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

ABSTRACT: Proponents of big data claim it will fuel a social research revolution, but skeptics challenge its reliability and decontextualization. The largest subset of big data is not designed for social research. Data augmentation-systematic assessment of measurement against known quantities and expansion of extant data with new information-is an important tool to maximize such data's validity and research value. Using trained research assistants or specialized algorithms are common approaches to augmentation but may not scale to big data or appease skeptics. We consider a third alternative: data augmentation with online crowdsourcing. Three empirical cases illustrate strengths and limitations of crowdsourcing, using Amazon Mechanical Turk to verify automated coding, link online databases, and gather data on online resources. Using these, we develop best practice guidelines and a reporting template to enhance reproducibility. Carefully designed, correctly applied, and rigorously documented crowdsourcing help address concerns about big data's usefulness for social research.

SUBMITTER: Porter ND

PROVIDER: S-EPMC7286483 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Porter Nathaniel D ND Verdery Ashton M AM Gaddis S Michael SM

PloS one 20200610 6

Proponents of big data claim it will fuel a social research revolution, but skeptics challenge its reliability and decontextualization. The largest subset of big data is not designed for social research. Data augmentation-systematic assessment of measurement against known quantities and expansion of extant data with new information-is an important tool to maximize such data's validity and research value. Using trained research assistants or specialized algorithms are common approaches to augment ...[more]

PMID: 32520948

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Project description:BACKGROUND:Polypharmacy is a key challenge in healthcare especially in older and multimorbid patients. The use of multiple medications increases the potential for drug interactions and for prescription of potentially inappropriate medications. eHealth solutions are increasingly recommended in healthcare, with big data analysis techniques as a major component. In the following we use the term analysis of big data as referring to the computational analysis of large data sets to find patterns, trends, and associations in large data sets collected from a wide range of sources in contrast to using classical statistics programs. It is hypothesized that big data analysis is able to reveal patterns in patient data that would not be identifiable using conventional methods of data analysis. The aim of this review was to evaluate whether there are existing big data analysis techniques that can help to identify patients consuming multiple drugs and to assist in the reduction of polypharmacy in patients. METHODS:A computerized search was conducted in February 2019 and updated in May 2020, using the PubMed, Web of Science and Cochrane Library databases. The search strategy was defined by the principles of a systematic search, using the PICO scheme. All studies evaluating big data analytics about patients consuming multiple drugs were considered. Two researchers assessed all search results independently to identify eligible studies. The data was then extracted into standardized tables. RESULTS:A total of 327 studies were identified through the database search. After title and abstract screening, 302 items were removed. Only three studies were identified as addressing big data analysis techniques in patients with polypharmacy. One study extracted antipsychotic polypharmacy data, the second introduced a decision support system to evaluate side-effects in patients with polypharmacy and the third evaluated a decision support system to identify polypharmacy-related problems in individuals. CONCLUSIONS:There are few studies to date which have used big data analysis techniques for identification and management of polypharmacy. There may be a need to further explore interdisciplinary collaboration between computer scientists and healthcare professionals, to develop and evaluate big data analysis techniques that can be implemented to manage polypharmacy.

Dataset Information

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

Publications

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

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