Social media captures demographic and regional physical activity

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review

BACKGROUND

Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges.

OBJECTIVE

This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used.

METHODS

We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies.

RESULTS

Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions.

CONCLUSIONS

Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

How the natural environment in downtown neighborhood affects physical activity and sentiment: Using social media data and machine learning

BACKGROUND

Natural environment might encourage physical exercise, hence enhancing human health and wellbeing. Social media offers an extensive repository of spatiotemporal data, containing details on the feelings and behaviors of individuals. However, investigations on physical activity and public sentiment in the natural environment of the downtown neighborhood are lacking in the existing literature.

METHODS

To extract environmental and behavioral information from social media data and other multi-source data, natural language processing, semantic segmentation, instance segmentation, and fully convolutional neural networks are employed. The research examines how neighborhood blue-green spaces and other health-promoting facilities affect physical activity and public sentiment.

RESULTS

The results reveal that blue space visibility, activity facilities, street furniture, and safety all have a favorable influence on physical activity with a social gradient. Amenities, perceived street safety and beauty positively correlated to public sentiment. The findings from social media about the environment and physical activity are consistent with traditional surveys from the same time period with a 0.588 kappa value.

CONCLUSION

According to our findings, social media data might be utilized to learn more about how urban environments influence people's physical activity patterns. Also, the health-promoting effects of blue space require more investigation.

Association of Core Muscle Endurance with Weekly Workout Time, Speed, and the Symmetry of Frontal Core Motion during Indoor Walking and Cycling

This study investigated the factors that influence core muscle endurance, i.e., the symmetry of frontal core motion during indoor walking and cycling, the symmetry of lateral core muscle endurance, the symmetry of the hip abductor strength, the weekly workout time and fast walking and cycling speeds, while controlling for gender. Seventy-nine healthy young adults participated in this study. In a regression analysis, the core muscle endurance time was the dependent variable. The independent variables were the symmetry of frontal core motion (measured using a wireless earbud sensor during walking and cycling), the symmetry of side plank time and of hip abductor strength, the weekly workout time and fast walking and cycling speeds. In the multiple regression analysis, weekly workout time, fast walking speed, symmetry of frontal core motion during fast cycling and symmetry of lateral side plank time predicted core muscle endurance (adjusted R2 = 0.42). Thus, clinicians and fitness personnel should consider the association of core muscle endurance with the symmetry of frontal core motion during cycling and the symmetry of side plank holding time, as well as with the weekly workout time and a fast walking speed, when designing core muscle exercise programmes.

Diet during the COVID-19 pandemic: An analysis of Twitter data

In this study, we measured the association between county characteristics and changes in healthy-food, fast-food, and alcohol tweets during the COVID-19 pandemic in the United States. Our analytic dataset consisted of 1,282,316 geotagged tweets that referenced food consumption posted before (63.2%) and during (36.8%) the pandemic and included all US states. We found the share of healthy-food tweets increased by 20.5% during the pandemic compared with pre-pandemic, while fast-food and alcohol tweets decreased by 9.4% and 11.4%, respectively. We also observed that time spent at home and more grocery stores per capita were associated with increased odds of healthy-food tweets and decreased odds of fast-food tweets. More liquor stores per capita was associated with increased odds of alcohol tweets. Our results highlight the potential impact of the pandemic on nutrition and alcohol consumption and the association between the built environment and health behaviors.

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We used Twitter data to quantify self-reported diet trends during the COVID-19 pandemic

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Healthy food consumption increased during the pandemic; alcohol consumption decreased

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Proximity to grocery stores and more time at home were associated with healthier diet

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Proximity to liquor stores corresponded with increased alcohol consumption

The COVID-19 pandemic upended many aspects of daily life, including how we eat and drink. Restaurant closures and retail restrictions likely impacted individuals’ consumption habits, but longitudinal surveys that monitor nutrition and/or alcohol intake are costly to administer and are prone to response bias. In this study, we use digital trace data from Twitter to track population-level patterns in nutritional intake. Linking geotagged tweets to data measuring US county characteristics and built environment, this study finds that increased time at home and access to grocery stores during the pandemic may have promoted healthy-food consumption. This study also suggests that access to alcohol retail establishments may have led to more drinking. These findings validate the importance of the built environment to health behaviors while highlighting how social media data may be used to assess the impact of public health crises.

Analytic Advances in Social Networks and Health in the Twenty-First Century

The study of social networks is increasingly central to health research for medical sociologists and scholars in other fields. Here, we review the innovations in theory, substance, data collection, and methodology that have propelled the study of social networks and health from a niche subfield to the center of larger sociological and scientific debates. In particular, we contextualize the broader history of network analysis and its connections to health research, concentrating on work beginning in the late 1990s, much of it in this journal. Using bibliometric and network visualization approaches, we examine the subfield's evolution over this period in terms of topics, trends, key debates, and core insights. We conclude by reflecting on persistent challenges and areas of innovation shaping the study of social networks and health and its intersection with medical sociology in the coming years.

Best Practices on Big Data Analytics to Address Sex-Specific Biases in Our Understanding of the Etiology, Diagnosis, and Prognosis of Diseases

A bias in health research to favor understanding diseases as they present in men can have a grave impact on the health of women. This paper reports on a conceptual review of the literature on machine learning or natural language processing (NLP) techniques to interrogate big data for identifying sex-specific health disparities. We searched Ovid MEDLINE, Embase, and PsycINFO in October 2021 using synonyms and indexing terms for (a) "women," "men," or "sex"; (b) "big data," "artificial intelligence," or "NLP"; and (c) "disparities" or "differences." From 902 records, 22 studies met the inclusion criteria and were analyzed. Results demonstrate that the inclusion by sex is inconsistent and often unreported, although the inclusion of men in these studies is disproportionately less than women. Even though artificial intelligence and NLP techniques are widely applied in health research, few studies use them to take advantage of unstructured text to investigate sex-related differences or disparities. Researchers are increasingly aware of sex-based data bias, but the process toward correction is slow. We reflect on best practices on using big data analytics to address sex-specific biases in understanding the etiology, diagnosis, and prognosis of diseases. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Can parkland mitigate mental health burden imposed by the COVID-19? A national study in China

The worldwide coronavirus disease 2019 (COVID-19) pandemic has seriously affected not only physical health but also mental wellbeing (i.e mental stress and suicide intention) of numerous urban inhabitants across the globe. While many studies have elucidated urban parkland enhances and mental wellbeing of urban residents, the potential for parkland to mitigate mental health burden imposed by the COVID-19 has received no attention. This nationwide study systematically explored the association between parkland, the COVID-19 pandemic situation and mental wellbeing from 296 cities in China. The study innovatively used big data from Baidu Search Engine to assess city-level mental wellbeing, thereby enabling comparisons among cities. The results show that the provision of parkland is positively associated with mental wellbeing during the COVID-19 epidemic. For COVID-19-related indicators, the geographical distance to Wuhan city, work resumption rate, and travel intensity within the city are also positively associated with mental wellbeing, while the number of COVID-19 infections and the proportion of migrants from Hubei Province for each city are negatively associated with mental wellbeing. Last, the most important finding is that parkland reduces the negative effect of COVID-19 on mental wellbeing during the COVID-19 epidemic. To achieve the goal of promoting mental wellbeing through urban planning and design during the future pandemics, policymakers and planners are advised to provide more well-maintained and accessible parkland and encourage residents to use them with proper precautions.

Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study

BACKGROUND

The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors.

OBJECTIVE

The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa.

METHODS

We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance.

RESULTS

The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence.

CONCLUSIONS

Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review

BACKGROUND

Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change.

OBJECTIVE

The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms.

METHODS

We performed a systematic review of the literature in September 2020 by searching three databases-PubMed, Web of Science, and Scopus-using relevant keywords, such as "social media," "online health communities," "machine learning," "data mining," etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies.

RESULTS

The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies.

CONCLUSIONS

Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.

Multidimensional mining of public opinion in emergency events

Purpose

The suddenness, urgency and social publicity of emergency events lead to great impacts on public life. The deep analysis of emergency events can provide detailed and comprehensive information for the public to get trends of events timely. With the development of social media, users prefer to express opinions on emergency events online. Thus, massive public opinion information of emergencies has been generated. Hence, this paper aims to conduct multidimensional mining on emergency events based on user-generated contents, so as to obtain finer-grained results.

Design/methodology/approach

This paper conducted public opinion analysis via fine-grained mining. Specifically, public opinion about an emergency event was collected as experimental data. Secondly, opinion mining was conducted to get users’ opinion polarities. Meanwhile, users’ information was analysed to identify impacts of users’ characteristics on public opinion.

Findings

The experimental results indicate that public opinion is mainly negative in emergencies. Meanwhile, users in developed regions are more active in expressing opinions. In addition, male users, especially male users with high influence, are more rational in public opinion expression.

Originality/value

To the best of the authors’ knowledge, this is the first research to identify public opinion in emergency events from multiple dimensions, which can get in-detail differences of users’ online expression.

Use of Social Media, Search Queries, and Demographic Data to Assess Obesity Prevalence in the United States

Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter's streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. Additionally, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.789 (95% CI, 0.785, 0.786) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.