Social media behavior during uprisings: selective sharing and avoidance in the China (Hong Kong), Iran, Iraq, and Lebanon protests

1.1 Background on the protests

1.2 Selective sharing on social media

Civic engagement and political participation have been conceptualized in various ways (Bakker and de Vreese 2011; Gil de Zúñiga and Valenzuela 2011; Gil de Zúñiga et al. 2012). In the online world, the common denominator between the different definitions is citizens engaging in some sort of civic and political behavior, such as sharing political information and participating in online discussions about politics or being proactive in their communities (Gil de Zúñiga et al. 2012). Advancements in digital technologies have facilitated information sharing by making it easy to post content on digital networks, especially on social media. The latter offer individuals interactive platforms for political communication, encouraging them to pursue agency and influence through various activities, among which are mobilization and circulation of information (Kahne et al. 2014). The wide reach of social media puts individuals in touch with others from across the globe, allowing them to network with people they do not know. Ideologically diverse discussion networks have been linked to politically active individuals who seem to benefit from the flow of opinions to make more-informed political judgments (Nir 2011). Large networks have been deemed important for increasing people’s exposure to diverse opinions, prompting individuals to participate in online discussions, which pave the way for civic involvement (Gil de Zúñiga and Valenzuela 2011). The significance of political participation in diverse networks is perhaps most obvious in relation to political deliberation (Garrett 2009). Exposure to diverse opinions can dilute echo chambers (Min and Wohn 2018) that are formed when like-minded people coalesce around the extremities, leading to opinion polarization (Sunstein 1999). Consequently, online bubbles pose a threat to political deliberation, as they increase both people’s exposure to attitude-supporting information and the frequency of occurrence of such content within these circles (Aruguete and Calvo 2018). With polarization occurring due to partisan selection bias in online filter bubbles (Lu and Lee 2018; Stroud 2010), the likelihood of sharing attitude-consistent information becomes even higher in such digital circles (Liang 2018).

Social media news use is perhaps most pronounced during protests, particularly due to the platforms’ technological affordances that promote participation and social interaction (Starbird and Palen 2012; Valenzuela et al. 2012). Like all types of external events where rival ideologies clash and social identities are threatened (Slater 2015), protests change people’s behavior, diminishing their tolerance of anti-attitudinal opinions (Kozman and Melki 2022), and prompting them to coalesce around like-minded sources (Slater 2015). Protests also change an otherwise casual communication environment into one that requires problem solving as opposed to mere decision making, a concept that Kim and colleagues brought forward in their theorization of information behaviors during problematic situations (see Kim et al. 2010; Kim and Grunig 2011, 2021). In their communicative action model, which forms an integral part of the situational theory of problem solving (Kim and Grunig 2011), individuals are involved in three levels of information behavior: selection, transmission, and acquisition (Kim et al. 2010). According to this model, rotests can be conceptualized as problematic situations that require people to engage in problem solving through transmitting information they have previously selected. In such challenging times, individuals might find in social media an avenue to express themselves, offering their support to one group over another, without the need to engage in actual political behavior. Social media encourage political discourse during protests (Haciyakupoglu and Zhang 2015) and ease communication of personal experiences and stories (Bennett and Segerberg 2012). During the Arab uprisings, Egyptian activists used Twitter to receive information streams from journalists, which they used to spread local updates and organize their offline activities (Kidd and McIntosh 2016). In Russia, Facebook served as a platform to share information about the ongoing events related to the 2011 Duma elections as well as promote the protestors’ agendas (White and McAllister 2014). Elsewhere, Chilean youth actively used Facebook to socialize and engage with the news during the 2010 pro-environmental protests (Valenzuela et al. 2012), while Turkish protestors used social media to criticize the government and legacy media (Baykurt 2013).

Studies have demonstrated that social media are not neutral (Cardenal et al. 2019) and could contribute to segmenting the public and increasing polarization through algorithms that favor individuals’ previous selections and sharing behaviors (Conover et al. 2011; Ohme 2021). Among the motives that drive news sharing, informing others and interacting with them are two important ones (Kümpel et al. 2015). People with strong attitudes were found to express their opinions in political discussions more than others (Matthes et al. 2010), as were politically affiliated individuals compared to non-partisans (Kalogeropoulos et al. 2017). Since transmission of information becomes more intense the more one is active in solving a problem (Kim et al. 2010), individuals involved in protests can be expected to increase their information forwarding behavior. Describing behavior in troubling situations as “cognitive arrest,” Kim and Grunig (2021) contend individuals resort to preset conclusions at which they have arrived in similar previous situations, thus engaging in “machine-like cognitive action” where they “load” their knowledge database with “inclined beliefs” and fire them at others to reach the desired outcomes (p. 233). One such group is partisans whose strength of partisanship has been found to drive online political participation, making politically involved people more likely to share information that is consistent with their attitudes and ideologies (Valenzuela et al. 2012). As pro-attitudinal news consumption invokes feelings of anger, it prompts individuals to share political content related to the topic (Hassell and Weeks 2016). But even those who encounter information inconsistent with their political beliefs are likely to seek like-minded individuals and subsequently share attitude-consistent information (Weeks et al. 2017). Evidence from the 2019 anti-governmental protests in Lebanon corroborates this finding, indicating staunch protestors were more likely than opposers to selectively share protest news on social media (Melki and Kozman 2021b). The result of such selective sharing, Chan and Fu (2017) contend, “can overemphasize one-sided arguments and effectively downplay counterarguments” (p. 268). This could further aid in attitude reinforcement, especially when accompanied by heavy algorithmic social media exposure (Ohme 2021), which leads to social media bubbles that exaggerate the prevalence of specific protest information (Aruguete and Calvo 2018). Based on evidence regarding the relationship between people’s attitudes and their social media sharing behavior, we propose the following hypotheses:

1.3 Selective avoidance on social media

Recent advances in research on communication behaviors that have brought to the forefront the situational theory of problem solving have highlighted the various decisions individuals make during the process of information communication (Kim and Grunig 2011). Besides information acquisition and transmission, the communicative action model outlined above takes into account the act of avoidance, which Kim et al. (2010) label “information forefending” (p. 136). In problematic situations, individuals “fend off certain information by judging its value and relevance in advance in a given problem-solving task,” which helps them reduce cognitive discrepancy as well as manage information overload (Kim et al. 2010, p. 136, italics original). Selective avoidance, or the drive to flee attitude-discrepant information, has also been studied in relation to selective exposure, which is the act of seeking attitude-consistent information (Garrett and Stroud 2014). As the first to test selection bias in political issues, Hyman and Sheatsley (1947) found that individuals seek information congruent with their attitudes and avoid incongruent content. Later experimental research found that avoidance occurred more for attitude-discrepant than for attitude-consistent content (Frey 1982). Selective exposure to pro-attitudinal content, which forms the base of Festinger’s (1957) theory of cognitive dissonance, however, does not automatically predict selective avoidance of counter-attitudinal content (Garrett and Stroud 2014).

Building on theories of psychology and social learning, Case et al. (2005) contended that avoiding information is associated with several cognitive and emotional elements, among which are anxiety, self-efficacy, and locus of control. People practice selective avoidance to create a clean environment that does not include counter-attitudinal information (John and Dvir-Gvirsman 2015). One of the reasons they would do so is political. Social media news users have admitted to hiding, blocking, or unfriending someone for political causes (Rainie and Smith 2012). Other studies also suggest people who are more engaged in political talk and people who hold strong ideologies are subject to practicing selective avoidance on social media (Bode 2016). Considering problematic situations call for more active communicative behavior, individuals’ tendency to engage in more selective information communication, which includes avoiding certain content, also rises (Kim and Grunig 2021). A study of the 2014 Hong Kong Umbrella Movement found as much as 16% of users tend to hide posts or unfriend users for political reasons (Zhu et al. 2017). This percentage is higher than that calculated during politically steady periods, indicating protest-related use of social media promotes shielding from attitude-inconsistent points of view (Zhu et al. 2017). During political unrest, because differences in political opinions are often treated emotionally (Zhu et al. 2017), politically active people with extreme ideologies will most likely unfriend users who disagree with them and hide their comments (John and Dvir-Gvirsman 2015). Thus, the higher the user is politically involved, the higher is their tendency to break ties with those who do not share their opinion (Skoric et al. 2018). Dissonance avoidance was also found to be more common in political than non-political topics, suggesting the reason could be people’s stronger attitudes about political rather than non-political issues (Nam et al. 2013).

Attitude strength, in general, is a crucial driver of selectivity bias that can predict exposure to both pro-attitudinal information (Garett 2009; Garrett and Stroud 2014) and anti-attitudinal information (Peralta et al. 2017). However, the case might be different during political upheaval, a time where social identities are perceived to be threatened, prompting individuals to revert to the group with which they identify (Slater 2015). Consequently, they might avoid information that poses a threat to their opinions, based on the premise that people seek information to reduce uncertainty and anxiety (Berger and Calabrese 1975).

Taken together, the above findings suggest people’s political involvement and attitudes towards an issue puts them at a higher risk to engage in selective avoidance of counter-attitudinal content on social media. The propensity to avoid disagreeable content might be even more pronounced if accompanied by frequent uses of these networks. Generally, heavy activity on social media indicates heterogenous networks, which are linked to higher exposer to diverse opinions, especially when the usage is for political reasons (Kim 2011; Lee et al. 2014). Network heterogeneity is also a natural byproduct of the structural characteristics of social media platforms, considering people use social media for various reasons that extend beyond politics, thus, choosing friends based on a myriad of apolitical criteria (Lee et al. 2014). As people engage with various activities on social media, they come across individuals who might not hold similar opinions. While it might be easy to ignore these people on a regular day, protests carry with them strong emotions that could make people less tolerant to opposing views (Zhu et al. 2017). This could be additionally problematic for heavy users of social media, who are more likely to encounter different types of information (Valenzuela et al. 2012).

Indeed, for those who frequently engage in news-related activities on social media, such as sharing or seeking news, exposure to counter-attitudinal content has been linked to a decrease in their political discussions (Lu et al. 2016). Nir (2011) found that facing opposition in the form of attitude-discrepant content without support from one’s own circle is detrimental to political engagement. Avoiding such opposition, then, could aid social media news users in creating a supportive network that promotes involvement and a sense of belonging that facilitates participation (Zhu et al. 2017). Such acts of selective avoidance, manifested through political unfriending and unfollowing, could be especially intense in larger political discussion networks (Skoric et al. 2018). Based on studies that shows frequency of usage is a significant predictor of various social media phenomena, we expect heavy protest-related social media news use, which includes following and posting news about the protests on social media, to play a role in selective avoidance, in the presence of other political factors, such as issue attitudes and general interest in politics. Although literature provides some direction toward this relationship, research on this topic is still in its infancy. We, therefore, pose the following research question.

2.1 Sample

The total sample size is 6,209 participants. Different sampling methods were applied in different countries. Random samples were pursued in Lebanon and Hong Kong. For Hong Kong, participants were randomly selected from the Hong Kong People Representative Panel and the Hong Kong People Volunteer Panel. Established by the Hong Kong Public Opinion Research Institute, both panels are free and open for anyone to join. Researchers sent emails with a link to the questionnaire to 7,466 from the former panel and 63,806 from the latter. A total of 4,355 responses were received, of which incomplete questionnaires were discarded, leaving the total sample at 3,599. Researchers examined the collected data to make sure that the sample reflected the population. The political inclination of participants was not largely representative of the population and mostly represented liberals (54%). This was inevitable because online surveys attract more youth who tend to be more liberal. For Lebanon, a random sample of 1,000 individuals was targeted based on a population of six million (95%CI, ±3.1%). Questionnaires were distributed proportionally to the number of residents in each governorate. The study adopted a multi-stage probability sampling technique to ensure a random representative sample. In addition, to ensure sufficient representation from protestors in the streets, 30% of the surveys were conducted with respondents present at the main protest squares. Researchers used a systematic random sample in this case, depending on the size of the protest, picking every third person for small and medium-sized gatherings and every fifth person for large gatherings.

Due to political and security risks, non-random samples were the only options in Iraq and Iran. For Iraq, questionnaires were shared on different Facebook groups of Iraqi governorates (south, west and central Iraq). Some surveys were also administered to activists who are members of closed Facebook groups, which are usually used to share information about protests. After an overall sample size of 1,000 individuals was reached, the survey was closed. The sample likely faced self-selection bias. For Iran, an overall sample of 614 participants were selected through a snowball sampling technique. Questionnaires were sent to personal contacts of the researchers in Iran, who in turn, shared the questionnaire with other personal contacts. Questionnaires were either administered through Google Forms or in printed format. Snowballing was the only viable option given the risks of conducting research about this sensitive issue in Iran.

2.2 Instrument and measurement

The questionnaire consisted of only close-ended questions that needed around 10 min to answer. All the variables, except demographics, were measured at the interval level following a 4-point scale. Table 1 shows the sample distributions.

4.1 Limitations and supplemental material

The study encountered several limitations. It did not account for the differences in selectivity when the topic and/or medium changed, although previous research has revealed that these two can influence selectivity (Stroud 2010). Given the different political environments in each country, researchers had to administer questionnaires differently. The inconsistencies in the sampling technique generated differences among the four samples. Some countries administered questionnaires randomly, allowing for generalization, while others administered questionnaires non-randomly and thus, limited the generalizability of the study. This is necessary in many countries of the Global South, especially in states where the political system (e.g., Iran) or the security situation (e.g., Iraq) preclude proper randomized sampling. Additionally, this study used a survey method and relied on self-reporting to understand people’s prior behaviors. While this method provides an opportunity to understand real-life political events, the reliance on self-reporting allows for incorrect reports of prior behaviors and self-presentation issues.

When comparing the sample demographics to publicly available census records, our samples differed from the populations, except for Lebanon where the sample roughly represented the population demographics: 49.6% of Lebanese are female, 49.7% are under 30, and 21% hold university degrees. For Hong Kong, females were underrepresented, while those under 30 and university degree holders were overrepresented, as census records show that 54.2% of the Hong Kong population are female, 26.9% are under 30, and 32.7% hold university degrees. For Iraq, females were underrepresented, and university degree holders were overrepresented: 49.4% of Iraqis are female, 65.4% are under 30, and an estimated 20% hold university degrees. For Iran, females were slightly underrepresented, while those under 30 and university degree holders were overrepresented. The latest census records from 2016 show that 49.5% of Iranians are female, 49% are under 30, and an estimated 25% hold university degrees.

The study provides additional analyses beyond the scope of its hypotheses. In relation to H1, follow-up comparisons between the four groups of attitudes, using the post-hoc non-parametric Mann-Whitney U tests for differences between two groups of the independent variable, returned consistent results with minor discrepancies that do not affect the findings (see supplementary material for details). Moreover, beyond the stated hypothesis, we gauged whether supporters made the decision to share news that supports the protests while simultaneously refraining from sharing news that opposes the protests, and vice versa for opposers. Because the statistical tests violated multiple assumptions, we listed these additional results in the supplementary material.