Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 13 de 13
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Front Public Health ; 9: 772236, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34778197

RESUMO

Background: The mental health of racial/ethnic minorities in the U.S. has been disproportionately impacted by the COVID-19 pandemic. This study examined the extent to which disruptions in employment and housing, coronavirus-specific forms of victimization and racial bias independently and conjointly contributed to mental health risk among Asian, Black, and Latinx adults in the United States during the pandemic. Methods: This study reports on data from 401 Asian, Black, and Latinx adults (age 18-72) who participated in a larger national online survey conducted from October 2020-June 2021, Measures included financial and health information, housing disruptions and distress in response to employment changes, coronavirus related victimization distress and perceived increases in racial bias, depression and anxiety. Results: Asian participants had significantly higher levels of COVID-related victimization distress and perceived increases in racial bias than Black and Latinx. Young adults (<26 years old) were more vulnerable to depression, anxiety, and coronavirus victimization distress than older respondents. Having at least one COVID-related health risk, distress in response to changes in employment and housing disruptions, pandemic related victimization distress and perceived increases in racial bias were positively and significantly related to depression and anxiety. Structural equation modeling indicated COVID-related increases in racial bias mediated the effect of COVID-19 related victimization distress on depression and anxiety. Conclusions: COVID-19 has created new pathways to mental health disparities among racial/ethnic minorities in the U.S. by exacerbating existing structural and societal inequities linked to race. Findings highlight the necessity of mental health services sensitive to specific challenges in employment and housing and social bias experienced by people of color during the current and future health crises.


Assuntos
COVID-19 , Vítimas de Crime , Racismo , Adolescente , Adulto , Idoso , Emprego , Habitação , Humanos , Saúde Mental , Pessoa de Meia-Idade , Pandemias , SARS-CoV-2 , Estados Unidos/epidemiologia , Adulto Jovem
2.
Psychol Methods ; 26(4): 398-427, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34726465

RESUMO

Technology now makes it possible to understand efficiently and at large scale how people use language to reveal their everyday thoughts, behaviors, and emotions. Written text has been analyzed through both theory-based, closed-vocabulary methods from the social sciences as well as data-driven, open-vocabulary methods from computer science, but these approaches have not been comprehensively compared. To provide guidance on best practices for automatically analyzing written text, this narrative review and quantitative synthesis compares five predominant closed- and open-vocabulary methods: Linguistic Inquiry and Word Count (LIWC), the General Inquirer, DICTION, Latent Dirichlet Allocation, and Differential Language Analysis. We compare the linguistic features associated with gender, age, and personality across the five methods using an existing dataset of Facebook status updates and self-reported survey data from 65,896 users. Results are fairly consistent across methods. The closed-vocabulary approaches efficiently summarize concepts and are helpful for understanding how people think, with LIWC2015 yielding the strongest, most parsimonious results. Open-vocabulary approaches reveal more specific and concrete patterns across a broad range of content domains, better address ambiguous word senses, and are less prone to misinterpretation, suggesting that they are well-suited for capturing the nuances of everyday psychological processes. We detail several errors that can occur in closed-vocabulary analyses, the impact of sample size, number of words per user and number of topics included in open-vocabulary analyses, and implications of different analytical decisions. We conclude with recommendations for researchers, advocating for a complementary approach that combines closed- and open-vocabulary methods. (PsycInfo Database Record (c) 2021 APA, all rights reserved).


Assuntos
Linguística , Vocabulário , Emoções , Humanos , Idioma , Personalidade
3.
J Pers ; 2021 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-34536229

RESUMO

OBJECTIVE: We explore the personality of counties as assessed through linguistic patterns on social media. Such studies were previously limited by the cost and feasibility of large-scale surveys; however, language-based computational models applied to large social media datasets now allow for large-scale personality assessment. METHOD: We applied a language-based assessment of the five factor model of personality to 6,064,267 U.S. Twitter users. We aggregated the Twitter-based personality scores to 2,041 counties and compared to political, economic, social, and health outcomes measured through surveys and by government agencies. RESULTS: There was significant personality variation across counties. Openness to experience was higher on the coasts, conscientiousness was uniformly spread, extraversion was higher in southern states, agreeableness was higher in western states, and emotional stability was highest in the south. Across 13 outcomes, language-based personality estimates replicated patterns that have been observed in individual-level and geographic studies. This includes higher Republican vote share in less agreeable counties and increased life satisfaction in more conscientious counties. CONCLUSIONS: Results suggest that regions vary in their personality and that these differences can be studied through computational linguistic analysis of social media. Furthermore, these methods may be used to explore other psychological constructs across geographies.

4.
Proc Natl Acad Sci U S A ; 118(39)2021 09 28.
Artigo em Inglês | MEDLINE | ID: mdl-34544875

RESUMO

On May 25, 2020, George Floyd, an unarmed Black American male, was killed by a White police officer. Footage of the murder was widely shared. We examined the psychological impact of Floyd's death using two population surveys that collected data before and after his death; one from Gallup (117,568 responses from n = 47,355) and one from the US Census (409,652 responses from n = 319,471). According to the Gallup data, in the week following Floyd's death, anger and sadness increased to unprecedented levels in the US population. During this period, more than a third of the US population reported these emotions. These increases were more pronounced for Black Americans, nearly half of whom reported these emotions. According to the US Census Household Pulse data, in the week following Floyd's death, depression and anxiety severity increased among Black Americans at significantly higher rates than that of White Americans. Our estimates suggest that this increase corresponds to an additional 900,000 Black Americans who would have screened positive for depression, associated with a burden of roughly 2.7 million to 6.3 million mentally unhealthy days.


Assuntos
Ansiedade/epidemiologia , Depressão/epidemiologia , Emoções/fisiologia , Homicídio/psicologia , Saúde Mental/etnologia , Polícia/estatística & dados numéricos , Racismo/psicologia , Adolescente , Adulto , Afro-Americanos/psicologia , Ira/fisiologia , Ansiedade/psicologia , Depressão/psicologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estados Unidos/epidemiologia , Adulto Jovem
5.
Alcohol Alcohol ; 2021 Aug 20.
Artigo em Inglês | MEDLINE | ID: mdl-34414405

RESUMO

AIMS: This pilot study aimed to identify associations of loneliness and daily alcohol consumption among US adults during the Coronavirus Disease-2019 pandemic. METHOD: Participants completed daily assessments for 30 days. RESULTS: Results suggest people who feel lonelier on average drink more alcohol, however, people who feel lonelier than usual drink less. CONCLUSION: Findings highlight the need to disaggregate within- and between-person components of alcohol use.

6.
J Med Internet Res ; 23(5): e26933, 2021 05 20.
Artigo em Inglês | MEDLINE | ID: mdl-33882014

RESUMO

As of March 2021, the SARS-CoV-2 virus has been responsible for over 115 million cases of COVID-19 worldwide, resulting in over 2.5 million deaths. As the virus spread exponentially, so did its media coverage, resulting in a proliferation of conflicting information on social media platforms-a so-called "infodemic." In this viewpoint, we survey past literature investigating the role of automated accounts, or "bots," in spreading such misinformation, drawing connections to the COVID-19 pandemic. We also review strategies used by bots to spread (mis)information and examine the potential origins of bots. We conclude by conducting and presenting a secondary analysis of data sets of known bots in which we find that up to 66% of bots are discussing COVID-19. The proliferation of COVID-19 (mis)information by bots, coupled with human susceptibility to believing and sharing misinformation, may well impact the course of the pandemic.


Assuntos
COVID-19/epidemiologia , Comunicação , Mídias Sociais/estatística & dados numéricos , Humanos , Pandemias , SARS-CoV-2/isolamento & purificação
7.
Proc Natl Acad Sci U S A ; 117(19): 10165-10171, 2020 05 12.
Artigo em Inglês | MEDLINE | ID: mdl-32341156

RESUMO

Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates. Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Sharecare Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up to r = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used.

8.
JMIR Public Health Surveill ; 6(1): e16191, 2020 03 26.
Artigo em Inglês | MEDLINE | ID: mdl-32213472

RESUMO

BACKGROUND: Substance use by youth remains a significant public health concern. Social media provides the opportunity to discuss and display substance use-related beliefs and behaviors, suggesting that the act of posting drug-related content, or viewing posted content, may influence substance use in youth. This aligns with empirically supported theories, which posit that behavior is influenced by perceptions of normative behavior. Nevertheless, few studies have explored the content of posts by youth related to substance use. OBJECTIVE: This study aimed to identify the beliefs and behaviors of youth related to substance use by characterizing the content of youths' drug-related tweets. Using a sequential explanatory mixed methods approach, we sampled drug-relevant tweets and qualitatively examined their content. METHODS: We used natural language processing to determine the frequency of drug-related words in public tweets (from 2011 to 2015) among youth Twitter users geolocated to Pennsylvania. We limited our sample by age (13-24 years), yielding approximately 23 million tweets from 20,112 users. We developed a list of drug-related keywords and phrases and selected a random sample of tweets with the most commonly used keywords to identify themes (n=249). RESULTS: We identified two broad classes of emergent themes: functional themes and relational themes. Functional themes included posts that explicated a function of drugs in one's life, with subthemes indicative of pride, longing, coping, and reminiscing as they relate to drug use and effects. Relational themes emphasized a relational nature of substance use, capturing substance use as a part of social relationships, with subthemes indicative of drug-related identity and companionship. We also identified topical areas in tweets related to drug use, including reference to polysubstance use, pop culture, and antidrug content. Across the tweets, the themes of pride (63/249, 25.3%) and longing (39/249, 15.7%) were the most popular. Most tweets that expressed pride (46/63, 73%) were explicitly related to marijuana. Nearly half of the tweets on coping (17/36, 47%) were related to prescription drugs. Very few of the tweets contained antidrug content (9/249, 3.6%). CONCLUSIONS: Data integration indicates that drugs are typically discussed in a positive manner, with content largely reflective of functional and relational patterns of use. The dissemination of this information, coupled with the relative absence of antidrug content, may influence youth such that they perceive drug use as normative and justified. Strategies to address the underlying causes of drug use (eg, coping with stressors) and engage antidrug messaging on social media may reduce normative perceptions and associated behaviors among youth. The findings of this study warrant research to further examine the effects of this content on beliefs and behaviors and to identify ways to leverage social media to decrease substance use in this population.


Assuntos
Mídias Sociais/estatística & dados numéricos , Transtornos Relacionados ao Uso de Substâncias/epidemiologia , Adolescente , Humanos , Processamento de Linguagem Natural , Pennsylvania/epidemiologia , Pesquisa Qualitativa , Adulto Jovem
9.
Artigo em Inglês | MEDLINE | ID: mdl-32053866

RESUMO

Excessive alcohol use in the US contributes to over 88,000 deaths per year and costs over $250 billion annually. While previous studies have shown that excessive alcohol use can be detected from general patterns of social media engagement, we characterized how drinking-specific language varies across regions and cultures in the US. From a database of 38 billion public tweets, we selected those mentioning "drunk", found the words and phrases distinctive of drinking posts, and then clustered these into topics and sets of semantically related words. We identified geolocated "drunk" tweets and correlated their language with the prevalence of self-reported excessive alcohol consumption (Behavioral Risk Factor Surveillance System; BRFSS). We then identified linguistic markers associated with excessive drinking in different regions and cultural communities as identified by the American Community Project. "Drunk" tweet frequency (of the 3.3 million geolocated "drunk" tweets) correlated with excessive alcohol consumption at both the county and state levels (r = 0.26 and 0.45, respectively, p < 0.01). Topic analyses revealed that excessive alcohol consumption was most correlated with references to drinking with friends (r = 0.20), family (r = 0.15), and driving under the influence (r = 0.14). Using the American Community Project classification, we found a number of cultural markers of drinking: religious communities had a high frequency of anti-drunk driving tweets, Hispanic centers discussed family members drinking, and college towns discussed sexual behavior. This study shows that Twitter can be used to explore the specific sociocultural contexts in which excessive alcohol use occurs within particular regions and communities. These findings can inform more targeted public health messaging and help to better understand cultural determinants of substance abuse.


Assuntos
Consumo de Bebidas Alcoólicas , Intoxicação Alcoólica , Características Culturais , Dirigir sob a Influência , Mídias Sociais , Consumo de Bebidas Alcoólicas/etnologia , Intoxicação Alcoólica/etnologia , Condução de Veículo , Feminino , Humanos , Masculino , Estados Unidos
10.
J Subst Abuse Treat ; 109: 50-55, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-31856951

RESUMO

BACKGROUND: Recovery support services, including in vivo (i.e., face to face) peer-based supports and social networks, are associated with positive effects on substance use disorder recovery outcomes. The translation of in vivo supports to digital platforms is a recent development that is mostly unexamined. The types of users and their engagement patterns of digital recovery support services (D-RSS), and the utility of objective and self-report data in predicting future recovery outcomes require further study to move the recovery support field forward. METHODS: De-identified individual user data from Sober Grid, a recovery social network site (R-SNS) smartphone application, for the years 2015-2018 was analyzed to identify the demographics, engagement patterns, and recovery outcomes of active users. Analysis of variance (ANOVA) tests were used to examine between generational group differences on activity variables and recovery outcomes. Logistic and linear regressions were used to identify significant predictors of sobriety length and relapse among users. RESULTS: The most active tercile of users (n = 1273; mAge = 39 years; 62% male) had average sobriety lengths of 195.5 days and had experienced 4.4 relapses on average since sign-up. Users have over 33,000 unilateral and nearly 14,000 bilateral connections. Users generated over 120,000 unique posts, 507,000 comments, 1617,000 likes, 12,900 check-ins, and 593,000 chats during the period of analysis. Recovery outcomes did not vary between generations, though user activity was significantly different between Generations (Millennials, Generation X, and Baby Boomers), with baby boomers and generation X having higher levels of engagement and connection among all activity markers. Logistic regression results revealed gender (female) was associated with a lower likelihood of reporting loneliness or sexual feelings as an emotional trigger. Linear regressions revealed generation, number of unilateral connections, and number of check-ins was associated with sobriety length, while generation and number of check-ins was associated with number of relapses. CONCLUSIONS: Active users of Sober Grid engage in several platform features that provide objective data that can supplement self-report data for analysis of recovery outcomes. Most commonly uses features are those similar to features readily available in open-ecosystem social network sites (e.g., Facebook). Prediction model results suggest that demographic factors (e.g., age, gender) and activity factors (e.g., number of check-ins) may be useful in deploying just-in-time interventions to prevent relapse or offer additional social support. Further empirical examination is needed to identify the utility of such interventions, as well as the mechanisms of support that accompany feature use or engagement with the D-RSS.


Assuntos
Recuperação da Saúde Mental , Aplicativos Móveis , Smartphone , Rede Social , Apoio Social , Adulto , Feminino , Humanos , Masculino
11.
Artigo em Inglês | MEDLINE | ID: mdl-34095902

RESUMO

The novelty and global scale of the COVID-19 pandemic has lead to rapid societal changes in a short span of time. As government policy and health measures shift, public perceptions and concerns also change, an evolution documented within discourse on social media. We propose a dynamic content-specific LDA topic modeling technique that can help to identify different domains of COVID-specific discourse that can be used to track societal shifts in concerns or views. Our experiments show that these model-derived topics are more coherent than standard LDA topics, and also provide new features that are more helpful in prediction of COVID-19 related outcomes including mobility and unemployment rate.

12.
Psychol Assess ; 31(1): 82-99, 2019 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-30299119

RESUMO

Beck's insight-that beliefs about one's self, future, and environment shape behavior-transformed depression treatment. Yet environment beliefs remain relatively understudied. We introduce a set of environment beliefs-primal world beliefs or primals-that concern the world's overall character (e.g., the world is interesting, the world is dangerous). To create a measure, we systematically identified candidate primals (e.g., analyzing tweets, historical texts, etc.); conducted exploratory factor analysis (N = 930) and two confirmatory factor analyses (N = 524; N = 529); examined sequence effects (N = 219) and concurrent validity (N = 122); and conducted test-retests over 2 weeks (n = 122), 9 months (n = 134), and 19 months (n = 398). The resulting 99-item Primals Inventory (PI-99) measures 26 primals with three overarching beliefs-Safe, Enticing, and Alive (mean α = .93)-that typically explain ∼55% of the common variance. These beliefs were normally distributed; stable (2 weeks, 9 months, and 19 month test-retest results averaged .88, .75, and .77, respectively); strongly correlated with many personality and wellbeing variables (e.g., Safe and optimism, r = .61; Enticing and depression, r = -.52; Alive and meaning, r = .54); and explained more variance in life satisfaction, transcendent experience, trust, and gratitude than the BIG 5 (3%, 3%, 6%, and 12% more variance, respectively). In sum, the PI-99 showed strong psychometric characteristics, primals plausibly shape many personality and wellbeing variables, and a broad research effort examining these relationships is warranted. (PsycINFO Database Record (c) 2018 APA, all rights reserved).


Assuntos
Atitude , Cultura , Mineração de Dados/métodos , Processamento de Linguagem Natural , Psicometria/instrumentação , Humanos , Psicometria/normas
13.
PLoS One ; 13(4): e0194290, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29617408

RESUMO

OBJECTIVES: The current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county. METHODS: Data from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis. RESULTS: Twitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption. CONCLUSIONS: Twitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.


Assuntos
Consumo de Bebidas Alcoólicas/epidemiologia , Mídias Sociais , Estudos Transversais , Humanos , Saúde Pública , Estados Unidos
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...