Microaggression clues from social media: revealing and counteracting the suppression of women's health care

Social Media and Nutritional Guidelines in Pregnancy

INTRODUCTION

Nutrition impacts health outcomes of pregnant people and their fetuses. Discussing nutrition with patients may be challenging for health care providers. In this vacuum, patients use social media for health information during pregnancy. Little is known about the type and quality of nutrition information that is available on social media. The aim of this study was to qualitatively explore the pregnancy nutrition messages contained in social media videos for content and evaluate the information's concordance with evidence-based guidelines.

METHODS

A review of current social media video content for nutrition in pregnancy was conducted across 4 platforms: YouTube, Instagram, Facebook, and TikTok. Content analysis was used to descriptively and categorically analyze the social media video content.

RESULTS

In the 62 social media videos reviewed, most were not consistent with established guidelines, were inaccurate, or presented a homogenous and unrealistic image of the pregnant person. Four categories of nutrition in pregnancy social media information emerged: what to eat, what not to eat, what I ate: aspirational, and what I ate: rebellion. Few videos were produced by health care organizations. Some were produced by anonymous organizations, with the majority made by pregnant or postpartum people. Many were testimonial or confessional in style. Despite regulations requiring identification of paid posts and advertisements, it was difficult to discern if videos were authentic sharing of patient experiences or advertisements.

DISCUSSION

There is incorrect and incomplete information on social media surrounding nutrition in pregnancy. Professional guidelines for nutrition in pregnancy are available but are not as accessible to patients as social media videos. Social media use is associated with disordered eating and increased risk of anxiety and depression. However, targeted interventions by health care providers that use social media platforms have been successful in the distribution of credible health information. Midwives and other health care providers should share evidence-based nutrition recommendation with patients to facilitate meaningful conversations around nutrition in pregnancy during in person and online interactions.

How Do Monetary Incentives Affect Healthcare Social Media Content? A Study Based on Topic Modeling and Sentiment Analysis (Preprint)

BACKGROUND

While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content.

OBJECTIVE

Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content?

METHODS

We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis-based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts.

RESULTS

Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50% more Steemit posts had analytical content and 33% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57% of Steemit posts had better quality than Reddit posts, and they were at least 52% more likely to like and comment on Steemit posts than Reddit posts.

CONCLUSIONS

It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information.