
Large Scale ◽  
Community Support ◽  
Cross Sectional Study ◽  
Public Understanding ◽  
Cross Sectional ◽  
Social Distancing ◽  
English Only ◽  
Twitter Users

Abstract Objective: Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter. Design: Retrospective cross-sectional study. Methods: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. Results: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0–0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise). Conclusions: Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation.

scholarly journals From Text to Thought: How Analyzing Language Can Advance Psychological Science

2020 ◽  
Author(s):  
Joshua Conrad Jackson ◽  
Joseph Watts ◽  
Johann-Mattis List ◽  
Curtis Puryear ◽  
Ryan Drabble ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Statistical Power ◽  
Culturally Diverse ◽  
Large Scale ◽  
Human Cognition ◽  
Comparative Linguistics ◽  
Psychological Science ◽  
Language Analysis ◽  
The Mind

Humans have been using language for thousands of years, but psychologists seldom consider what natural language can tell us about the mind. Here we propose that language offers a unique window into human cognition. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—comparative linguistics and natural language processing—are already contributing to how we understand emotion, creativity, and religion, and overcoming methodological obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods, and highlight the best way to combine language analysis techniques with behavioral paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

scholarly journals Studying how individuals who express the feeling of loneliness in an online loneliness forum communicate in a non-loneliness forum: An Observational Study (Preprint)

2021 ◽  
Author(s):  
Anietie Andy
Keyword(s):  
Language Processing ◽  
Language Use ◽  
Latent Dirichlet Allocation ◽  
Well Being ◽  
Health Concern ◽  
Control Group ◽  
Online Forums ◽  
Cohen’S D ◽  
Linguistic Inquiry Word Count ◽  
Cohen's D

BACKGROUND Loneliness is a public health concern and increasingly individuals experiencing loneliness are seeking support on online forums - some of which focus on discussions around loneliness (loneliness forum). Loneliness may influence how individuals express themselves and interact with others in different settings or forums not related to loneliness or well-being (non-loneliness forums). Hence, in order to design and implement appropriate and efficient online loneliness interventions, it is important to understand how individuals who express loneliness on online loneliness forums communicate in non-loneliness forums they belong; this could provide insights into the support needs of these users. OBJECTIVE This work studies how users who express the feeling of loneliness in an online loneliness forum communicate in an online non-loneliness forum. METHODS 2,401 users who expressed loneliness in posts published on a loneliness forum on Reddit and had published posts in a non-loneliness forum were identified. Using a natural language processing method, Latent dirichlet allocation (LDA), a psycholinguistic dictionary, Linguistic Inquiry Word Count (LIWC), and the word-score based language features: valence, arousal, and dominance, we determine the language use differences in posts published in the non-loneliness forum by these users compared to a control group of users who did not belong to any loneliness forum on Reddit. RESULTS We find that in posts published in the non-loneliness forum, users who expressed loneliness tend to use more words associated with the LIWC categories on sadness (cohen’s d =0.10) and seeking to socialize (cohen’s d =0.114) and use words associated with valence (cohen’s d=0.364) and dominance (cohen’s d = 0.117); also, they tend to publish posts related to LDA topics such as relationships (cohen’s d= 0.105) and family and friends / mental health (cohen’s d = 0.10). CONCLUSIONS There are clear distinctions in language use in non-loneliness forum posts by users who express loneliness compared to a control group of users. These findings can help with the design and implementation of online interventions around loneliness.

scholarly journals From Text to Thought: How Analyzing Language Can Advance Psychological Science

2021 ◽  
pp. 174569162110048
Author(s):  
Joshua Conrad Jackson ◽  
Joseph Watts ◽  
Johann-Mattis List ◽  
Curtis Puryear ◽  
Ryan Drabble ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Statistical Power ◽  
Culturally Diverse ◽  
Large Scale ◽  
Comparative Linguistics ◽  
Psychological Science ◽  
Language Analysis ◽  
The Mind ◽  
Psychological Paradigms

Humans have been using language for millennia but have only just begun to scratch the surface of what natural language can reveal about the mind. Here we propose that language offers a unique window into psychology. After briefly summarizing the legacy of language analyses in psychological science, we show how methodological advances have made these analyses more feasible and insightful than ever before. In particular, we describe how two forms of language analysis—natural-language processing and comparative linguistics—are contributing to how we understand topics as diverse as emotion, creativity, and religion and overcoming obstacles related to statistical power and culturally diverse samples. We summarize resources for learning both of these methods and highlight the best way to combine language analysis with more traditional psychological paradigms. Applying language analysis to large-scale and cross-cultural datasets promises to provide major breakthroughs in psychological science.

scholarly journals A Quest to Identify Prostate Cancer Circulating Biomarkers with a Bench-to-Bedside Potential

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jaspreet Singh Batra ◽  
Swati Girdhani ◽  
Lynn Hlatky
Keyword(s):  
Prostate Cancer ◽  
Large Scale ◽  
Specific Antigen ◽  
Health Concern ◽  
Treatment Modalities ◽  
Potential Candidate ◽  
Circulating Biomarkers ◽  
Major Health ◽  
The Past

Prostate cancer (PCA) is a major health concern in current times. Ever since prostate specific antigen (PSA) was introduced in clinical practice almost three decades ago, the diagnosis and management of PCA have been revolutionized. With time, concerns arose as to the inherent shortcomings of this biomarker and alternatives were actively sought. Over the past decade new PCA biomarkers have been identified in tissue, blood, urine, and other body fluids that offer improved specificity and supplement our knowledge of disease progression. This review focuses on superiority of circulating biomarkers over tissue biomarkers due to the advantages of being more readily accessible, minimally invasive (blood) or noninvasive (urine), accessible for sampling on regular intervals, and easily utilized for follow-up after surgery or other treatment modalities. Some of the circulating biomarkers like PCA3, IL-6, and TMPRSS2-ERG are now detectable by commercially available kits while others like microRNAs (miR-21, -221, -141) and exosomes hold potential to become available as multiplexed assays. In this paper, we will review some of these potential candidate circulating biomarkers that either individually or in combination, once validated with large-scale trials, may eventually get utilized clinically for improved diagnosis, risk stratification, and treatment.

The ATTEND trial: An alternative explanation with implications for future recovery and rehabilitation clinical trials

2017 ◽  
Vol 13 (2) ◽  
pp. 112-116 ◽  
Author(s):  
Carolee Winstein
Keyword(s):  
Clinical Trials ◽  
Large Scale ◽  
Optimal Recovery ◽  
Stroke Recovery ◽  
Control Group ◽  
Experimental Intervention ◽  
The Past ◽  
Group A ◽  
Intervention Trials ◽  
The Individual

Over the past decade, ATTEND is one of only a handful of moderate to large-scale nonpharmacologic stroke recovery trials with a focus on rehabilitation. While unique in some respects, its test of superiority for the experimental intervention returned negative/neutral results, with no differences in outcome between the experimental intervention and an appropriate control group – a result not uncommon to the majority of moderate to large stroke rehabilitation intervention trials (i.e. six out of eight conducted in the past decade). The authors offer a number of potential explanations for the negative outcome, all of which have merit. We choose not to dwell on these possibilities, but rather offer a radically different explanation, one which has implications for future rehabilitation clinical trials. Our premise is that the process of neurorehabilitation is complex and multifaceted, but most importantly, for success, it requires a genuine collaboration between the patient and the clinician or caregiver to effect optimal recovery. This collaborative relationship must be defined by the unique perspective of each patient. By doing so, we acknowledge the importance of the individual patient’s values, goals, perspectives, and capacity. Rehabilitation scientists can design what arguably is a scientifically sound intervention that is evidence-based and even with preliminary data supporting its efficacy, but if the patient does not value the target outcome, does not fully engage in the therapy, or does not expect the intervention to succeed, the likelihood of success is poor. We offer this opinion, not to be critical, but to suggest a paradigm shift in the way in which we conduct stroke recovery and rehabilitation trials.

scholarly journals 486. Understanding Public Perception of COVID-19 Social Distancing on Twitter

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S309-S309
Author(s):  
Sameh N Saleh ◽  
Christoph Lehmann ◽  
Samuel McDonald ◽  
Mujeeb Basit ◽  
Richard J Medford
Keyword(s):  
Language Processing ◽  
Topic Modeling ◽  
Public Perception ◽  
Large Scale ◽  
Statistical Significance ◽  
Leisure Activities ◽  
Community Support ◽  
Social Distancing ◽  
Positive Sentiment ◽  
Twitter Users

Abstract Background Managing and changing public opinion and behavior are vital for social distancing to successfully slow transmission of COVID-19, preserve hospital resources, and prevent overwhelming the healthcare system’s resources. We sought to leveraging organic, large-scale discussion on Twitter about social distancing to understand public’s beliefs and opinions on this policy. Methods Between March 27 and April 10, 2020, we sampled 574,903 English tweets that matched the two most trending social distancing hashtags at the time, #socialdistancing and #stayathome. We used natural language processing techniques to conduct a sentiment analysis that identifies tweet polarity and emotions. We also evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and compared the sentiment by topic. Results There was net positive sentiment toward both #socialdistancing and #stayathome with mean sentiment scores of 0.150 (standard deviation [SD], 0.292) and 0.144 (SD, 0.287) respectively. Tweets were also more likely to be objective (median, 0.40; IQR, 0 to 0.6) with approximately 30% of all tweets labeled as completely objective. Approximately half (50.4%) of all tweets primarily expressed joy and one-fifth expressed fear and surprise each (Figure 1). These trends correlated well with topic clusters identified by frequency including leisure activities and community support (i.e., joy), concerns about food insecurity and effects of the quarantine (i.e., fear), and unpredictability of COVID and its unforeseen implications (i.e., surprise) (Table 1). Table 1. Topic clusters identified by topic modeling. Words contributing to the model are shown in decreasing order of weighting. The topics are labeled manually based on these words. The number of tweets primarily with that topic, mean sentiment, mean subjectivity, and sample tweets are also included. Figure 1. Emotion analysis for all tweets and stratified by tweets with the hashtag #socialdistancing and #stayathome. Comparison between the two hashtags is done using Chi-squared testing. Bonferroni correction was used to define statistical significance at a threshold of p = 0.008 (0.05/n, where n = 6 since 6 comparisons were completed). Conclusion The positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing measures in the early stages of their implementation. Disclosures All Authors: No reported disclosures

Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Exposure Assessment ◽  
Large Scale ◽  
First Generation ◽  
Cancer Epidemiology ◽  
Policy Changes ◽  
The Past ◽  
New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

Contributory Factors for Obesity in Elderly: Review of the Literature

2019 ◽  
Vol 15 (3) ◽  
Author(s):  
Nur Raihan Ismail ◽  
Noor Aman Hamid
Keyword(s):  
English Language ◽  
The Elderly ◽  
Dietary Factors ◽  
Health Concern ◽  
Cross Sectional ◽  
Contributory Factors ◽  
The Past ◽  
Prevalence Of Obesity ◽  
Program Intervention ◽  
Future Education

Introduction: The prevalence of obesity has been rising, adding to morbidity and mortality. As the proportion of elderly aged 60 years and above grows, so too the prevalence of obesity among this population. Obesity in the elderly is a rapidly growing public health concern as it contributes to significant changes in the health of older people. Objective: This review aims to assess the contributory factors for obesity in the elderly over the past decade. Methods: A literature search was conducted. The search was restricted to articles written in the English language published from 2008 to 2018. Qualitative studies were excluded. Results: A total of 19 full articles were retrieved, of which 18 cross-sectional and one cohort were included. The contributory factors were divided into three components: (a) socio demographic characteristics, (b) medical history and dietary factors and (c) environmental factors. Conclusions: This review informs an emerging knowledge regarding contributory factors for obesity and has implications for future education and program intervention in fighting obesity in the elderly.

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