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While the partisanship of U.S. media outlets is well documented, the role of political affiliation in social media adoption and online discussions is much less studied. Social media allow individuals to contribute to the dissemination of information. As a result, understanding the relationship between political affiliation and content-sharing behaviors provides insight into whose voices are represented in social media content. Political affiliation might be particularly pertinent to engagement with politically charged topics, such as the COVID-19 pandemic. This paper uses survey data collected in Spring 2020 to examine the role of political affiliation in both social media adoption and content sharing about COVID-19 on three platforms: Facebook, Instagram, and Twitter. Subsequently, we look at how these factors relate to COVID-19 knowledge, an important outcome with broader health implications. We find that political affiliation relates to both site adoption and sharing on the platforms, but these relationships are not uniform across all three platforms. We find no connection between political affiliation and knowledge about the virus but we do find that content sharing on two of the examined platforms is negatively related to knowledge. This work has larger implications for other contexts where polarized and politicized arguments take place, such as climate change and other contentious topics as it concerns both whose voices are heard in these discourses as well as what people can take away from engaging with content.

Contents

Introduction
Social media adoption and sharing
Methods
Platform adoption by political affiliation
Sharing COVID-19 content on social media
COVID-19 knowledge and content sharing
Discussion
Conclusion

 

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Introduction

As the world ground to a halt because of the COVID-19 pandemic in spring 2020, people turned to the Internet and news media to keep up with the constantly evolving situation (Hargittai, 2022; Kemp, 2020). From the onset of the pandemic, the topic of the virus took on a political charge. Both print and network news outlets in the United States were highly politicized and polarized in their coverage of the pandemic (Hart, et al., 2020). Research indicates that such variation in coverage across news outlets could affect how people perceived the pandemic and its severity (Halpern, 2020). The different messaging from liberal and conservative news media thus potentially exacerbated the public’s polarized perceptions of COVID-19 by political affiliation (Halpern, 2020; Hart, et al., 2020). Since social media have come to play an important role in public discourse as well as in the dissemination of information during times of crisis (Abdullah, et al., 2017; Saud, et al., 2020), it is worth asking whether sharing content about COVID-19 on social media varied by political affiliation.

Political affiliation has repeatedly been linked to various outcomes concerning COVID-19 (Calvillo, et al., 2020; Freiling, et al., 2023; Gadarian, et al., 2021; Rothgerber, et al., 2020), particularly in the United States (Stroebe, et al., 2021). For example, individuals with more conservative values were less likely to adhere to the recommendations regarding physical distancing (Gadarian, et al., 2021; Rothgerber, et al., 2020) and more likely to believe in misinformation (Calvillo, et al., 2020) than those with liberal beliefs. So far, little scholarship has examined the relationship between political affiliation and online behaviors in relation to the pandemic and how this in turn can affect various outcomes. Understanding who shared content about the virus might provide insights not only into who actively engaged with the topic through their social media usage, but also into who was most likely to contribute to the public discourse about it. Given the large number of people who engage with social media, including when it comes to news (Matsa and Liedke, 2022) and the topic of the pandemic in particular (Hargittai, 2022), it is important to understand whose voices might be more or less represented in related material on some of the most widely used online platforms.

In examining the relationship between political affiliation and social media adoption and use, this study extends past research that reveals that such behaviors differ by sociodemographic background (e.g., age, gender, education) and Internet experiences (e.g., autonomy of use, skills) (Blank and Lutz, 2017; Hargittai and Litt, 2011). Given the political nature of many topics discussed online, engaging with content on social media might not only be a function of one’s sociodemographic background and Internet experiences, but also one’s political affiliation. Nonetheless, existing scholarship has hardly considered political affiliation as a potential predictor for site adoption (Vogels, et al., 2021 is a notable exception) and online sharing behaviors. Yet, political affiliation might be a particularly decisive determinant for contributing online about politically charged topics such as COVID-19 (Halpern, 2020; Hart, et al., 2020). Variation in participation in ongoing public discourse on social media has implications for whose perspectives are represented in the content on such platforms, as well as who reaps benefits of active participation. One potential benefit could be better understanding a situation such as being more informed about the coronavirus. This in turn could lead to more informed choices about someone’s daily behaviors and keep people safer (Hargittai, 2022).

Rather than conceptualizing social media adoption and use as monolithic, we consider three social media platforms separately. By doing so, we build on prior research revealing that patterns in platform adoption and online participation can differ by platform (Hargittai, 2015). Looking at social media platforms individually allows us to capture these nuances and disentangle how the relationships play out in each environment (Matassi and Boczkowski, 2021). This paper investigates the role of political affiliation in platform adoption and sharing content about COVID-19 on Facebook, Instagram, and Twitter.

 

Social media adoption and sharing

Over the past two decades, social media have become a prominent medium for social interaction, public discourse, and news (e.g., Matsa and Liedke, 2022; Wike, et al., 2022). During the COVID-19 pandemic, social media use skyrocketed (Kemp, 2020), becoming a lifeline for people to stay in touch with the world. Indeed, research shows that in periods of disruption, social media allow people to receive and share up-to-date information (Abdullah, et al., 2017; Adebimpe, et al., 2015) and to connect with others (Abdullah, et al., 2017). During the early days of the COVID-19 pandemic, people used social media to increase awareness, share information, and receive both emotional and social support (Saud, et al., 2020).

While there is vast research examining social media and the social dynamics it facilitates, most such studies focus on single platforms (Matassi and Boczkowski, 2021). The two most researched platforms are Facebook and Twitter as indicated by their popularity as keywords in journal articles (Puschmann and Pentzold, 2021). There have been calls to research other platforms (Blank and Lutz, 2017; Rains and Brunner, 2015), as this focus provides a limited view and ignores other sites that have become popular and influential (Oriz-Ospina, 2019). For example, the widely popular image-focused platform Instagram surpassed one billion users in 2020 (Enberg, 2020), compared to considerably fewer users on Twitter, which much more research addresses. Studying multiple platforms concurrently reveals a variance in the sociodemographic composition of users across platforms (Blank and Lutz, 2017; Hargittai, 2015). Similarly, content sharing does not look the same on different sites (Hargittai, et al., 2018). Differing user bases likely indicate different types of content people can and do encounter on a platform. As a result, academics have stressed the importance of breaking down social media usage and considering platforms separately (e.g., Hargittai, 2015; Matassi and Boczkowski, 2021).

Research that explores social media use across different platforms shows that people engage with platforms for different purposes (Bayor, et al., 2018) and topics (Hargittai, et al.,, 2018). Often, each platform serves a specific purpose to a given user, causing them to interact in diverging ways with various sites (Bayor, et al., 2018). Similarly, the perceived appropriateness of sharing emotionally loaded content differs by platform (Waterloo, et al., 2018). Among the three platforms we study in this paper, research has found that users view Facebook and Twitter as better for sharing negative emotions, while perceiving Instagram to be the appropriate place for sharing more positive emotions (Waterloo, et al., 2018). These differences are likely reflected in the content people share, including on the topic of COVID-19. Past research shows that while Facebook, Instagram, and Twitter have all been used to share political news, the rates at which this happens varies with users most likely to share news on Facebook, followed by Twitter, and then Instagram (Soroka and Krupnikov, 2021). The current study extends this line of research beyond what is a focus usually on one type of political news sharing.

Political affiliation and content sharing

Prior research has identified different factors related to site adoption (Blank and Lutz, 2017; Hargittai, 2015; Hargittai and Litt, 2011) and online participation such as age, gender, and education (see Hargittai and Jennrich, 2016 for an overview), but few studies have considered the role of political affiliation. One Pew study found differences in site adoption based on partisanship; Democrats were more likely than Republicans to use most platforms although the difference for Facebook was not significant (Vogels, et al., 2021). Similarly, Hargittai and Litt (2011) considered whether interest in various topics was related to Twitter adoption among a group of young adults, but found no such relationship for politics (whereas interest in entertainment and celebrities explained adopting that particular platform). The present paper examines whether political affiliation relates to site adoption and content sharing across three platforms using a national sample of U.S. adults.

When looking at actions online, some studies have considered political affiliation in relation to specific online behaviors such as sharing news online (Weeks and Holbert, 2013) or online political activities (Hargittai and Shaw, 2013). This research has not led to consistent findings. For example, one study finds that partisans were more likely to share news than non-partisans (Weeks and Holbert, 2013). In their analysis of survey data on offline and online political engagement among first-year college students, Hargittai and Shaw (2013) do not find evidence of a relationship between partisanship and a range of engagements with political content online. An analysis of Swiss political parties’ Facebook content revealed discrepancies in user engagement depending on which party shared content, with the most left- and right-wing parties receiving bigger shares in engagement (Maitra and Hänggli Fricker, 2023). Some work has found that political affiliation matters for sharing misinformation on social media (Freiling. et al., 2023; Guess. et al., 2019; Morosoli. et al., 2022). These various findings suggest that differences in engagement may depend on users’ political affiliation, which the present study investigates.

Research specifically on content sharing during the COVID-19 pandemic is sparse. Some studies examine users’ intentions when sharing information (e.g., Hashim, et al., 2020) or the type of content people saw being shared during this time (e.g., Saud, et al., 2020). A few studies have examined social media use during the pandemic, finding differences in who maintained and increased digital communication and contributed on social media by sociodemographic factors and Internet experiences (Campos-Castillo and Laestadius, 2020; Nguyen, Hargittai, et al., 2021). The majority of studies about sharing in the context of the COVID-19 pandemic have focused on misinformation (e.g., Freiling, et al., 2023), but since misinformation makes up a small percentage of social media posts (Gabarron, et al., 2021; Guess, et al., 2019), it is worthwhile to focus on content sharing more broadly.

Research questions

Given the above identified gaps in the literature, this paper answers the following research questions:

1: How does political affiliation relate to the adoption of Facebook, Instagram, and Twitter? 2: How does political affiliation relate to sharing content about COVID-19 on social media?        2a: Restricted to users of the respective platforms, how does political affiliation relate to sharing content about COVID-19? 3: How do political affiliation and content sharing on social media relate to knowledge about COVID-19?

The above questions advance research in three ways. First, we examine the role of political affiliation in platform adoption and sharing COVID-19-related content. One’s political views and beliefs might be a particularly relevant predictor of such behaviors given the political nature of the topic. Second, past research on social media use during the COVID-19 pandemic has not distinguished practices across platforms. We advance the agenda by differentiating content sharing on three popular social media platforms: Facebook, Instagram, and Twitter. Third, we consider whether political affiliation and content sharing relate to an important COVID-19-related outcome: knowledge about the virus.

 

Methods

To answer the research questions, we analyze national survey data we collected a few weeks into initial COVID-19 lockdowns in April 2020 in the United States. We contracted with the online survey firm Cint for the data collection and requested quotas for age, gender, education, and region. The data represent all states plus Washington D.C. The survey included attention-check questions (Berinsky, et al., 2014) and only respondents who met the criteria were included in the final data set. The final sample consists of 1,374 adults 18 and over.

Independent variables

Political affiliation. We measured political affiliation by asking people if they identified as a Republican, Democrat, independent, or no preference. We created dummy variables for Democrats, independents, and Republicans counting those who identified with each as belonging to that group. Just over a third (36.4 percent) identified as Democrat, and just under a third identified as independent (31.8 percent) and Republican (31.5 percent) respectively.

Sociodemographics. To determine respondents’ age, we asked people for their birth year and subtracted that from 2020. Age ranges from 18 to 91 (mean: 45.6). We measured gender by offering three options: male, female, and other, which we recoded into a binary female variable (1= female, 0= not female; there was one respondent who chose “other”). More than half of the sample is female (53.9 percent). For education, we offered six answer options, which we recoded into three dummy variables reflecting: high school or less (49.1 percent), some college (21.4 percent), and college degree or higher (29.5 percent). Household income was measured with 13 income ranges, which we recoded to their mid-point values. For the logistic regression, we logged this variable. The average income was $59,103.50 (SD: 52,156.6). Metropolitan status is reflected in three variables: rural (16.2 percent), suburban (38.4 percent), and urban (45.5 percent). The majority of participants were White (64.7 percent), followed by Hispanic (15.1 percent), Black (12.7 percent), Asian (5 percent), and Native American/Pacific Islander (2.1 percent). Table 1 provides an overview of the sample.

 

Table 1: Descriptive statistics of the sample.
	Percent	Mean	SD	N
Age (18–82)		45.6	15.9	1,374
Income (US$5,000–300,000)		59,103.5	52,156.6	1,372
Female	53.9			1,374
Metropolitan status
Rural	16.2			1,374
Suburban	38.4			1,374
Urban	45.5			1,374
Education
HS or less	49.1			1,374
Some college	21.4			1,374
BA or more	29.5			1,374
Race and ethnicity
White	64.7			1,367
Black	12.7			1,367
Hispanic	15.1			1,367
Asian	5.0			1,367
Native American	2.1			1,367
Political affiliation
Democrat	36.7			1,364
Republican	31.5			1,364
Independent	31.8			1,364
Internet experiences
Autonomy of use (0–5)		3.2	1.3	1,373
Social media skills (1–5)		3.8	1.2	1,373

 

Internet experiences. We included two measures related to respondents’ Internet experiences. For autonomy of use, we asked participants on which of five devices they have Internet access at home. On average, participants had access to the Internet on 3.2 devices (SD: 1.3). We measured social media skills as a type of Internet skill, given that we are examining social media uses. We asked participants to rate their level of understanding of six terms related to social media on a 5-point scale (i.e., “privacy settings”, “meme”, “tagging”, “followers”, “viral”, and “hashtag”). The average of these items constitutes people’s social media skills (3.8, SD: 1.2, Cronbach’s α: .93).

Dependent variables

Platform adoption. To measure social media adoption, the survey asked respondents whether and how often they use various social media platforms with three answer options: “never,” “few times a week or less,” and “daily or almost daily”. For each platform, we created a dummy variable to indicate whether people used a platform or not (1=yes, 0=no).

Sharing COVID-19 content. To measure whether people shared content about COVID-19 on social media, the survey asked respondents who had indicated using Facebook, Instagram, and Twitter, respectively, whether they had shared the following types of content on COVID-19 on said platform:

• tips on how to avoid getting infected
• information about symptoms
• numbers or charts about its spread
• governmental rules about what people are allowed to do
• religious sentiments and teachings related to it
• humor, jokes, funny content related to it
• gratitude expressed toward health care workers
• news coverage about it

For each platform, we created a dummy variable for each content type, indicating whether the respondent had shared that type of content on it. We then aggregated all content types into one dichotomous variable by platform. These three variables indicate whether people had shared any COVID-19-related content on Facebook, Instagram, and Twitter, respectively (1=yes, 0=no). Overall, around half of the sample (50.9 percent) had shared at least one form of COVID-19 related content on at least one platform. The most popular type of content shared was tips to avoid infection and the least popular was sharing religious sentiments and teachings about the pandemic. The most popular platform on which people shared content was Facebook (Table 4a and 4b show details).

COVID-19 knowledge. The COVID-19 knowledge score is based on two sets of questions in the survey that assessed people’s knowledge about (1) the virus and (2) ways to avoid infection. The first measure contained five multiple-choice questions about the virus. For each, we asked participants to pick the correct answer and if they were unsure, to take their best guess. The questions covered widely discussed topics and the correct answers are based on the World Health Organization’s (WHO) (2020) recommendations for the public [1]. The questions addressed:

• What people should do if they were in close contact with someone infected (correct answer: self-quarantine)
• What common symptoms were (dry cough and a fever)
• The timespan between contracting the virus and showing symptoms (up to two weeks)
• Who is at most risk for severe health consequences from the virus (older people with certain pre-existing conditions)
• What can be said about people who have been tested positive for COVID-19 but are in good health (that they are contagious regardless of whether they show symptoms)

The second set of questions asked about strategies to avoid getting infected with COVID-19. For this measure, we offered 17 different strategies, six of which were effective, the rest was ineffective according to the WHO’s (2020) recommendations. Participants were asked to pick all ways to reduced infection risk and if they were unsure to give their best guess [2]. Listed below are the six correct options:

• To wash their hands with soap
• To keep a distance of six feet (two meters) from other people
• To avoid handshakes
• To not touch one’s eyes, nose or mouth with one’s hands
• To clean and disinfect frequently touched surfaces
• Not to leave the house

These sets of questions combined resulted in the overall knowledge score, where zero indicates no knowledge and 11 having gotten all the answers correct. The average knowledge score was 9.5, showing that many participants had a good basic knowledge about the virus and how to avoid getting infected. In fact, 45.4 percent got all questions right.

Analysis

We first run bivariate Chi-squared tests to examine Facebook, Instagram, and Twitter adoption by political affiliation (Table 2). To see whether these results are robust when controlling for other factors, we fit logistics regressions with adoption of each platform as the outcome (Table 3). Subsequently, we examine who shared COVID-19-related content on each platform by political affiliation by calculating Chi-squared tests considering the full sample (Table 4a). Next, we do the same, but this time restricting the analysis to users of the given platform, respectively (Table 4b).

We fit logistic regression models for sharing content on each of the three social media platforms on the full sample (Table 5a) as well as on restricted samples of platforms users (Table 5b). In all regression models, we control for sociodemographic characteristics and Internet experiences. We analyze the prevalence of such content-sharing for both the full sample and restricted to users of a specific platform, because we are interested in who in general (full sample) is responsible for COVID-19-related content on social media while also wanting to see who among the users of a specific platform are most likely to engage in such sharing. Lastly, we examine the relationship between content sharing on each platform and COVID-19 knowledge by running OLS regression for COVID-19 knowledge (Table 6). The model controls for sociodemographic characteristics, political affiliation, and Internet experience.

 

Platform adoption by political affiliation

Table 2 contains the bivariate results corresponding to the first research question about the relationship between political affiliation and the adoption of Facebook, Instagram, and Twitter. Most of the sample uses Facebook (82.5 percent). There is no statistically significant difference between the rate at which Democrats and Republicans are present on that platform. Instagram is the second most popular platform among respondents with approximately half using it (51.7 percent). There are differences by political affiliation as to who is on the platform with 53.4 percent of Democratics versus 47.2 percent of Republicans. Twitter was the least popular platform with 43.8 percent of the sample having adopted it. Among individuals identifying as Democrats, 57.9 percent use the micro-blogging platform compared to 41.3 percent of Republicans.

 

Table 2: Adoption of social media platform by political affiliation. Note: For regression coefficients, * p < .05; ** p < .01; *** p < .001.
	Full sample	Democrat	Republican
Facebook	82.5	81.4	84.7
Instagram	51.7	57.9***	47.2*
Twitter	43.8	53.4***	41.3

 

 

Table 3: Logistic regression on social media platform adoption. Note: For regression coefficients, * p < .05; ** p < .01; *** p < .001.
	Facebook		Instagram		Twitter
	b	SE	b	SE	b	SE
Political affiliation (base = Republican)
Democrat	-0.3	0.2	-0.002	0.2	0.3	0.2
Independent	-0.2	0.2	-0.2	0.2	-0.4*	0.2
Age	0.003	0.01	-0.1***	0.005	-0.03***	0.004
Household income (logged)	-0.1	0.1	0.2**	0.1	-0.1	0.1
Female	0.3*	0.1	0.1	0.1	-0.6***	0.1
Metropolitan status (base = Urban)
Rural	0.2	0.2	-0.05	0.2	-0.02	0.2
Suburban	0.2	0.2	0.3*	0.1	0.2	0.1
Education (base = High school or less)
Some college	-0.2	0.2	0.4**	0.2	0.4*	0.2
Bachelor’s degree or more	-0.4	0.2	0.3*	0.2	0.6***	0.2
Race and ethnicity (base = White)
Black	-0.3	0.2	1.0***	0.2	0.6**	0.2
Hispanic	0.03	0.2	0.5**	0.2	0.3	0.2
Asian	0	0.4	0.4	0.3	0	0.3
Native American	0.01	0.6	-0.02	0.4	0.5	0.4
Internet experiences
Autonomy of use	0.2**	0.1	0.1*	0.1	0.2***	0.1
Social media skills	0.4***	0.1	0.4***	0.1	0.3***	0.1
Constant	1	1	-1.8*	0.8	-0.5	0.8
N	1,352		1,351		1,351
Log likelihood	-592.1		-724.4		-785
AIC	1,216.10		1,480.90		1,602.00

 

Next, by running logistical regression analyses on platform adoption (see Table 3 for the results), we examine whether the differences we observe above by political affiliation are robust to controlling for other factors. For Facebook, political leaning remains unrelated, while women are more likely to use the platform, and both autonomy of Internet use and social media skills are linked to its adoption. In the case of Instagram, once we control for sociodemographic factors and online experiences (autonomy of use and skills), political affiliation is no longer relevant for its use. In the case of Twitter, when controlling for the background variables, identifying as independent as compared to Republican affiliation is negatively related to platform adoption.

 

Sharing COVID-19 content on social media

To examine whether Democrats and Republicans shared COVID-19-related content on social media at different rates, we first look at the bivariate analyses presented in Tables 4a and 4b. Table 4a considers sharing for the full sample, while Table 4b restricts the analyses to users of each platform. We discuss the results that are statistically significant. We then report the results from the logistic regressions, in which we control for background characteristics and Internet experiences. For the dependent variable of sharing COVID-19-related content, we fit logistic regression on the full sample (Table 5a) and on the restricted sample of platform users (Table 5b). We report significant results here by platform.

Facebook. We find no relationship between political affiliation and any type of content sharing when looking at the full sample. When only looking at Facebook users, there are a couple of differences concerning specific content types. Over a third (38.1 percent) of Democrats on the platform shared tips to avoid infection, which is significantly more than the 31.3 percent of Republicans who had done so. Regarding the sharing of news coverage about the virus, 33.2 percent of Democrats did so, which is statistically significantly higher than the 25.3 percent of Republicans who had done so. The logistic regression results do not indicate a relationship between political affiliation and sharing COVID-19 related content on Facebook for either the full or the restricted sample (Tables 5a and 5b).

Instagram. A quarter (25.8 percent) of Democrats shared at least one form of COVID-19-related content on Instagram compared to 19.8 percent of Republicans (see Table 4a). Specific content that was higher among Democrats than Republicans included expressing gratitude towards health care workers, sharing news coverage, as well as numbers or charts about the spread of the virus. When restricting the sample to Instagram users (Table 4b), we do not find any relationships between political affiliation and sharing content on COVID-19. When controlling for sociodemographic background and Internet experiences (Tables 5a and 5b), political affiliation is not a significant predictor for sharing content related to COVID-19 on Instagram.

Twitter. Among Democrats in the sample, 24.4 percent have shared at least one form of COVID-19-related content in general, which is significantly higher than the 19.8 percent of Republicans. Twitter is the platform where we observe the most statistically significant differences by political affiliation. Democrats were more likely to share the following types of content (see Table 4a for details): tips on how to avoid getting infected; expressions of gratitude toward health care workers; humor, jokes, funny content related to, news coverage; and information about symptoms. In contrast, Republicans were especially less likely to share news coverage and avoidance tips. When restricting the analyses to platform users (Table 4b), we do not find the same relationships between political affiliation and sharing behaviors except for Republicans continuing to be much less likely to share avoidance tips. Again, when controlling for sociodemographic background and Internet experiences, these effects no longer hold. We find no connection between political affiliation and content sharing on Twitter for either sample (Tables 5a and 5b).

 

Table 4a: Experiences with sharing COVID-19 content on social media platforms by political affiliation — Full sample. Note: For regression coefficients, * p < .05; ** p < .01; *** p < .001.
	Full sample	Facebook			Instagram			Twitter
		All	Dem	Rep	All	Dem	Rep	All	Dem	Rep
Sharing any type of content about COVID-19	50.9	44.4	47.2	45.8	21.4	25.8**	19.8	18.3	24.4***	19.8
Avoidance tips	35.9	28.0	31.0	26.5	10.0	12.0	10.0	8.7	12.6***	5.8*
Gratitude toward health care workers	37.1	27.1	28.8	26.3	11.4	14.4*	9.8	9.6	12.2*	8.6
Humor and jokes	35.1	26.0	26.4	25.6	11.1	13.4	9.5	7.5	10.8***	6.5
News coverage	33.7	24.3	27.0	21.4	10.4	12.8*	10.0	8.4	12.0***	6.0*
Information about symptoms	31.4	20.8	22.0	19.3	10.8	12.6	11.9	8.3	11.6**	7.4
Government rules	30.3	20.2	22.2	18.8	10.2	11.4	10.2	8.9	11.0	8.6
Numbers or charts	27.6	18.5	20.6	17.9	8.9	11.8**	8.1	8.7	9.4	8.6
Religious sentiments	23.7	16.0	16.2	18.8	8.8	9.0	9.3	7.1	9.4*	6.0

 

 

Table 4b: Experiences with sharing COVID-19 content on social media platforms by political affiliation — Restricted to platform users. Note: For regression coefficients, * p < .05; ** p < .01; *** p < .001.
	All FB users	Facebook		All IG users	Instagram		All TW users	Twitter
		Dem	Rep		Dem	Rep		Dem	Rep
Sharing any type of content about COVID-19	53.9	58.0	54.1	41.5	44.6	41.9	41.9	45.7	40.1
Avoidance tips	34.0	38.1*	31.3	19.5	20.8	21.2	20.0	23.6	14.1*
Gratitude toward health care workers	33.0	35.4	31.0	22.0	24.9	20.7	22.0	22.8	20.9
Humor and jokes	31.5	32.4	30.2	21.4	23.2	20.2	17.1	20.2	15.8
News coverage	29.5	33.2	25.3*	20.2	22.1	21.2	19.1	22.5	14.7
Information about symptoms	25.3	27.0	22.8	21.0	21.8	25.1	19.0	21.7	18.1
Government rules	24.5	27.3	22.3	19.7	19.7	21.7	20.3	20.6	20.9
Numbers or charts	22.4	25.3	21.2	17.2	20.4	17.2	19.8	17.6	20.9
Religious sentiments	19.4	19.9	22.3	17.1	15.6	19.7	16.1	17.6	14.7

 

 

Table 5a: Logistic regression on sharing content on COVID-19 — Full sample. Note: For regression coefficients, * p < .05; ** p < .01; *** p < .001.
	Facebook		Instagram		Twitter
	b	SE	b	SE	b	SE
Political affiliation (base = Republican)
Democrat	0.02	0.1	-0.1	0.2	0.3	0.2
Independent	-0.2	0.1	-0.4	0.2	--0.4	0.2
Age	-0.01	0.004	-0.1***	0.01	-0.01***	0.01
Household income (logged)	-0.1	0.1	0.1	0.1	-0.1	0.1
Female	0.1	0.1	-0.5**	0.2	-0.8***	0.2
Metropolitan status (base = Urban)
Rural	0.2	0.2	-0.6*	0.3	-0.3	0.3
Suburban	-0.05	0.1	0.2	0.2	0.2	0.2
Education (base = High school or less)
Some college	-0.1	0.1	0.7***	0.2	1.1***	0.2
Bachelor’s degree or more	-0.1	0.1	0.3*	0.2	0.6***	0.2
Race and ethnicity (base = White)
Black	-0.1	0.2	0.8***	0.2	0.4	0.2
Hispanic	-0.03	0.2	0.6**	0.2	0.5*	0.2
Asian	-0.3	0.3	0.3	-0.2	0	0.3
Native American	0.7	0.4	0.1	0.6	0.5	0.5
Internet experiences
Autonomy of use	0.1**	0.05	0.1	0.1	0.1	0.1
Social media skills	0.3***	0.1	0.2**	0.1	0.2*	0.1
Constant	-0.8	0.7	-0.9	1	0.2	1
N	1,353		1,353		1,353
Log likelihood	-892.4		-549.9		-530.9
AIC	1,816.80		1,131.90		1,093.80

 

 

Table 5b: Logistic regression on sharing content on COVID-19 — Restricted to platform users. Note: For regression coefficients, * p < .05; ** p < .01; *** p < .001.
	Facebook		Instagram		Twitter
	b	SE	b	SE	b	SE
Political affiliation (base = Republican)
Democrat	0.1	0.2	-0.1	0.2	0.2	0.2
Independent	-0.2	0.2	-0.4	0.2	--0.3	0.2
Age	-0.01*	0.004	-0.04***	0.01	-0.04***	0.01
Household income (logged)	-0.04	0.1	-0.02	0.1	-0.1	0.1
Female	0.05	0.1	-0.6***	0.2	-0.5*	0.2
Metropolitan status (base = Urban)
Rural	0.2	0.2	-0.6	0.3	-0.2	0.3
Suburban	-0.1	0.1	0.1	0.2	0.1	0.2
Education (base = High school or less)
Some college	0.2	0.2	0.4*	0.2	0.7**	0.2
Bachelor’s degree or more	-0.1	0.2	0.6**	0.2	1.0***	0.2
Race and ethnicity (base = White)
Black	0.02	0.2	0.4	0.2	0.1	0.3
Hispanic	-0.04	0.2	0.4*	0.2	0.4	0.2
Asian	-0.5	0.3	0.2	0.3	-0.2	0.4
Native American	0.9	0.5	0.02	0.6	0.4	0.6
Internet experiences
Autonomy of use	0.1	0.1	0.1	0.1	0.02	0.1
Social media skills	0.2**	0.1	0.1	0.1	-0.01	0.1
Constant	-0.2	0.8	0.9	1.1	2.4*	1.2
N	1,116		699		596
Log likelihood	-744.6		-425.4		-367.1
AIC	1,521.20		882.8		766.2

 

 

COVID-19 knowledge and content sharing

To examine how political affiliation and content sharing about COVID-19 on social media relate to knowledge about the virus, we run OLS regression where we include political affiliation, content sharing, and sociodemographic characteristics as independent variables (Table 6). We find no connection between political affiliation and knowledge about the virus. We do find, however, that sharing content on Instagram as well as Twitter correlates negatively with COVID-19 knowledge.

 

Table 6: OLS regression on COVID-19 knowledge. Note: For regression coefficients, * p < .05; ** p < .01; *** p < .001.
	b	SE
Political affiliation (base = Republican)
Democrat	0.1	0.1
Independent	0.03	0.1
Sharing COVID-19 content on Facebook	-0.2	0.1
Sharing COVID-19 content on Instagram	-0.8***	0.2
Sharing COVID-19 content on Twitter	-1.3***	0.2
Age	0.03***	0.004
Household income (log)	0.2**	0.1
Female	0.4***	0.1
Metropolitan status (base = urban)
Rural	-0.1	0.2
Suburban	-0.1	0.1
Education (base = high school)
Some college	0.02	0.1
Bachelor’s degree or more	0.1	0.1
Race and ethnicity (base = White)
Black	-0.7***	0.2
Hispanic	-0.3*	0.2
Asian	0.3	0.2
Native American	-0.5	0.4
Internet experiences
Autonomy of use	0.3***	0.05
Social media skills	0.3***	0.1
Constant	4.8***	0.7
N	1,353
Log likelihood	-2,804.00
AIC	5,646.00

 

 

Discussion

This paper set out to investigate the relationship between political affiliation and the adoption of Facebook, Instagram, and Twitter (RQ1), and sharing content about COVID-19 on them (RQ2, RQ2a). Subsequently, it investigated the role of both political affiliation and social media use on knowledge about COVID-19 (RQ3). We find that political affiliation plays a role in social media adoption and usage, but the relationship looks different on the three platforms. Regarding site adoption, our bivariate analyses indicate that political affiliation matters for Instagram and Twitter use, where Democrats are more likely to have adopted those platforms than others. When holding other variables constant in the logistic regression, identifying as independent as compared to being a Republican is negatively related to Twitter adoption. Regarding sharing content about COVID-19, our bivariate results show that, across the full sample, Democrats are more likely to engage in such behavior on Instagram and Twitter. The regressions reveal no connection between political affiliation and sharing content on any platform, however. As for the restricted sample, we find no significant relationships between political affiliation and sharing content on any platform either in the bivariate tests or the regressions. When looking at the relationship between sharing behaviors and COVID-19 knowledge, we find that sharing content related to the pandemic on either Instagram or Twitter correlates negatively with knowledge about the virus. In other words, participation on these two platforms relates to lower COVID-19 knowledge. We find no such connection related to posting on Facebook nor political affiliation.

Overall, our results suggest that political affiliation can factor into the likelihood of adopting social media and sharing content about COVID-19. However, once controlling for sociodemographic characteristics and Internet experience, these factors seem to outweigh political affiliation. These findings add nuance to past research regarding social media adoption (Vogels, et al., 2021), where Democrats were more likely to adopt platforms, but the analyses did not control for background characteristics. Our findings highlight the importance of doing so.

When looking at the type of content shared, we find clear differences depending on political affiliation. On both Instagram and Twitter, Democrats were more likely to share various types of content than Republicans. The most popular type of content varied depending on the platform, indicating that the purpose those platforms serve might differ (Bayor, et al., 2018). Twitter in the past has been identified as a hub for sharing political news (Soroka and Krupnikov, 2021); when looking at COVID-19 content, avoidance tips as well as other types of content were shared much more often there. More information-based content was more popular on Twitter. The appeal of Instagram might lie in its image focus and role as a “beacon of positivity” [3]. This prominence of positivity as well as visual content on Instagram (Soroka and Krupnikov, 2021; Waterloo, et al., 2018) might explain the difference in the popularity of different content types (Table 4a). Specifically, Democrats were more likely to share gratitude toward healthcare workers as well as numbers or charts on the spread of COVID-19. The former arguably presents positive content and the latter lends itself well to the image-focused affordances of Instagram.

Knowing who does and does not partake in sharing content about the pandemic, we can draw inferences about the voices that are represented on social media. Namely, we observe differences in the type of content shared depending on political affiliation on Twitter and Instagram, which underlines that social media content is not representative of the opinions and beliefs of society at large (Blank and Lutz, 2017; Hargittai, 2015), challenging the use of such sources as signals of more general public opinion. Our findings also emphasize the importance of not treating platforms as one and the same (Hargittai, 2015), and preferably having data about social media behaviors disaggregated by platform and content when researching online participation (Matassi and Boczkowski, 2021).

When investigating knowledge about the virus, we find no connection to political affiliation but a negative relationship with content sharing on Instagram and Twitter. Sharing COVID-19 related content on these two platforms correlates with less knowledge of the virus. This is not encouraging in terms of the types of content that would have been circulating on these platforms about the virus. It also has potential implications for the benefits people can reap from being online and health behavior (Robinson, et al., 2020). People with less knowledge about the virus were less likely to follow health guidelines (Hargittai, 2022). Further, even Twitter users with high social media skills were less informed than people who had other information sources (Hargittai, 2022), indicating that both using certain social media as an information source and engaging with COVID-19 content can relate negatively to knowledge.

Like all studies, this one has limitations. Given the cross-sectional nature of our data, it is unclear if people with lower knowledge were more likely to engage with content or the other way around. Further our study did not measure whether the information that people shared was correct. While prior research finds significant relationships between more conservative individuals and engagement with misinformation (Freiling, et al., 2023; Guess, et al., 2019; Morosoli, et al., 2022), our findings show no connection between content sharing and political affiliation. Future research might expand on this study by examining the role of political affiliation in other forms of content creation and sharing. Such research might also develop more precise measures, for example, by differentiating between posting original content versus reposting content and amplifying the voices of others. Further, in this study, we collapsed different types of content about COVID-19 into one variable for the regression analyses. Potential differences across the platforms might exist for more specific content types, especially since different types of content had been shared more widely on some platforms than others.

We examined content about COVID-19 as a topic with a political charge (Hart, et al., 2020). Since the pandemic constitutes a worldwide health crisis affecting everyone to some extent, the relationship between political affiliation and online participation about a politicized topic could be partially explained by the omnipresence of the topic. Future studies might investigate the relationship between political affiliation and sharing social media content with similarly politically charged topics such as climate change or immigration.

 

Conclusion

This study draws on a national sample of U.S. adults to investigate the role of political affiliation in social media adoption and sharing content about COVID-19, and how these relate to COVID-19 knowledge. We find that while political affiliation does factor into both site adoption and actions on two of the three platforms, it does so differently across Instagram and Twitter. Particularly Democrats were more likely to be on the platform and share content on it, though not once controlling for background characteristics. While they were more likely to adopt and share content on Twitter and Instagram, this was not the case on Facebook. Past research regarding engagement with misinformation found that conservatives were more likely to engage with content (Freiling, et al., 2023; Guess, et al., 2019; Morosoli, et al., 2022), yet we find no such relationship in our more inclusive approach. We find no connection between political affiliation and knowledge about the virus, but we do find that sharing content on Instagram and Twitter correlates negatively to COVID-19 knowledge.

Our findings have important implications for who was able to benefit from social media in the early days of the pandemic, both potentially for protective measures (Gadarian, et al., 2021) and to feel less isolated (Hargittai, 2022; Nguyen, Gruber, et al., 2021). Further, the results indicate whose voices were heard on social media and thus potentially shaped public opinion. This study shows that while political affiliation might not factor into social media adoption and engagement with pandemic content in general, there are differences in the types of COVID-19-related content people engage with by political affiliation. Past scholarship has seldomly considered political affiliation in relation to these factors, potentially due to the lack of findings. Nonetheless, considering the importance of political affiliation for many outcomes, we argue it should be considered and reported on in future work, particularly in relation to politically charged topics. This study also shows the importance of considering individual platforms and different content types, as the findings differ along both of these axes.

 

About the authors

Jaelle Fuchs is a Ph.D. candidate and a research and teaching assistant at the Department of Communication and Media Research of the University of Zurich. She is a member of the Internet Use and Society Division. Her research interests include online participation and digital inequality.
E-mail: j [dot] fuchs [at] ikmz [dot] uzh [dot] ch

Floor Fiers is a Ph.D. candidate in the Media, Technology, and Society program at Northwestern University and a member of the Community Data Science Collective. She is interested in social inequality and user agency as it relates to digital technologies, particularly in the context of the gig economy and other digitally mediated work environments.
E-mail: floorfiers [at] u [dot] northwestern [dot] edu

Eszter Hargittai is a professor of communication and media research and holds the Chair in Internet Use and Society at the University of Zurich. Her research interests include digital inequality with a particular focus on how people’s Internet skills relate to what they do online.
E-mail: pubs [at] webuse [dot] org

 

Notes

1. For a detailed overview of all the answer options see Hargittai, 2022, p. 136.

2. Op. cit.

3. Soroka and Krupnikov, 2021, p. 40.

 

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Editorial history

Received 10 August 2023; accepted 5 November 2023.

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This paper is licensed under a CC BY-NC-ND 4.0.

Engaging with COVID-19 content on social media in the United States: Does political affiliation matter
by Jaelle Fuchs, Floor Fiers, and Eszter Hargittai.
First Monday, Volume 28, Number 11 - 6 November 2023
https://firstmonday.org/ojs/index.php/fm/article/download/13289/11367
doi: https://dx.doi.org/10.5210/fm.v28i11.13289