Current Social Media Conversations about Genetics and Genomics in Health: A Twitter-Based Analysis (2024)

Abstract

Keywords: Genetics, Genomics, Social media, Health information, Twitter

Introduction

Genomic information is increasingly being incorporated into patient care, advertised through direct-to-consumer testing, and discussed in the news and through social media [1–5]. At the same time, social media continues to advance and alter the ways the public accesses medical and health information, shifting how people communicate about and understand their health [6–10]. Today, health information seeking is the third most popular online activity among adults with internet access with 80% of internet users having looked online for information about health topics (e.g., commentary or experience about a health topic, online videos, review of drugs or medical treatment) [8]. Historically, individuals sought information from health-specific websites, but there has been a drastic shift toward the use of social media (e.g., Facebook and Twitter) to gather and share health information [11].

The pace at which access to personalized genomic information is becoming available, combined with the public’s regular use of social media to access health information, has created a new space for conversations related to genetics and genomics. Social media (specifically health-related content available on social media) may be an important contributing factor for an individual’s beliefs about health conditions and subsequent health behaviors and decision-making [12, 13]. Given the speed at which genomic science has advanced and information is becoming available via social media, we sought to characterize existing conversations about genomics on Twitter, where articles and information are commonly shared in short posts called tweets [11]. The aim of this report was to provide a description of how Twitter users are sharing information about genomics and what information is most discussed among users.

Methods

To better understand user conversations related to genomics and health, we obtained Twitter data from Crimson Hexagon, a data collection and analysis toolkit that includes posts from social media networks (Boston, MA, USA). Twitter is an online micro-blogging platform that is among the most commonly accessed social media sites and used globally by millions of individuals [14]. This platform allows users to post tweets: real-time messages with up to 140 characters (during our study period), images, and videos. Individuals can communicate digital content frequently and quickly by passing information through followers, re-tweets of quotes, and replies to other users’ conversations [14, 15]. Each Twitter user has an account (@) and the user can share information categorically with their followers and non-followers via hashtags (e.g., #genomics) or the user’s profile. Most conversations are publicly available (90%), providing a rich source of information about people’s perceptions and communication about specific topics [16].

We used search terms to collect tweets that employed Boolean logic for increased precision and were any combination of the words “genetic,” “genomic,” “health,” “public health,” “disease,” “disorder,” “condition,” and “medicine.” These terms could have appeared in posts or any accompanying hashtags. We collected tweets posted between October 1, 2016, and October 1, 2017. The yearlong date range offers insight about activity trends.

After having obtained the tweets from Crimson Hexagon, we used features of the software (e.g., Clusters, Topic Wheel) to identify salient themes, analyze emergent content, and pinpoint the most commonly used words in the data set. Crimson Hexagon uses an algorithm that recognizes patterns of words to interpret text and has been tested and validated by the Pew Research Center [17, 18]. The Topic Wheel algorithm analyzed the collection of tweets in the data set and identified the most frequently used words to inductively produce primary categories and subcategories of the most prevalent themes in the data set. We also used the Clusters feature, which employs an algorithm that produces a graph showing which words in a data set are most often used in conjunction with one another. The Clusters illustrated associations between words and provided a further description of key themes in conversation. Both the Topic Wheel and Clusters features of the platform allowed us to assess a random selection of 10,000 tweets for analysis. All themes and topics from Crimson Hexagon were checked by the authors for clarity and consistency.

Crimson Hexagon also described the “emotions” of the tweets in the data set. The program analyzed the tweets based on words commonly associated with positive, negative, or neutral sentiment. Beyond sentiment, the platform’s algorithm computed different types of emotion, such as fear and joy. The users in the data set were further analyzed through Crimson Hexagon’s user demographic features. Users’ gender and age were collected from their profiles, in addition to identifying the place of origin of the posted tweet. Top influencers were also identified, based on the total number of followers and users’ reach.

Results

A total of 347,179 tweets were obtained from Crimson Hexagon that met our search criteria. The average monthly volume of tweets was 28,432, with a weekly average volume of 6,549 and a daily average volume of 949 (Fig. 1). Over the course of the year, there were multiple spikes in activity; notably during the week of July 31 to August 5 there were over 30,000 tweets (conversations focused on sharing about rare genetic disorders). On a weekly basis, most of the tweets occurred on Fridays (17%). Thirteen percent of the tweets were categorized as negative and 15% as positive. The tweets categorized as having an “emotion” by Crimson Hexagon (51%) displayed joy (21%), sadness (11%), disgust (9%), and fear (8%) (Table 1; Fig. 2).

Discussion

This brief report documents the current health-related conversations about genetics and genomics occurring on Twitter. We found over 300,000 tweets related to this topic on Twitter between October 2016 and October 2017. Considering there are over 200 billion tweets per year, discussion about genetics, genomics, and health makes up only a small portion of the conversation; however, it is important to consider those who are engaged in the conversation and the type of information discussed. This analysis is an initial step in better understanding topics of interest related to genetics and genomics. It demonstrates the potential utility of using social media data to describe conversations and attitudes surrounding this topic.

The top influencers in this data set were news sources. Each of these influencers had between 2 million and 10 million followers, demonstrating the potential wide-spread reach of information they share. These accounts primarily shared information about genetic disorders deemed newsworthy, such as Ehlers-Danlos syndrome, spinal muscle atrophy, and Crohn’s disease. Because social media plays a role in shaping public opinions and conversations, the endorsem*nt of ideas from large groups, like the influencers in this sample, tends to drive social media conversation on a topic [19–22]. Opinions on Twitter evolve rapidly, but once a public opinion stabilizes, it can be difficult to change [22]. Although this analysis is a snapshot in time, it is important to consider how certain topics and opinions about genetics are portrayed through social media. Endorsem*nts and information shared by top influencers do not necessarily represent individuals’ views, but they do offer insight about information being shared about a topic and may have a strong effect on the conversations about a specific topic. In this instance, the conversations related to genetics and genomics were highly focused on rare conditions.

It is not surprising that a large part of content in this data set was related to rare diseases and disorders, as social media platforms can serve as a forum for individuals to share information and experiences about a topic [23]. Given the considerable lack of information available about rare diseases, individuals may turn to Twitter and other social media sites to gather information, share personal stories, and create a community [24].

Another notable theme was a tendency for users to describe how they believed genes caused certain diseases. Individuals tended to overemphasize genetics as a determinant of diseases (e.g., they shared information about the extent to which genes were related to obesity). These discussions often centered on the risk of disease and personal stories. Users also noted concerns about policies and legislation that could influence their ability to receive testing. It is important to consider how the public perceives policies related to genetics and genomics to understand the consequences of genetic testing uptake. Further, there are opportunities for reliable sources, such as health agencies, to strategically engage in and contribute to the conversation related to genomics and public health. For example, tweets that use hashtags have been associated with a higher level of engagement [25].

There are several limitations to consider. First, we only provide observational data and cannot establish associations or causalities. Second, we did not analyze the quality of the information sources or the information provided in the tweets. Other studies have identified misinformation about certain diseases being shared via social media, which influences health beliefs and behaviors [11, 26–28]. Because we did not assess the quality of the information discussed, we were not able to address any differences in spread of information based on the accuracy of the content. Thus, future studies should assess the quality of the information being shared and consider the types of information being shared related to genomics (e.g., what type of information is more widely spread). In addition, while we focused on existing health-related internet use that may influence causal beliefs about genomics and health, we did not evaluate how individual users came to use Twitter in communicating ideas. We were also unable to identify if and how this information may impact causal beliefs and subsequent health behaviors. Our search strategy was targeted with broad search terms to capture the majority of tweets about genetics, genomics, health, and disease; however, we likely underreported the number of tweets during this period related to genomics and health, as some tweets may not have used any of the specific search terms from our criteria. For example, we did not capture tweets that discussed specific diseases (e.g., leukemia) unless the tweet included the combination of search terms outlined in the Methods section. Finally, the majority of tweets were from the USA, followed by the UK. It is unclear whether these data are generalizable to other cultural settings.

Ongoing research is needed to track public discourse about genetics and other emerging health topics. In addition, future research should consider how these conversations may shape causal beliefs and be shaped by other characteristics (e.g., sociodemographic characteristics). Such research could lead to further theory-building that would inform interventions to improve the dissemination of information about genomics and health. Tracking changes in conversations over time and encouraging researchers to engage in social media to promote reliable scientific information are important steps to ensure that accurate information is available to the public about the rapidly advancing conversations related to genetics and genomics. Our study provides baseline insight about genetics- and genomics-related conversations on Twitter that reflect the current public discourse surrounding the topic. This assessment is a first step toward tracking the public discourse and could lead to better-informed, more precise efforts to intervene in the public discourse related to genomics.

Acknowledgements

There is no funding to report for this study. We would like to acknowledge Sylvia Chou for her comments on the final version of this manuscript.

Footnotes

References