Who Ends Up in a Social Bubble? Exploring Young Users' Behavior on Social Media Platforms in Ukraine

Who Ends Up in a Social Bubble? Exploring Young Users’ Behavior on Social Media Platforms in Ukraine

Photo: unsplash.com / Prateek Katyal
18 June 2024
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This research examines why young social media users might become trapped in a “social bubble” by seeking information that supports only their existing beliefs. We use Qualitative Comparative Analysis to identify combinations of factors that either contribute to or prevent the formation of these bubbles. We find three combinations that tend to create social bubbles. All three include respondents who tend to conform to dominant opinions and rarely listen to  diverse viewpoints.

We have also identified one combination that leads to the opposite outcome, i.e. when young individuals reject the idea of being in a social bubble. Such persons avoid conforming to dominant opinions, frequently engage in debates, and regularly expose themselves to diverse perspectives, even if they use only a few social media platforms. These results suggest that educational institutions and parents should pay attention to shaping social media behavior of youth by teaching them to look for diverse viewpoints and critically evaluate them to form their own independent opinions. 

Social media platforms (such as Facebook, Twitter, LinkedIn, etc) play an important role in shaping public perceptions of events and policies by enabling users to share content, consume information, and interact with each other. Their impact is not always positive because users of social media can frequently be trapped in what is known as a social bubble. This bubble, also referred to as an echo chamber or filter bubble, describes a scenario where people primarily seek information, ideas, and perspectives that resonate with their preexisting beliefs and preferences. Social media platforms often use algorithms to personalize content feeds based on past interactions, such as likes, shares, and comments. As a result, users are likely to repeatedly encounter content that reinforces their existing viewpoints, creating a social bubble. 

Our study examines how young people’s choice of information on social media platforms can influence their opinion formation in Ukraine. The country is in a crucial stage of the war, and hence, comprehending the formation of social bubbles under the impact of social media can help avoid the undesirable manipulations of public opinion. The focus on young people is necessary since they are usually regarded as one of the most vulnerable groups in society, prone to external influence, and relatively unstable in maintaining their opinions.

What do recent studies reveal about social bubbles?

Social media shapes the formation of opinions through various channels. The most influential one is how individuals seek information on media platforms. Studies show that users typically choose the information that aligns with prevailing viewpoints or widely accepted beliefs while avoiding content that challenges those beliefs. This inclination is especially pronounced among young people, as the desire to conform is particularly strong during early adulthood years.

The personal characteristics of young individuals are expected to influence this tendency to conform. Those who are more resilient to social pressure are less likely to conform to dominant opinions within their social networks and more willing to express less popular viewpoints. This is because a person’s resilience fosters a practice of critical evaluation of information, leading to opinion formation based on reasoned analysis rather than social conformity. In addition to resilience, actively seeking diverse information and engaging in dialogue with others can further reduce the likelihood of trapping oneself in a social bubble.  

Features of social media, such as the size of networks, can also impact the formation of opinions. As the size of a network grows, users are more likely to encounter a wider range of perspectives, beliefs, and viewpoints because with more connections and interactions people have more opportunities to engage with individuals from diverse backgrounds, ideologies, and experiences. A greater diversity of opinions within the network can prompt individuals to incorporate new information and perspectives into their own belief systems, increasing the probability of breaking out of a social bubble. 

In larger networks, minority viewpoints or opinions that initially exist on the periphery of the network have a greater chance of gaining visibility through the process called information cascades. As more individuals adopt and share minority perspectives, they can challenge dominant beliefs and contribute to the diversification of opinions within the network, encouraging users to reevaluate their current stance regarding certain events. 

Which factors form social bubbles?

The above overview allows us to identify the following determinants of social bubble formation when using social media platforms: (1) perceived pressure to conform to dominant opinions, (2) frequency of exposure to diverse viewpoints, (3) frequency of debating, and (4) network size. These factors will be the main conditions addressed in our study. Our research question is: “Which combinations of the four conditions can explain trapping online social media users in a social bubble, and which combinations contribute to the opposite situation, i.e. when the users seek information deviating from their preexisting beliefs, values, and preferences?”

For our analysis, we collected data through an online survey in February 2024. The sampling strategy relied on voluntary participation, affording interested individuals the opportunity to contribute. We recognize that there may be a selection bias in our sample, given that participants may be more self-aware of how they seek information than non-participants. In total, 257 persons provided responses in this survey. Their age ranged from 16 to 31, with a mean age of 19.9 years. 65.8 percent of respondents were female and 34.2 percent were male. Half of the respondents (55.3%) were employed at the moment the survey was conducted. 

For this study, we use qualitative comparative analysis (QCA), which aims to identify all the combinations of conditions leading to a given outcome. More specifically, this method abandons the idea that each predictor has an autonomous influence on the dependent variable. Instead, it assumes that there are multiple combinations of predictors explaining the same outcome. Furthermore, the combinations for a positive outcome can be different from combinations that matter for a negative outcome. 

The outcome variable is “being in a social bubble on social media” (SOCBUB) measured by asking respondents whether they feel to be in a social bubble due to being primarily exposed to information, ideas, and perspectives that match their existing beliefs and preferences. A response of “yes” is coded as “1,” while “no” and “I am not sure” are coded as “0.” In total, 50.6% of respondents declared to be in a social bubble. We acknowledge certain subjectivity of the self-declared social bubble measure as individuals reflect on how much the information they encounter on social media platforms differs from their own beliefs. 

To facilitate the analysis, all predictors are converted into binary measures. The tendency to conform to the dominant opinion (CONFORM) is operationalized through the question, “Do you feel the pressure to align your opinion with dominant or popular ones even if you disagree with them?”. Responses are measured on a Likert scale from 1 (“Not at all”) to 5 (“Very often”). Following Emmenegger et al. (2014), responses of 1, 2, and 3 are coded as “0” (more out of the set), while responses of 4 and 5 are coded as “1” (more in the set), thus converting them into a binary measure. 

The frequency of debating (DEBAT) is measured by the question: “How often do you participate in debates on social media?” The responses “Never to avoid confrontations” and “Seldom” are coded as 0, and “Sometimes or Often” are coded as 1. Network size (NETSIZE) is determined by counting the number of social media platforms respondents reported using most frequently. This number ranges from 1 to 6 in our dataset. We calibrated these values into a binary measure, using a threshold of 3.1, defined with the findTh command. Values below the threshold are coded as “0,” while values above the threshold are coded as “1.” Exposure to diverse opinions (DIVEROP) is assessed by asking respondents how often they listen to or read opinions on social media that differ from their own. Responses “Rarely” and “Sometimes” are coded as “0,” while “Often” is coded as “1.”

What can be found about social bubbles in Ukraine?

We begin our analysis by assessing necessity. If something is always there every time respondents admit they are in a social bubble, it is called a necessary condition. We measure the necessity using two scores: consistency (InclN) and coverage (CovN). Consistency tells us how many people in a social bubble also have the condition (predictor) we are examining, compared to the total number of people in social bubbles. Coverage helps us understand how important a necessary condition is. It shows the proportion of people in social bubbles who also have the condition, out of all the cases where the condition is present. We only look at coverage if the consistency score is high enough, usually 0.900 or more as defined by the QCA protocol. For coverage, we want scores of 0.600 or higher.

Table 1 summarizes the consistency and coverage scores, suggesting that none of the chosen conditions meet the necessity standard when applying the typical consistency threshold of 0.900. Hence, none of these predictors are needed to determine whether or not individuals are part of a social bubble. Young people might find themselves in a social bubble even if any or all of these conditions are absent in their surroundings. 

Table 1. Results of the necessity analysis 

Conditions  consistency (InclN) coverage (CovN)
Tendency to conform (CONFORM) 0.585 0.633
Exposure to diverse opinions (DIVEROP) 0.338 0.379
Frequency of debating (DEBAT) 0.700 0.569
Network size (NETSIZE) 0.608 0.497

Note: For CONFORM, the consistency score should be interpreted as follows: Out of all observations in which respondents declared to be in a social bubble, only 58.5 percent declared to conform to the popular opinion, which is below the threshold of 0.900. Hence, the respondent can be in a social bubble even without being characterized by high conformity to the popular opinion. The CovN of 0.633 indicates that out of all the respondents who conform to the popular opinion, 63.3 percent declared to be in a social bubble. This suggests that the tendency to conform to the popular opinion is relatively common and captures a substantial proportion of the cases in our dataset. However, it is still not a necessary condition since the inclusion score is below the threshold of 0.900. The statistics for other conditions can be interpreted in a similar way. 

The next step is to conduct an analysis of sufficiency. Here, the primary goal is to identify the minimal combinations of conditions (predictors) that are sufficient to produce a given outcome. This process involves creating a truth table while applying a sufficiency inclusion score threshold of 0.800 or higher to choose sufficient conditions as defined by the QCA protocol. The results from the truth table are summarized in Table 2. Briefly, there are four combinations of conditions surpassing the threshold for significance. Three of them correspond to producing the outcome where individuals admit to being in a social bubble (SOCBUB = 1). Only one of them corresponds to the opposite outcome (SOCBUB = 0), where individuals deny being in a social bubble. Specifically, those who are not in a social bubble are usually characterized by rarely feeling the pressure to conform to the dominant opinion, frequently debating and listening to diverse opinions on social media, even if attending a low number of social media platforms. 

Table 2. Combinations of conditions for the sufficiency analysis

Conditions Outcome = 1 (In a Social Bubble) Outcome = 0

(Not in a Social Bubble) 

Combination 1 Combination 2 Combination 3
CONFORM 1 1 1 0
DIVEROP   0 0 0 1
DEBAT  0 1 1 1
NETSIZE  0 0 1 0
Inclusion score 0.800 0.857 0.833 0.836
PRI 0.801 0.856 0.832 0.835

Note: All other potential combinations failed to meet the inclusion threshold of 0.800, indicating that they encompass an insufficient number of cases to ensure high generalizability. The inclusion score shows the proportion of respondents trapped in a social bubble out of all cases where the relevant combination of predictors is present. For example, the inclusion score of 0.800 for Combination 1 indicates that out of all cases where this combination was present, 80.0 percent of respondents declared to be in a social bubble. PRI stands for Proportional Reduction in Inconsistency. It shows how consistently a combination of predictors leads to the same outcome and should be close to 1. For instance, the PRI of 0.801 for Combination 1 means that in 80.1% of the cases, this combination of predictors reliably leads to the outcome of being in a social bubble, demonstrating strong and predictable patterns in the data. 

There are three possible combinations pertaining to individuals trapped in a social bubble (SOCBUB  = 1). The first combination (Combination 1) represents the most negative scenario, where individuals are subjected to frequent pressure to conform, rarely expose themselves to diverse information, rarely participate in debates on social media, and use a limited number of social media platforms. This suggests that young users tend to be locked in a social bubble as long as they strive to conform to the popular opinion even if they disagree with it, do not seek diverse information, refuse to debate with other users, and have a limited number of social platforms from which they collect information. 

The second combination (Combination 2) includes individuals with high pressure to conform to the dominant opinion and rare exposure to diverse viewpoints while actively debating on social media, provided that the number of social media platforms used regularly is fewer than or equal to three. In other words, young people who strive to conform to popular opinions with limited diversity in viewpoints in their network end up in a social bubble as long as their network is  limited in size despite their tendency to actively debate on social media platforms. 

The third combination (Combination 3) includes young users often feeling pressure to conform to dominant opinions and rarely seeking diverse opinions even if they frequently participate in debates and regularly use a large number of different social media platforms. This means that young individuals with a pronounced tendency to conform and a limited diversity of viewpoints expressed in their network are still likely to be trapped in a social bubble even if they use a large number of media platforms to seek information and frequently exchange opinions with other users through debating. 

Conclusions

In summary, three combinations of the selected predictors explain why young people stay in a social bubble. Of the four conditions (or predictors) considered, two are always present in these combinations. First, the tendency to conform significantly influences individuals’ behavior on social media platforms. The increased pressure to fit in with popular opinion causes users to seek information and connections that reinforce this dominant viewpoint. This finding is in line with the previous studies showing that the tendency to conform and seek validation from like-minded others contributes to the formation of social bubbles. Secondly, infrequent exposure to diverse opinions prevents young people from challenging their existing views, thereby contributing to the formation or maintenance of social bubbles. This result is also in line with previous research, which concluded that exposing oneself to information limited in diversity can lead to the formation of echo chambers. 

Based on the above findings, we propose that parents and educational institutions of any level should play a more active role in promoting media literacy among young individuals. Firstly, young people should be encouraged to act as independent thinkers, critically analyzing all information collected on social media and forming their own opinions. The goal should be to cultivate a culture where every opinion is personalized and formed based on the information that is critically assessed and not on the desire to conform. 

Secondly, independent thinking should be integrated within the framework of informed thinking. This should include educating young people to seek diverse information and perspectives, and go beyond conventional approaches to discuss a given topic. Demonstrating that each question can have multiple answers fosters not only more democratic conduct but also requires young persons to engage in individual analysis when processing and comparing diverse opinions, thereby further enhancing their critical thinking abilities.

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