Dataset Information

Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining.

ABSTRACT: By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people's stress levels. Psychological studies using traditional surveys are time-consuming and contain cognitive and sampling biases, and therefore cannot be used to build large datasets for a real-time depression analysis. In this article, we propose a CorExQ9 algorithm that integrates a Correlation Explanation (CorEx) learning algorithm and clinical Patient Health Questionnaire (PHQ) lexicon to detect COVID-19 related stress symptoms at a spatiotemporal scale in the United States. The proposed algorithm overcomes the common limitations of traditional topic detection models and minimizes the ambiguity that is caused by human interventions in social media data mining. The results show a strong correlation between stress symptoms and the number of increased COVID-19 cases for major U.S. cities such as Chicago, San Francisco, Seattle, New York, and Miami. The results also show that people's risk perception is sensitive to the release of COVID-19 related public news and media messages. Between January and March, fear of infection and unpredictability of the virus caused widespread panic and people began stockpiling supplies, but later in April, concerns shifted as financial worries in western and eastern coastal areas of the U.S. left people uncertain of the long-term effects of COVID-19 on their lives.

SUBMITTER: Li D

PROVIDER: S-EPMC7400345 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining.

Li Diya D Chaudhary Harshita H Zhang Zhe Z

International journal of environmental research and public health 20200710 14

By 29 May 2020, the coronavirus disease (COVID-19) caused by SARS-CoV-2 had spread to 188 countries, infecting more than 5.9 million people, and causing 361,249 deaths. Governments issued travel restrictions, gatherings of institutions were cancelled, and citizens were ordered to socially distance themselves in an effort to limit the spread of the virus. Fear of being infected by the virus and panic over job losses and missed education opportunities have increased people's stress levels. Psychol ...[more]

PMID: 32664388

Similar Datasets

Project description:This paper investigates the feasibility, from a user perspective, of integrating a heterogeneous information network mining (HINM) technique into SensePlace3 (SP3), a web-based geovisual analytics environment. The core contribution of this paper is a user study that determines whether an analyst with minimal background can comprehend the network data modeling metaphors employed by the resulting system, whether they can employ said metaphors to explore spatial data, and whether they can interpret the results of such spatial analysis correctly. This study confirms that all of the above is, indeed, possible, and provides empirical evidence about the importance of a hands-on tutorial and a graphical approach to explaining data modeling metaphors in the successful adoption of advanced data mining techniques. Analysis of outcomes of data exploration by the study participants also demonstrates the kinds of insights that a visual interface to HINM can enable. A second contribution is a realistic case study that demonstrates that our HINM approach (made accessible through a visual interface that provides immediate visual feedback for user queries), produces a clear and a positive difference in the outcome of spatial analysis. Although this study does not aim to validate HINM as a data modeling approach (there is considerable evidence for this in existing literature), the results of the case study suggest that HINM holds promise in the (geo)visual analytics domain as well, particularly when integrated into geovisual analytics applications. A third contribution is a user study protocol that is based on and improves upon the current methodological state of the art. This protocol includes a hands-on tutorial and a set of realistic data analysis tasks. Detailed evaluation protocols are rare in geovisual analytics (and in visual analytics more broadly), with most studies reviewed in this paper failing to provide sufficient details for study replication or comparison work.

Project description:ImportanceAn increased risk of depressive symptoms has been associated with the transition to menopause, but the risk of depressive symptoms in the early postmenopausal years has not been well characterized.ObjectivesTo identify within-woman changes in depressive symptoms during a 14-year period around menopause, determine associations of a history of depression with the pattern of depressive symptoms, and evaluate the rate of change in reproductive hormones as predictors of depressive symptoms following menopause.Design, setting, and participantsA randomly identified, population-based sample in Philadelphia County, Pennsylvania, of 203 late-reproductive-age women who were premenopausal at baseline and reached natural menopause.Main outcomes and measuresCenter for Epidemiologic Studies Depression Scale.ResultsThe prevalence of high scores on the Center for Epidemiologic Studies Depression Scale decreased from 10 years before to 8 years after the final menstrual period (FMP), with a decrease of approximately 15% of baseline per year (odds ratio, 0.85; 95% CI, 0.81-0.89; P < .001). Relative to the FMP, the risk of depressive symptoms was higher in the years before and lower in the years after the FMP. Among women with a history of depression, the likelihood of depressive symptoms was more than 13 times greater overall and 8 times greater after menopause compared with women with no depression history. Among women who first experienced depressive symptoms approaching menopause, the risk of depressive symptoms declined after the FMP, with a significantly lower risk the second year after menopause. The risk of depressive symptoms after menopause decreased by 35% for each unit (SD) increase before the FMP in the log rate of change of follicle-stimulating hormone (odds ratio, 0.65; 95% CI, 0.46-0.91; P = .01).Conclusions and relevanceThe FMP was pivotal in the overall pattern of decreasing depressive symptoms in midlife women, with higher risk before and lower risk after the FMP. A history of depression strongly increased the risk both before and after menopause. Women who had no history of depression before the menopause transition had a low risk of depressive symptoms 2 or more years after the FMP.

Project description:BackgroundIndividuals frequently turning to social media to discuss medical conditions and medication, sharing their experiences and information and asking questions among themselves. These online discussions can provide valuable insights into individual perceptions of medical treatment, and increasingly, studies are focusing on the potential use of this information to improve health care management.ObjectiveThe objective of this infodemiology study was to identify social media posts mentioning paracetamol-containing products to develop a better understanding of patients' opinions and perceptions of the drug.MethodsPosts between January 2003 and March 2019 containing at least one mention of paracetamol were extracted from 18 French forums in May 2019 with the use of the Detec't (Kap Code) web crawler. Posts were then analyzed using the automated Detec't tool, which uses machine learning and text mining methods to inspect social media posts and extract relevant content. Posts were classified into groups: Paracetamol Only, Paracetamol and Opioids, Paracetamol and Others, and the Aggregate group.ResultsOverall, 44,283 posts were analyzed from 20,883 different users. Post volume over the study period showed a peak in activity between 2009 and 2012, as well as a spike in 2017 in the Aggregate group. The number of posts tended to be higher during winter each year. Posts were made predominantly by women (14,897/20,883, 71.34%), with 12.00% (2507/20,883) made by men and 16.67% (3479/20,883) by individuals of unknown gender. The mean age of web users was 39 (SD 19) years. In the Aggregate group, pain was the most common medical concept discussed (22,257/37,863, 58.78%), and paracetamol risk was the most common discussion topic, addressed in 20.36% (8902/43,725) of posts. Doliprane was the most common medication mentioned (14,058/44,283, 31.74%) within the Aggregate group, and tramadol was the most commonly mentioned drug in combination with paracetamol in the Aggregate group (1038/19,587, 5.30%). The most common unapproved indication mentioned within the Paracetamol Only group was fatigue (190/616, with 16.32% positive for an unapproved indication), with reference to dependence made by 1.61% (136/8470) of the web users, accounting for 1.33% (171/12,843) of the posts in the Paracetamol Only group. Dependence mentions in the Paracetamol and Opioids group were provided by 6.94% (248/3576) of web users, accounting for 5.44% (342/6281) of total posts. Reference to overdose was made by 245 web users across 291 posts within the Paracetamol Only group. The most common potential adverse event detected was nausea (306/12843, 2.38%) within the Paracetamol Only group.ConclusionsThe use of social media mining with the Detec't tool provided valuable information on the perceptions and understanding of the web users, highlighting areas where providing more information for the general public on paracetamol, as well as other medications, may be of benefit.

Dataset Information

Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining.

Publications

Modeling Spatiotemporal Pattern of Depressive Symptoms Caused by COVID-19 Using Social Media Data Mining.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets