Enhancing big data in the social sciences with crowdsourcing: Data augmentation practices, techniques, and opportunities

Porter, Nathaniel D.; Verdery, Ashton M.; Gaddis, S. Michael

doi:10.1371/journal.pone.0233154

Cited by 22 publications

(15 citation statements)

References 60 publications

(74 reference statements)

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“…5.1. Collective Impact Regional Data Hubs, like 2-1-1 San Diego's CIE, Offer New Sources for High-Quality Quantitative Data on the Homeless Population, That Could Be Used to Replicate Study Findings and Expand Research of the Homeless Population in Other Geographies Future studies could explore the use of collective impact hub data [18][19][20] for advanced statistical quantitative research on the experiences of homelessness in other geographies.…”

Section: Discussionmentioning

confidence: 99%

“…Relatively few studies have used machine learning techniques like cluster analysis and decision trees to examine homelessness given the historic lack of quantitative data on those categorized as homeless [16]. The advent of the Collective Impact movement has furthered opportunities for quantitative research in this area through the development of more sophisticated data collection, storage, and management systems in social agencies [18][19][20]. Applying more advanced analytics strategies to data gathered from these shared data systems will provide opportunities to validate and expand on the insightful early research seeking to understand and alleviate homelessness in America.…”

Section: Recent Advances In Data Science Approaches To Studying Socia...mentioning

confidence: 99%

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Using K-Means Cluster Analysis and Decision Trees to Highlight Significant Factors Leading to Homelessness

Clark

Blumenfeld²,

Lal

et al. 2021

Mathematics

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Homelessness has been a persistent social concern in the United States. A combination of political and economic events since the 1960s has driven increases in poverty that, by 1991, had surpassed 1928 depression era levels in some accounts. This paper explores how the emerging field of behavioral economics can use machine learning and data science methods to explore preventative responses to homelessness. In this study, machine learning data mining strategies, specifically K-means cluster analysis and later, decision trees, were used to understand how environmental factors and resultant behaviors can contribute to the experience of homelessness. Prevention of the first homeless event is especially important as studies show that if a person has experienced homelessness once, they are 2.6 times more likely to have another homeless episode. Study findings demonstrate that when someone is at risk for not being able to pay utility bills at the same time as they experience challenges with two or more of the other social determinants of health, the individual is statistically significantly more likely to have their first homeless event. Additionally, for men over 50 who are not in the workforce, have a health hardship, and experience two or more other social determinants of health hardships at the same time, the individual has a high statistically significant probability of experiencing homelessness for the first time.

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Section: Discussionmentioning

confidence: 99%

Section: Recent Advances In Data Science Approaches To Studying Socia...mentioning

confidence: 99%

Using K-Means Cluster Analysis and Decision Trees to Highlight Significant Factors Leading to Homelessness

Clark

Blumenfeld²,

Lal

et al. 2021

Mathematics

View full text Add to dashboard Cite

show abstract

“…We chose Amazon Mechanical Turk (AMT) as our web-based platform as it would facilitate rapid, large-scale participant recruitment [ 8 ]. Importantly, AMT has become an increasingly accepted means of collecting responses from diverse participants [ 9 ].…”

Section: Methodsmentioning

confidence: 99%

Mobile Sensing Apps and Self-management of Mental Health During the COVID-19 Pandemic: Web-Based Survey

Suruliraj¹,

Bessenyei²,

Bagnell³

et al. 2021

JMIR Form Res

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Background During the COVID-19 pandemic, people had to adapt their daily life routines to the currently implemented public health measures, which is likely to have resulted in a lack of in-person social interactions, physical activity, or sleep. Such changes can have a significant impact on mental health. Mobile sensing apps can passively record the daily life routines of people, thus making them aware of maladaptive behavioral adjustments to the pandemic. Objective This study aimed to explore the views of people on mobile sensing apps that passively record behaviors and their potential to increase awareness and helpfulness for self-managing mental health during the pandemic. Methods We conducted an anonymous web-based survey including people with and those without mental disorders, asking them to rate the helpfulness of mobile sensing apps for the self-management of mental health during the COVID-19 pandemic. The survey was conducted in May 2020. Results The majority of participants, particularly those with a mental disorder (n=106/148, 72%), perceived mobile sensing apps as very or extremely helpful for managing their mental health by becoming aware of maladaptive behaviors. The perceived helpfulness of mobile sensing apps was also higher among people who experienced a stronger health impact of the COVID-19 pandemic ( β =.24; 95% CI 0.16-0.33; P <.001), had a better understanding of technology ( β =.17; 95% CI 0.08-0.25; P <.001), and had a higher education ( β =.1; 95% CI 0.02-0.19; P =.02). Conclusions Our findings highlight the potential of mobile sensing apps to assist in mental health care during the pandemic.

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“…8 Originally developed to outsource tasks such as market research and media transcription, MTurk has evolved into a recruitment channel for marketing and behavioral science research as evidenced by a study showing that a third of all MTurk tasks originated from academic research groups. 9,10 We surveyed individuals with recent experiences obtaining COVID-19 testing to characterize their behavior related to the notification of testing results to close contacts, and receptiveness of digital contact tracing efforts. The survey inquired about the following: demographic data, details of COVID-19 testing, experiences regarding the notification of test results to close contacts, experiences with public health contact tracing efforts, and their opinions of digital contact tracing services.…”

Section: Survey Designmentioning

confidence: 99%

A Web-Based Digital Contact Tracing Strategy Addresses Stigma Concerns Among Individuals Evaluated for COVID-19

Treviño

Keswani

Pourmand

2022

Telemedicine and e-Health

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Background: Conventional contact tracing approaches have not kept pace with the scale of the coronavirus disease 2019 (COVID-19) pandemic and the highly anticipated smartphone applications for digital contact tracing efforts are plagued by low adoption rates attributed to privacy concerns; therefore, innovation is needed in this public health capability. Methods: This study involved a cross-sectional, nonrepresentative, online survey in the United States of individuals tested for COVID-19. Testing survey items measured the performance of conventional contact tracing programs, quantified the stigma related to the notification of COVID-19 close contacts, and assessed the acceptability of a website service for digital contact tracing. Results: A sample of 668 (19.9%) individuals met the inclusion criteria and consented to participation. Among the 95 participants with COVID-19, results were received after a median of 2 days, 63.2% interacted with a contact tracing program a median of 2 days after receiving test results, 62.1% had close contacts, and 37.1% of participants with COVID-19 and close contacts did not disclose their results to all close contacts. Among all participants, 17% had downloaded a mobile application and 40.3% reported interest in a website service. One hundred and nine participants perceived stigma with the disclosure of COVID-19 test results; of these, 58.7% reported that a website service for close contact notification would decrease this stigma. Discussion: Conventional contact tracing programs did not comprehensively contact individuals who tested positive for COVID-19 nor did so within a meaningful time frame. Digital contact tracing innovations may address these shortcomings; however, the low penetration of mobile application services in the United States indicates that a suite of digital contact tracing tools, including website services, are warranted for a more exhaustive coverage of the population. Conclusions: Public health officials should develop a complementary toolkit of digital contact tracing strategies to enable effective pandemic containment strategies.

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Enhancing big data in the social sciences with crowdsourcing: Data augmentation practices, techniques, and opportunities

Cited by 22 publications

References 60 publications

Using K-Means Cluster Analysis and Decision Trees to Highlight Significant Factors Leading to Homelessness

Using K-Means Cluster Analysis and Decision Trees to Highlight Significant Factors Leading to Homelessness

Mobile Sensing Apps and Self-management of Mental Health During the COVID-19 Pandemic: Web-Based Survey

A Web-Based Digital Contact Tracing Strategy Addresses Stigma Concerns Among Individuals Evaluated for COVID-19

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