A cross-country examination of normative expectations of social media platforms

Abstract

Normative expectations of social media platforms, or what people believe is important for social media platforms to do, include actions such as “show reliable information” and “ensure that people feel safe.” This study examines whether normative expectations vary by country and platform. A survey of over 20,000 people across 20 countries allowed us to investigate beliefs about 14 normative expectations. Although there is a relationship between the country in which one resides and what one believes is important for social media to do, the effects are small, adding nuance to theories of both globalization and localization. Differences in people’s normative expectations based on the platform they use most are also small. Aggregating people’s normative expectations reveals by-country clusters that are most closely related to Hofstede’s concept of individualism. Platform clusters emerge based on the platform parent company and unique affordances.

Keywords

norms social media globalization platforms localization

Introduction

The extent to which media crossing national boundaries have a globalizing effect, homogenizing and minimizing cultural and national differences, has been a long-standing research topic (e.g. Kraidy, 2002). Scholars have found evidence that media from the West garnered more attention than offerings from other countries (e.g. Schiller, 1991). Others have identified that although US media were present, national and regional media also were important (e.g. Straubhaar, 1991), and local consumers of foreign media integrated global and local identities (e.g. Kraidy, 1999). Globalizing and localizing forces may play out on social media as well. Many social media platforms are designed to scale globally yet originate in the United States. Just as earlier debates about the globalizing influence of television and other cultural products hinged on whether Western content affected other countries’ norms (e.g. Yan and Bissell, 2014), contemporary concerns about the globalizing influence of the Internet (Bailard, 2014) and social media (Choudhury, 2018) also revolve around their potential effect on norms.

Norms for social media can form within and across countries as well as within specific platforms. Social media are not monolithic – Facebook is different from Twitter (now X), which is different from Pinterest. Platforms differ not only in their features – LinkedIn allows you to see your network connections, whereas Reddit allows you to upvote and downvote comments – but also in terms of who uses each platform and how people use it. Indeed, previous work demonstrates that different social media platforms facilitate different types of networks and communication patterns (Valenzuela et al., 2018). These differences may yield platform-specific norms.

In this study, we analyze variation in normative expectations about social media platforms, by which we mean people’s thoughts about what is important for social media to do. Social media platforms have received much flak for allowing mis/disinformation to circulate, increasing political polarization, and facilitating harassment, among other criticisms. Underlying these ills are normative expectations about the type of digital space that people want. Concern about misinformation, for instance, corresponds with a belief that it is important for social media platforms to provide reliable information. Concern about harassment corresponds with a belief that it is important for social media platforms to ensure that people feel safe. We evaluate normative expectations like these and how public perceptions vary by country and by social media platform. We ask how the countries in which people reside and the platforms that they use most correlate with users’ normative expectations.

Using a survey of over 20,000 people across 20 countries, we investigate the influence of country and platform on whether people find 14 normative expectations important (see Table 1). We find that country and platform correlate with normative expectations, although both have only small effects in predicting normative expectations. These results complexify both globalization and localization theories, as well as the idea that platforms yield distinct norms. Aggregate data allow us to look at how beliefs about what is important for social media platforms to do vary across countries and platforms. Clusters of countries emerge that map most closely onto Hofstede’s (2011) dimension of individualism, or the degree of group orientation in a culture. Platforms cluster based on their corporate owner and unique affordances.

Table 1.

Percentage endorsing normative expectations.

Normative expectation	% saying “Important”
Keep people’s information secure	43.9%
Show reliable information	42.4%
Give everyone a chance to share their thoughts, regardless of their background	41.0%
Give people the chance to feel connected to other people and groups	39.9%
Help people become informed citizens	35.7%
Provide information about issues that concern people	34.7%
Encourage people to treat others humanely	34.6%
Provide opportunities for different groups to interact	34.5%
Ensure that people feel safe	34.4%
Promote thoughtful conversation	30.1%
Strengthen people’s connections to their local area	30.0%
Give citizens the opportunity to get involved in addressing society’s problems	29.1%
Give the public access to people in power, such as those at companies and in governments	21.9%
Help communities recover after crisis	20.8%

Normative expectations and social media platforms

Norms are cultural products, such as values, customs, and traditions (Sherif, 1936). The public and practitioners alike have norms about media; scholars have explored journalists’ and media producers’ norms (e.g. Garnier et al., 2020; Hedman, 2015), as well as audience norms for journalism (Banjac and Hanusch, 2020; Eldridge and Steel, 2016), for instance. Research has also begun to explore norms related to social media. Some have taken a normative approach to explore social media content (e.g. Freelon, 2015), whereas others have looked at norms for people’s behaviors on social media (e.g. Waterloo et al., 2018). Here, we are concerned with normative expectations of social media platforms, or what the public thinks is important for social media platforms to do. Although conceptually related to injunctive norms, or norms about what is socially approved, we conceive of normative expectations as individual views about what is important, regardless of what one assumes others believe. Past polling demonstrates that people have normative expectations of social platforms. Most Americans do not trust that social media sites will protect their data, for instance (Olmstead and Smith, 2017). In addition, a majority in the United States think that social media companies censor political views, social media has too much control over the news that they see, and one-sided and inaccurate news is a problem on social media (Shearer and Grieco, 2019; Smith, 2018). These studies focus on a narrow range of possible norms, and scholars have not, as of yet, analyzed whether norms vary by country or by platform.

Norms are crucial to understand because they affect organizations’ legitimacy; whether organizations live up to normative expectations ultimately affects their success and failure (Panwar et al., 2014; Sethi, 1975). Some have stopped using social media platforms when the platforms failed to live up to beliefs about what they should do (Brown, 2020). The Cambridge Analytica scandal, for example, violated users’ normative expectations about the importance of information security and cost Facebook both users and revenue (Perrin, 2018; Statt, 2018). Changes in fact-checking on Twitter (X) and Facebook violated some people’s expectations about the importance of providing reliable information, whereas others saw the moves as affirming norms about the importance of giving everyone a chance to share their thoughts (Duffy, 2025). As these examples demonstrate, people have normative expectations about social media platforms. Past research has identified 14 normative expectations related to social media platforms (Masullo et al., 2022), as outlined in Table 1. We ask people whether they think that each is important for their most-used social media platform. In the following sections, we explore theories about whether normative expectations vary by country or by platform.

Individual variation in normative expectations about social media platforms

Normative expectations of social media platforms could fall toward one side or another of continua about the influence of country and platform. Normative expectations may be similar regardless of the country where people live (globalization), or they may differ by country (localization). They could also vary by platform (platform heterogeneity) or not (platform homogeneity). We explore these continua, each with two anchoring perspectives, in turn.

Globalization

Several conceptions of globalization purport that Western views are transmitted throughout the world via the media (see Kraidy, 2002; Schiller, 1991; Straubhaar, 1991). Since many global social media companies started in the United States, these companies could have globalizing influences. As Kirkpatrick (2010) noted about US-based Facebook, “Facebook is resolutely American, even if it may not always seem so to its international users . . . Facebook carries those values [of the liberties of American discourse] around the world” (pp. 278–279). Choudhury (2018) writes that Facebook’s global appeal can be attributed to a “McDonaldization” of digital interactions. McDonaldization refers to the adoption of US-based fast-food restaurant elements, such as emphasizing efficiency and uniformity of experience (Ritzer, 1993; see also Jurgenson, 2010).

Indeed, social media platforms are often similar across countries. To maximize profit, social media companies build at scale and, as much as possible, avoid alterations. Although platforms make nods to local customs and sometimes bow to the demands of national governments to block content, ban users, and break encryption (The Economist, 2021), key aspects of social media’s content and functionality, such as the basic algorithms, are constant from country to country (Lada et al., 2021). There are opportunities to customize one’s digital experience based on, for example, who one friends or follows. Yet as Jurgenson (2010) acknowledges, “In structuring the processes of online social networking and the digital presentation of the self, we might argue that, on Facebook, socializing itself has been McDonaldized” (p. 168). Social media may create a sameness that is steeped in values, in many cases American values, or, as Choudhury (2018) hints, capitalist values. The idea that people have individual accounts and make selections of others to friend or follow – characteristics of most popular social media platforms developed by US-based companies – could be seen as valuing individualism, which is more characteristic of Western cultures (Triandis, 2001). To the extent that norms about social media platforms have globalized, people’s thoughts about what is important for social platforms to do may tend to be similar across countries.

Localization

Yet there are reasons to believe that the country in which one resides will influence normative expectations about social media platforms. Norms vary by culture in ways that could have implications for what we think of digital media (e.g. Cho et al., 2009). For example, Asian cultures often emphasize familial duties and collectivism (Trepte et al., 2017), so we might expect that people from Asian countries will be more likely to say that it is important for social media platforms to “give people the chance to feel connected to other people and groups.” With an emphasis on individualism (Triandis, 2001), people in Western countries might instead prioritize “giving everyone a chance to share their thoughts, regardless of their background.” Supporting the idea, research finds that those from more Western individualistic countries placed less emphasis on social gratification compared to those from more collectivist societies (Trepte et al., 2017). With respect to social media, users living in individualistic societies express less trust in their online friends compared to those in collectivist societies (e.g. China; Chu and Choi, 2010).

There are also variations in how social media platforms are used across countries (Nielsen and Schrøder, 2014) and geographical regions (Newman et al., 2024). One clear example comes from country-level variations in the use of Twitter, which some cultures saw as a way to create a personal “brand,” whereas others saw it as a way of subverting repressive governments (Postigo, 2016). As the uses of social media appear to vary by country, we might also expect people’s normative expectations to vary along this dimension, just as individuals’ beliefs about privacy (Neubaum et al., 2023) and standards of appropriate behavior on social media (Gupta et al., 2018) differ by country.

Another reason to expect cross-country variation is that people’s social networks are significantly predicted by proximity and cultural homophily (Barnett and Benefield, 2017). Social media users identify with specific regional identities through the content of their posts (Boatwright and Pyle, 2023) and the accounts they follow (Lu and Chu, 2012). Although social media can facilitate international contact, data suggest that people tend to interact with those who are physically closer to them (Laniado et al., 2018) – and therefore, with those who tend to share cultural values. If people primarily interact with others sharing their national culture, then members of each national culture can bring their own unique expectations that are then reinforced by interacting with fellow national residents.

To understand under what circumstances the data support theories of globalization or localization, we analyze the extent to which one’s country of residence predicts what they think is important for social media platforms to do. A globalization perspective would anticipate few between-country differences, whereas a localization perspective would predict greater variation.

RQ1: To what extent do normative expectations of social media platforms vary by country?

Normative expectations may vary not only by country but also by the platform that people use. We investigate the extent to which people have similar norms across social media platforms (platform homogeneity) or different norms based on the platform (platform heterogeneity).

Platform homogeneity

By definition, social media companies aim to connect people and allow them to socialize. Many platforms have similar architectures, such as news feeds, private messaging, and the opportunity for live broadcasting (Pardes, 2020). Social media’s similar functionality across platforms may inspire similar normative expectations. For example, social media emphasize social interaction and self-presentation, including the disclosure of personal information (boyd and Ellison, 2008). We might therefore expect that individuals will consider “keep people’s information secure” to be of relatively equal importance across platforms. Furthermore, all social media platforms face similar issues, such as hate speech and misinformation. We may therefore surmise that people will have similar beliefs about the importance of “encouraging the humanization of others” and “showing reliable information” regardless of platform.

Platform heterogeneity

Yet there may be differences in normative expectations depending on the platform. Social media platforms have unique purposes. LinkedIn describes itself as a place designed to “connect the world’s professionals to make them more productive and successful” (LinkedIn, 2026), whereas Facebook’s parent company, Meta, describes its mission as “building the future of human connection” (Meta, 2026). Previous research has found that behaviors differ platform to platform and that each platform presents a different social context (e.g. Lim et al., 2015; Qiu et al., 2013; Valenzuela et al., 2018; Waterloo et al., 2018). Social media users have distinct perceptions of what is considered appropriate behavior on different platforms, for example, with regard to relationship management (Tandoc et al., 2019) and self-presentation (Zhong et al., 2017). Users consider norms about appropriate conduct when deciding when and how to use one platform over another (Taber et al., 2023; Tandoc et al., 2019). For instance, Waterloo et al. (2018) found that expressing both positive and negative emotions was considered more appropriate on WhatsApp than on other platforms. Qiu et al. (2013) discovered that users perform more benevolent in-group sharing on the Chinese platform Renren than on the American platform Facebook. The authors suggest that users’ behaviors may differ by platform due to “cultural mindset switching” (p. 117). We expand on this theorizing to suggest that users may have different ideas about what is important for a social platform to do depending on which platform is being considered. The continuum between heterogeneity and homogeneity perspectives on what people will find important for social platforms led us to pose the following question:

RQ2: To what extent do normative expectations of social media platforms vary by platform?

Aggregate variation in normative expectations about social media platforms

In addition to looking at individual-level relationships between the country where one resides and the social media platform they use, we also look at aggregate patterns of normative expectations by country and platform and analyze what clusters emerge. This represents a distinct analysis; differences by country and platform cannot be inferred from individual-level data; doing so would mean committing the atomistic fallacy, wherein results at the group level are inferred from results at the individual level (Ruppel, 2017).

We compare our by-country clustering to two well-known categorization schemes. First, and most well-known in communication, Hallin and Mancini (2004) identified three media system models characterized by the structure of the media, the role of journalism, and press–politics relations. Building on this seminal work, Brüggemann et al. (2014) identified four media systems based on quantitative indicators of the dimensions proposed by Hallin and Mancini. More recently, Humprecht et al. (2022) used cluster analysis to advance the work of Hallin and Mancini by incorporating digital media indicators. We examine the extent to which the country-by-country clusters we identify map onto these categorizations.

Second, we evaluate the extent to which our by-country clustering is related to Hofstede’s (2011) six cultural dimensions: power distance (acceptance of inequality), individualism (degree of group orientation), uncertainty avoidance (comfort with ambiguity), motivation toward achievement and success (MAS, also sometimes called masculinity), long-term orientation (respect for tradition vs future focus), and indulgence (life enjoyment). At the individual level, studies have found relationships between these dimensions and how people orient toward technology (Cruz-Cárdenas et al., 2019; Srite et al., 2008). Our interest at the country level follows Hofstede’s (2001) categorization of nations. We ask the extent to which our by-country clusters correlate with country-level measures of Hofstede’s cultural dimensions.

In addition to looking at clusters of countries, we also aggregate the data by platform and examine clusters of platforms. Less research has been done looking at how people’s experiences on social media cluster, but the study of affordances comes closest (Treem and Leonardi, 2012). Different affordances across social media may lead people to hold distinct normative expectations (e.g. Bucher and Helmond, 2018). Platforms offering more anonymity (e.g. Reddit), for instance, may yield a different set of norms than those asking for real names to be used (e.g. Facebook, LinkedIn). We ask:

RQ3: How do (a) countries cluster based on their residents’ normative expectations and (b) platforms cluster based on their users’ normative expectations of social media platforms?

Method

The Institutional Review Board (IRB)-approved survey of 22,112 adults across 20 countries was conducted by YouGov, a firm that maintains an opt-in online panel and selects panelists matching the distribution of age, gender, and region of the adult population of each country. YouGov had a native linguist translate the survey into a common language for each country, followed by a review by a proofreader and a final review by a project manager. Several translations were later reviewed by native speakers on our team. The survey was fielded between November 27 and December 17, 2019. Selecting which countries to include was an iterative process, governed in part by practical matters such as budget and availability, as well as a desire to have a diverse set of countries and a range of both emerging and long-standing digital markets. We examined publicly available data about social media use and solicited advice from experts at Global Voices, Google, the Omidyar Network, the Reuters Journalism Institute, and faculty at universities in the United States and Chile. Compiling the feedback, we selected 20 countries, including Argentina, Australia, Brazil, Canada, France, Germany, Ireland, Italy, Japan, Malaysia, Mexico, Norway, Poland, Romania, Singapore, South Africa, South Korea, Sweden, the United Kingdom, and the United States.

Participants

The sample size for each country was just over 1,000, except for Germany, where we obtained data for just over 2,000 participants. Across the countries, 51.6% of participants were female. Participants were asked about their educational background in a manner specific to their country of origin, and the responses were collapsed into a measure of low (10.0%), medium (38.9%), and high (51.2%) levels of education. Participant age ranges were 18–24 (12.1%), 25–34 (18.6%), 35–44 (18.4%), 45–54 (17.0%), and 55 + (33.8%). Data were weighted to match available demographic markers in each country.

Measures

Overall platform use

Participants were asked to indicate which of seven social media platforms (Facebook, Instagram, LinkedIn, Pinterest, Reddit, Twitter, or YouTube) they used in a typical week. We selected these platforms for several reasons. First, these platforms each had more than 250 million global users (Activate Consulting, 2019). Second, we focused on social media as opposed to closed-messaging apps (e.g. WhatsApp, Messenger). Third, we focused on established platforms that had each been around for at least 9 years prior to the study. Finally, as China was not one of the countries included in our study, we did not include social media outlets most popular in that country (e.g. Qzone). Although there is some debate as to whether YouTube should be considered a social media platform (e.g. Smith, 2024), we included it given that it allows users to comment and engage with content. Participants selecting more than one platform (n = 15,597) were asked to indicate which they used most often. If they did not use any (n = 1,766), they were removed from the analysis. In total, 10,565 used Facebook most often, 4,748 YouTube, 2,858 Instagram, 1,214 Twitter, 383 Pinterest, 359 LinkedIn, and 219 Reddit.

Normative expectations

In this study, participants were asked, “Which of the following do you think it is important for [most-used social media platform] to do?” We asked study participants about their most-used social platform for several reasons. First, as theorized, people may have different normative expectations of different social media platforms. This necessitates asking about a specific platform as opposed to platforms in general. Second, most people used only a few social media platforms (M = 2.49, SD = 1.53). People may not hold normative expectations about platforms they have not used, and if they do, the norms would be based on something other than direct experiential exposure. We thus opted to ask people only about their most-used platform. Although we could have asked people about their normative beliefs for every platform they used, we asked only about the platform they used most for reasons of respondent fatigue and to avoid variability in study length. The 14 norms were derived from literature reviews, in-depth interviews with experts in academia and industry, and focus groups across five countries (Masullo et al., 2020; Masullo et al., 2022; see Table 1). In total, 91.4% of respondents indicated that at least one of the norms was important (M = 4.71, SD = 3.82).

Results

To answer RQ1 and RQ2, we conducted mixed-effects logistic regression models with fixed effects for platform and random intercepts for country for each of the 14 normative expectations. The outcome variable was “1” when a respondent said that the action was important for a social media platform to do and “0” otherwise. Table 2 shows the variance decomposition for platform and country, as well as the range of predicted probabilities. For “share thoughts,” the platform explained 1% of the variance. We estimated the average marginal predicted probability of saying that it is important for the platform to “give everyone a chance to share their thoughts, regardless of their background” separately for each platform. We then subtracted the lowest average marginal predicted probability from the highest to provide the range of probabilities. Here, the lowest average marginal predicted probability was for Pinterest (0.30), and the highest was for Reddit (0.56), which yields a range of 0.26 for “share thoughts.” We replicated the same analysis for countries; for “share thoughts,” countries explained 5% of the variance, and the range of predicted probabilities was 0.35. The results are similar for all of the normative expectations. Answering RQ1 and RQ2, normative expectations vary both by platform and country at the individual level. The effects, however, are notably small.¹

Table 2.

Predicting normative expectations by country and platform.

	Variance explained		Range of predicted probabilities (max–min)
	Platform	Country	Platform	Country
Share thoughts	.01	.05	.26	.35
Treat humanely	.01	.06	.22	.36
Thoughtful conv	.01	.06	.33	.38
Access power	.01	.06	.23	.31
Feel connected	.01	.06	.16	.38
Groups interact	.01	.05	.24	.34
Recover crisis	.02	.07	.15	.28
Connect local	.02	.05	.18	.31
Concerning issues	.01	.06	.18	.33
Reliable info	.01	.04	.09	.31
Informed citizens	.02	.05	.29	.34
Get involved	.01	.05	.24	.32
Feel safe	.02	.08	.19	.42
Info secure	.01	.07	.18	.45

Note: Results are based on a mixed-effects logistic regression model with fixed effects for platform and random intercepts for country. Platform variance is the marginal R², which captures variance explained by platform alone; country variance is the difference between the conditional R² and marginal R², which captures the additional variance uniquely explained by country. The range of predicted probabilities is the difference between the maximum and minimum predicted probability across platforms (or countries), averaged over the other factor.

We then turned to evaluating aggregate patterns. To understand variation by country, we computed the percentage of people within each country saying that each norm was important (see heat maps containing descriptive data in Figures S1 and S2 in the Supplemental Appendix).

To evaluate RQ3, the data were clustered using hierarchical agglomerative clustering (Murtagh and Contreras, 2012) based on cosine distance. We plotted a dendrogram, where the y-axis is the cosine distance between vectors of each country’s normative expectations (Figure 1). The cosine distance theoretically runs from 0, which would indicate that the two countries are identical in their normative expectations, to 2, which would indicate that the countries’ normative expectations are the opposite of each other. Although there are techniques to determine an empirically driven ideal number of clusters, there is no standard cutpoint and approaches can contradict (Charrad et al., 2014). Rather than identify a set number of clusters, we examine the dendrogram to see how clusters emerge as we go from countries that are most similar in their normative expectations (lowest y-axis values for the cluster) to those that are more different (higher y-axis values for the cluster). In Figure 1, y-axis varies from 0 to 0.07, demonstrating considerable similarity across countries. Nonetheless, clustering is apparent. The first countries to cluster together, displayed in the middle of the dendrogram, are predominantly English-speaking countries: Australia, Canada, the United States, Ireland, and the United Kingdom. Those clustering last, indicating more distinct normative expectations, are Poland and South Korea. We include radar charts illustrating the descriptive patterns by cluster in the supplemental materials (Figures S3 and S4).

Figure 1.

Dendrogram of aggregate normative expectations by country.

As part of RQ3a, we compared our by-country clusters to two established country-by-country categorizations. First, we compared our categorization to that of Hallin and Mancini (2004) and other scholars building upon their work (Brüggemann et al., 2014; Humprecht et al., 2022). The clustering of English-speaking countries is reflected in Hallin and Mancini’s (2004) work (labeled “Liberal”). Sweden and Germany also clustered together in several categorizations (Hallin and Mancini, 2004; Humprecht et al., 2022), although in all of those, Norway appeared in the same cluster, which does not happen until later in our dendrogram. Our categorization has little in common with Brüggemann et al. (2014), other than both projects finding similarities between the United States and Ireland. What we take from this comparison is that normative expectations about social media platforms are distinct from these prior cross-country conceptualizations based on media systems.

There is more overlap between our clustering and some of Hofstede’s (2011) cultural dimension categories, although none align perfectly. We were able to compare, country-by-country, the percentage of residents saying that each normative expectation was important and the Hofstede values using publicly available data. We assessed the overlap using data provided by The Culture Factor Group (2025), which compiles Hofstede values by country based on academic publications (e.g. Minkov and Kaasa, 2022) and its own research (see the Supplemental Appendix for validation tests of these data). Of the six Hofstede values, there was the strongest correspondence between our social media norms and individualism (see Table S3 in the Supplemental Appendix), where greater individualism in a country corresponds with a smaller percentage of a country’s residents rating each normative expectation as important. The country cluster containing Brazil, Malaysia, Singapore, and South Africa corresponds to lower individualism scores (from 23 to 43 on a measure from 0 to 100), whereas the country cluster containing Norway, the United Kingdom, Ireland, the United States, Australia, Canada, Germany, and Sweden tended to have higher individualism scores (from 58 to 87). There are some mismatches, however; for example, Ireland and the United States have lower individualism ratings (58 and 60) compared to others in the cluster (72 to 87). We computed Spearman’s rho statistics comparing the percentage in each country saying each norm was important with the Hofstede value. For individualism, nine of the 14 Spearman’s rho statistics were significant (average Spearman’s rho for the relationship between each of our 14 norms and the Hofstede individualism score = −0.57, range = −0.81 to −0.28; eight remain significant after adjusting for multiple hypotheses).

Eight of the 14 Spearman’s rho statistics were significant for long-term orientation (five after adjusting for multiple hypotheses); however, the magnitude of the relationship was weaker in most cases than it was for individualism (average Spearman’s rho for the relationship between our 14 normative expectations and long-term orientation = −0.48, range = −0.75 to −0.28). For the other Hofstede value categories, at most two of the 14 categories were significant (average Spearman’s rho statistics for power = 0.25, motivation toward achievement and success [MAC] = 0.04, uncertainty avoidance = −0.13, and indulgence = 0.26). Overall, and answering RQ3a, the clustering of countries is most closely connected to Hofstede’s individualism dimension.

To answer RQ3b, we again created a dendrogram (see Figure 2). Here, the y-axis varies between 0.00 and 0.03, indicating minimal distance among the platforms in terms of normative expectations. Meta platforms, Facebook and Instagram, cluster first, followed by Twitter and YouTube. Clustering last are Reddit, LinkedIn, and Pinterest.

Figure 2.

Dendrogram of aggregate normative expectations by platform.

Discussion

This study offers a cross-country examination of normative expectations of social media platforms, drawing from a survey of over 20,000 people across 20 countries. The results offer insight into how people conceive of what is important for these platforms to do and to variation by country and platform, both at the individual and aggregate levels of analysis.

At the individual level, we asked how much normative expectations of social media platforms are consistent with a globalization perspective (Choudhury, 2018; Kirkpatrick, 2010), where people agree on the importance of various norms regardless of where they live, or a localization perspective (Gupta et al., 2018; Neubaum et al., 2023; Postigo, 2016), with residents of various countries forming distinct normative expectations. Based on the data here, one might be tempted to conclude that localization is occurring since the country in which one resides significantly predicted normative expectations. Yet this conclusion would be a misreading of the data. Although statistically significant, the magnitude of the country-by-country effects was notably small, detectable only because of our large sample size.

Given the weak relationships, one might instead be tempted to conclude that there is evidence of globalization in normative expectations of social media platforms. This would also be problematic, however, because a globalization perspective means that people hold similar normative expectations regardless of where they live. Yet there is considerable heterogeneity in what people believed, both within and across countries. Based on our analysis, we conclude that the relationship between country of residence and normative expectations strongly supports neither a globalization nor a localization perspective.

Based on these patterns, additional work could be undertaken to identify when and how normative expectations of social media globalize or localize. Perhaps after well-publicized violations of norms, expectations change in similar ways across countries. Data security breaches (e.g. the Cambridge Analytica scandal; Brown, 2020), for example, could increase the importance people place on keeping data secure. It is also possible that local policies, such as Australia’s recent legislation to ban those under 16 from accessing social media, could stem from and contribute to localized normative expectations.

At the individual level, we explored whether normative expectations varied depending on the platform that people used most often. We introduced two ends of a continuum: a homogeneity theory (e.g. Pardes, 2020), where people hold similar normative expectations regardless of which social media platform they use most, and a heterogeneity theory (e.g. Valenzuela et al., 2018), where the social media platform one uses most predicted which normative expectations are endorsed. The findings show that the platform one used most often significantly predicted what people believed was important for social media platforms to do. Again, the effects were notably small, and there was considerable variation both within and across platform use in normative expectations. We thus find little support for either perspective.

Future work could consider whether people’s unique uses of and experiences with the availability of different social media features (i.e. affordances; Treem and Leonardi, 2012) affect their endorsement of various normative expectations. Those using LinkedIn to connect with those in power, for example, may be particularly likely to see this normative expectation as important.

Overall, there is much to be explored about what predicts people’s normative expectations about social media platforms. For example, Postigo’s (2016) research found both country-level (i.e. regime type) and individual-level (i.e. personal interests) effects on the perceived affordances of YouTube. Similarly, previous research finds that trust in specific social media platforms, such as Facebook or YouTube, varies across ethnic groups (Zhang et al., 2024). Future research could explore more individual- and country-level variables.

Importantly, our results are confined to a particular point in time and a particular set of platforms. Although we suspect that some patterns will persist, others may change over time. For instance, “keep people’s information secure” may be a normative expectation consistently shared by a significant number of Internet users, given high levels of mistrust in social media companies using their data responsibly (McClain et al., 2023). “Show reliable information” may also be a prevalent normative expectation, given individuals’ use of social media as a source of information during times like COVID-19 (Van Aelst et al., 2021). On the other hand, we may expect changes in norms such as giving access to people in power given recent declines in trust in institutions (Valgarðsson et al., 2025), albeit with country-level variations (Gubbala and Austin, 2024). Similar patterns of normative expectations may also be found in platforms not considered in our survey. For example, the patterns we had observed for YouTube may be similar to those for TikTok, as TikTok’s video creation shares some common features with YouTube. Future research should consider variations in normative expectations at different points in time and across other platforms.

We investigated how normative expectations aggregated by country clustered together. Although we identified similarities across countries, differences did emerge. Importantly, normative expectations by country were most closely related to Hofstede’s cultural dimension of individualism. These correspondences – not causal relationships – indicate that in countries with a lower sense of individualism, more residents endorsed many of the normative expectations for social media platforms. This is particularly interesting given that social media originated in the United States, a country known for higher levels of individualism (Triandis, 2001). It is possible that agreement on norms – whether for social media platforms or any other topic – is higher in cultures less focused on individualism. Alternatively, there may be something unique about social media, such as its focus on connection and communication, that inspires more agreement in more collectivist cultures. This also may be because many of the normative expectations we analyze are collective in nature (e.g. a sense of belonging). If we instead had looked at normative expectations that were more individualistic – for example, “it is important for social media platforms to let people promote their individual brands” – perhaps the results would be reversed. Future research can examine these possibilities.

The aggregate clustering of normative expectations by platform also provides insight. The fact that users of social media platforms owned by Meta (i.e. Facebook and Instagram) share similar normative expectations coincides with previous research demonstrating an effect of ownership on media coverage (Lee et al., 2020). It is also possible that similar people use Meta platforms and that users differ for the other social media platforms we evaluated. The similarities between Twitter and YouTube may be explained by the findings of previous research, which found significant overlap between the two platforms for convenience, connection to daily life, and making social connections (Cha and Kweon, 2021). The divergent patterns for Reddit, LinkedIn, and Pinterest also make sense given that these platforms have different audiences and somewhat different purposes from the others.

There are several limitations to this work. Importantly, this is a cross-sectional analysis, and we cannot determine whether the relationship between country or platform and normative expectations is becoming weaker or stronger over time. A longitudinal analysis would be useful in sorting through the different theoretical accounts; for example, a strengthening effect of country over time could provide some support for localization.

There are also limitations to the survey questions used. First, although we undertook extensive efforts to develop the list of normative expectations, the list does not represent all possibilities. Second, we asked only whether people thought each action was important or not. It would have been ideal to have a measure with greater variation (e.g. not at all important to very important), but limitations in the survey length prevented this depth. The conclusions replicate, however, if we use a summed measure of normative expectations endorsed (see Supplemental Appendix). Future research would be well served to iterate on best practices for asking about normative expectations. Third, country-by-country differences could be due to different translations. Although multiple checks were put in place and we conducted additional analysis showing that leaving out different items or countries makes little difference in the overall conclusions (Supplemental Appendix), we cannot rule out the possibility that the translation process introduced some variation. Finally, we analyzed normative expectations by asking people about their most-used platform, as opposed to asking the same person about multiple platforms. This feature of our study is difficult to avoid because few people use all platforms. This approach ensures that participants only provide normative expectations for platforms with which they are familiar. A potential limitation of this approach, however, is that we obtained normative expectations for each platform only from those who use the platform often, rather than from a wider distribution of platform users. Whether the same patterns would result from those using other platforms more often cannot be ascertained. This feature of the study also means that variation in normative expectations by platform could be due to the technical and cultural features of a platform, differences in who uses different platforms, or some combination of these factors. Given the minimal differences we find across platforms, however, this seems unlikely to invalidate our conclusions.

Despite these limitations, this study offers a new perspective on understanding people’s thoughts about social media platforms. It is critical to understand norms because they can influence people’s behaviors by, for example, leading people to migrate to new platforms when existing platforms fail to meet expectations. Platforms must think through how to prioritize various norms, not only because there can be trade-offs (e.g. ensuring that people feel safe may run afoul of giving everyone a chance to share their thoughts) but also, as this research shows, because people prioritize these norms differently. This study may offer some solace to those concerned about the possible globalizing effects of social media (e.g. Choudhury, 2018). The country in which one resides explained little variance in people’s normative expectations, suggesting that individual variation in normative expectations remains to be explained. Further, the link between the importance ascribed to various normative expectations by each country’s residents corresponded with lower, as opposed to higher, levels of individualism in the country.

Supplemental Material

sj-docx-1-nms-10.1177_14614448261460024 – Supplemental material for A cross-country examination of normative expectations of social media platforms

Supplemental material, sj-docx-1-nms-10.1177_14614448261460024 for A cross-country examination of normative expectations of social media platforms by Natalie Jomini Stroud, Tamar Wilner, Moo Sun Kim and Ashwin Rajadesingan in New Media & Society

Footnotes

Acknowledgements

The team is grateful to Eli Pariser, current co-director of New_ Public, who partnered on the development of the survey. We thank the Center for Media Engagement for their feedback on drafts of this study.

ORCID iDs

Natalie Jomini Stroud

Tamar Wilner

Ashwin Rajadesingan

Ethical considerations

The University of Texas at Austin Institutional Review Board approved this study (2019020039).

Consent to participate

Respondents were informed of the study details prior to beginning the study; written consent was not required.

Author contributions

NJS and TW developed the survey and conceived of the study design. NJS and AR conducted the analysis. All authors were involved in drafting the article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Omidyar Network and the John S. And James L. Knight Foundation.

Declaration of conflicting interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: We note that Natalie Jomini Stroud was involved in the 2020 Facebook and Instagram Election Project. She previously has received funding from Facebook (now Meta). We also disclose that Ashwin Rajadesingan previously received a Meta Research PhD fellowship and completed an internship at Meta.

Data availability statement

Data will be made available upon reasonable request to the corresponding author.

Supplemental material

Supplemental material for this article is available online.

Notes

Author biographies

Natalie Jomini Stroud (Ph.D., University of Pennsylvania) holds the E. M. “Ted” Dealey Professorship in the Business of Journalism, is a professor in the Department of Communication Studies, is appointed to the School of Journalism and Media, and serves as the director of the Center for Media Engagement at the Moody College of Communication at The University of Texas at Austin. She also helped to co-found New_ Public, an organization exploring how to create digital public spaces where people can thrive and connect.

Tamar Wilner (Ph.D., University of Texas at Austin) is an assistant professor in the William Allen White School of Journalism and Mass Communications at the University of Kansas. Her research interests include news literacy, misinformation, and the gap between journalism research and practice.

Moo Sun Kim (M.A., Sungkyunkwan University) is a doctoral student in the Department of Communication Studies and worked as a graduate research assistant at the Center for Media Engagement at The University of Texas at Austin. He is interested in understanding the relationship between online incivility and acts of political participation.

Ashwin Rajadesingan (Ph.D., University of Michigan) is an assistant professor at the Department of Communication Studies and a senior faculty associate at the Center for Media Engagement at The University of Texas at Austin. He is a computational social scientist interested in how online spaces can foster spirited, engaging political discussions and contribute to building a vibrant deliberative democracy.

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