Social Policy on Social Media: How Opposition Actors Used Twitter and VKontakte to Oppose the Russian Pension Reform

ABSTRACT

How do opposition actors in electoral authoritarian regimes use social media to galvanize support? Based on 2,229 social media posts, I examine how Aleksey Navalny and the Communist Party (KPRF) politicize grievances about the pension reform of 2018 and use Twitter and VKontakte for protest mobilization. I find that Navalny integrates the reform in his narrative of government corruption and theft, strategically using the platforms’ different advantages. The KPRF frames less coherently and makes no distinction between VKontakte and Twitter. These differences, I argue, are mainly due to actors’ different target audiences and their respective position in the authoritarian political system

Introduction

“Make sure you attend [the protest]. Otherwise, you’ll first be robbed, and then you’ll work until you die.” This social media post by Russian opposition politician Aleksey Navalny was part of his campaign against the pension reform that the government announced on June 14, 2018. The reform envisioned raising the retirement age by five years for men and eight years for women. Despite President Vladimir Putin’s earlier promises to the contrary, the government and external analysts claimed that the reform was inescapable since the practice of subsidizing the pension fund from current state budgets was unsustainable (Cook, Aasland, and Prisyazhnyuk 2019).

The reform was criticized on many grounds. For instance, critics alleged that the plans were introduced without public discussion, that they disregarded follow-up problems like unemployment and age discrimination, and that they appeared demographically unjustified given the mean male life expectancy of 66.5 years (Olimpieva 2018). Not surprisingly, the announcement triggered broad popular discontent, with surveys showing between 80 and 90 percent of citizens to be opposed to the plans. A petition gathered about 2 million signatures in three days (Olimpieva 2018), while trade unions and opposition actors organized protests across the country in July and September 2018 (see Maltseva 2019).¹ In August, Putin announced some concessions, bringing the new retirement age for women from 63 down to 60 years (Kremlin.ru 2018), and signed the bill into law on October 3, 2018.

These events offer general insights into oppositional campaigning in electoral authoritarian regimes, polities that feature liberal democratic institutions but undermine them to an extent that precludes real competition and uncertainty (Howard and Roessler 2006; Schedler 2013). There is a large literature that investigates the functions of the formally democratic institutions like elections and parliaments in these regimes (Williamson and Magaloni 2020). Equally well developed is the research on bottom-up regime change through “negative coalitions” (Beissinger 2013) that bring together disaffected citizens from various ideological and socio-economic backgrounds (Goldstone 2011). At the same time, ordinary opposition campaigning in electoral authoritarian regimes is highly understudied.² However, the day-to-day activities of organized opposition reveal much about how such regimes function and thus should receive more attention. This study contributes to the research on authoritarian politics “in normal times,” focusing on the role that social media play under these circumstances.

Even though autocrats often use social media to stabilize their rule (Gunitsky 2015), these technologies still serve important purposes for opposition campaigning in environments where authorities obstruct electoral contention and restrict access to mass media (Breuer, Landman, and Farquhar 2015). In this study, I ask (1) how opposition actors employ social media for the politicization of popular grievances, focusing on actors’ diagnostic framing of problem definition and blame attribution (Snow 2013), and (2) whether and how actors differ in their strategic use of different social media platforms for the mobilization of resistance.

In the empirical analysis, I focus on the anti-corruption activist and opposition politician Aleksey Navalny and the Communist Party of the Russian Federation (KPRF). There are three reasons for this selection. First, they jointly represent a broad section of the ideological spectrum: while both have voiced nationalist positions in the past, Navalny is regarded as a liberal, while the KPRF oscillates between orthodox Marxism and more social-democratic positions (Levintova 2011). Second, they are arguably the most relevant opposition actors on the scene. Third, they differ on important factors that, in democratic contexts, have been identified as drivers of differences in platform usage—in particular the target audience and the previous use of digital technologies (Williams, Girish, and Gulati 2013). In addition, they occupy different positions in the authoritarian political system: while the KPRF is a tolerated opposition party that needs to maintain a balance between voice and loyalty to be able to partake in the authoritarian institutions, Navalny is fully barred from participation. The comparison offers the chance to examine whether these differences, which are specific to electoral authoritarian contexts, affect behavior in situations where both types of actors have an incentive to capitalize on existing grievances.

Building on qualitative and quantitative evidence from 2,229 posts scraped from the two actors’ official accounts on Twitter and VKontakte (the Russian Facebook equivalent) between June 14 and October 30, 2018, I find that Navalny integrates the pension reform into his ongoing narrative of a corrupt and incompetent state, and attributes most of the blame to President Putin personally. The KPRF’s framing, while grounded in a broadly left-wing perspective, displays a variety of problem assessments and a more heterogeneous pattern of blame attribution. The KPRF is thus much less coherent in its diagnostic framing, allowing different voices from the party to impact its online communication, which suggests that the party approached the topic less strategically than Navalny did. This is corroborated by the usage patterns of the social media platforms: while Navalny appears to employ VKontakte (VK) purposefully to increase turnout to his regional protest events, the KPRF makes no distinction between Twitter and VKontakte, suggesting that the party hardly has a social media strategy.

Social Media Under Electoral Authoritarianism: Framing and Mobilization

Long gone are the days when social media were one-sidedly heralded as a new tool in the hands of suppressed citizens to organize against their autocratic rulers (see, e.g., Shirky 2011). Many studies have shown how authoritarian regimes use social media to their advantage, for example by dissuading users from political activism (Pearce and Kendzior 2012), framing public discourse to their benefit, gathering information about the public’s falsified preferences (Gunitsky 2015), or harassing opposition actors (Pearce 2015). Nevertheless, in regimes that erect high barriers to oppositional coordination and bar dissenting voices from traditional mass media, social media fulfill two important functions.

First, they are often the only channel for opposition actors to spread their message and connect with potential supporters by distributing information and frames (Onuch 2015). A frame is conventionally understood as “an interpretative scheme that simplifies and condenses the ‘world out there’ by selectively punctuating and encoding objects, situations, events, experiences, and sequences of action within one’s present or past environment” (Snow and Benford 1992, 137). Frames can be diagnostic (problem definition and blame attribution), prognostic (proposition of solutions), or motivational (reasons for taking collective action). In this study, I focus on the diagnostic frame, which “provides answers to the questions of ‘What is or went wrong?’ and ‘Who or what is to blame?’” (Snow 2013, 3).³

Diagnostic framing by political actors who seek to politicize existing grievances—as is expected in the present empirical case—often entails the construction of an “injustice frame.” These frames do not just “focu[s] on the righteous anger” at “acts or conditions [that] have caused people to suffer undeserved hardship” (Gamson 2013, 1), but also necessarily contain a target. As Snow observes, “[a] life of impoverishment may be defined as an injustice, but its relationship to action is partly dependent […] on whether blame or responsibility is internalized or externalized. Thus, the emergence of an injustice frame must be accompanied by a corresponding shift in attributional orientation” (Benford and Snow 2000, 474). The importance of blame attribution has been observed in the Russian case as well: in the wage arrears crisis of the 1990s, the lack of possible targets for blame attribution explained the relatively low level of collective action despite widespread economic grievances (Javeline 2009). In short, the two parts of the diagnostic frame—problem definition and blame attribution—are closely linked and will hence both be subject to empirical analysis (Hypotheses 1 and 2, below).

The second important function of social media in authoritarian contexts concerns the mobilization of resistance. Where the collective action problem is aggravated by higher participation costs, the capacity of social media to overcome the problem of initial contributions (Bimber, Flanagin, and Stohl 2005; Tufekci 2014) may be particularly important. Social media help to mobilize protesters through facilitating information flows (Reuter and Szakonyi 2015; Tufekci and Wilson 2012) and signal initial protest readiness to observers, which can help to alleviate the free-rider problem (Breuer, Landman, and Farquhar 2015).

However, not all social media may be equally suitable for mobilization and campaigning. Since the literature on the use patterns of different social media platforms in political campaigning is only now emerging (Rossini et al. 2018b, 246–47), and is virtually absent for authoritarian contexts, this part of the analysis is largely exploratory. Still, the literature on democracies offers some orientation.

First, platforms have different technological structures, which shape the type of networks that emerge from their use. For instance, as opposed to Facebook’s “dyadic friend structure,” which produces networks that “largely mirror a user’s offline relationships,” Twitter does not require a user to confirm a requested connection, so that networks are larger and more open (Bossetta 2018, 475). Second, audiences diverge: in the United States, Twitter’s user base is smaller than Facebook’s, but it is “heavily used by journalists, politicians, and political analysts,” which may have consequences for campaign strategizing (Rossini et al. 2018b, 247). Therefore, third, such inter-platform differences have been found to affect candidates’ platform use. For instance, “candidates prefer to use Facebook over Twitter when they are trying to engage supporters to act” (Rossini et al. 2018a, 6). The second part of the empirical analysis builds upon and extends these early insights on differential platform usage in an authoritarian context, investigating the effect of platforms’ different affordances (Hypothesis 3) and the interaction of actor and context characteristics in platform use (Hypothesis 4).

Research Design

Case Selection

For investigating the research questions on framing and cross-platform differences, the pension reform is a suitable case. First, it is a policy measure that affects almost everybody in an understandable way and lends itself to framing from different ideological positions. Second, its broad rejection by the Russian population makes the case an important, rare opportunity for oppositional actors to position themselves and increase their followership. This episode is therefore likely to trigger actors’ serious engagement and to bring their strategies for communication and mobilization to the fore.

I choose to compare Navalny and the KPRF because they are the most important opposition actors in present-day Russia, each with substantial mobilization capacity. At the same time, they occupy different positions within the authoritarian political system, and clearly differ on ideology. The comparison thus captures important variation that is both of interest to students of contemporary Russia and may help to identify broader patterns. However, the case selection also raises questions of comparability. After all, Navalny is an individual politician while the KPRF is a large and complex organization. Findings of cross-actor variation in mobilization and framing, therefore, could be an artifact of these obvious differences. To remedy the problem, official social media accounts of the actors’ central headquarters are chosen as data sources, since both actors can reasonably be expected to make conscious and strategic decisions on the posted content regardless of the difference in volume and variation of their activities. However, given the substantial structural differences between the two actors, results should still be interpreted with caution.

Hypotheses

The first two of four hypotheses address the diagnostic framing of the pension reform by Navalny and the KPRF. Hypothesis 1 tackles the actors’ definition of the problem. Despite the fact that this case of a hugely unpopular policy opens up an opportunity for both actors to broaden their follower base and thus provides incentives to use inclusive framing that appeals to various social groups, I propose that both actors will approach the issue from their respective ideological core positions.

Which are those in the case of the KPRF? In its internal communication it has gradually put greater emphasis on pluralism, political rights, and democracy as compared to orthodox Marxism (Levintova 2011). However, its inability to abandon Stalinist dogmas and symbols, its continuous nationalist orientation (Beichelt 2009), and its uncompromising anti-Western position precluded a social-democratic self-renewal on the example of other communist successor parties (March 2012, 135). The KPRF, it follows, has not accomplished a clean social-democratic transition but also has not fully solidified around the position of “‘leftist retreat,’ centered around anti–bourgeois democracy and anti–capitalist exploitation” (Levintova 2011, 728). For this reason, it is difficult to make concise predictions on whether social-democratic or orthodox Marxist positions will dominate in its pension-related framing. I do expect, however, that the KPRF will anchor its resistance in left-wing claims of social justice.

Since Aleksey Navalny’s co-leadership in the “For Fair Elections” protests of 2011–2012, he has become the most serious (but not uncontested) liberal opposition leader. In contrast to the KPRF’s firm basis on the economic left, Aleksey Navalny’s ideological base is a center-right liberal position that emphasizes human rights, the rule of law, free enterprise, and accountable government. From this value basis, he and his team⁴ regularly publish professionally executed investigations of corruption by bureaucrats and politicians.

However, Navalny appears to be flexible when it comes to capturing prevalent societal moods. Having decreased his nationalist rhetoric of the 2000s (Moen-Larsen 2014), his presidential campaign of 2017–2018 incorporated a strong center-left element with demands for greater social protection and state investment (Dollbaum, Semenov, and Sirotkina 2018). His engagement with the pension reform reveals a similar strategic consideration—to connect an issue of high societal relevance to his long-term plan to decrease public tolerance of the regime and to pressure the ruling group. Whereas social justice and care for the elderly are part of the KPRF’s core message, in Navalny’s case the opposition to an economically liberal reform needs a conscious framing effort if he is to stay true to his political orientation. Therefore, I expect him to approach the pension reform from a liberal perspective, incorporating the issue into his main cause of fighting corruption and stressing individual cost due to “theft” by an allegedly corrupt government. Hence,

H1: In defining the problem of the pension reform, Navalny focuses on corruption and theft; the KPRF stresses social justice and the responsibility of the state vis-à-vis its citizens.

The second hypothesis addresses the second part of the diagnostic frame, namely, “Who is to blame?” I expect the two actors to attribute blame differently because of their different positions in the political system.

In accordance with the functionalist arguments on the tempering of opposition through including them in parliaments (Williamson and Magaloni 2020), Reuter and Robertson (2015) have found that the KPRF is susceptible to cooptation incentives, reducing its protest activities in regions where its elites control lucrative parliamentary posts. However, in contrast to the other two parliamentary opposition parties (the nationalist LDPR and the center-left Just Russia), this cooptation incentive appeared not to have affected the KPRF during the largest post-Soviet protest wave in 2011–2012 (Dollbaum 2017), suggesting that the party’s public activity is a permanent balance between voice and loyalty. As an in-system party in an essentially uncompetitive authoritarian regime, it participates at the mercy of the authorities and thus needs to avoid provoking its marginalization and exclusion by attacking the authoritarian hierarchy (think: the ultimate public authority of the president). At the same time, it seeks to increase its relative representation and influence in the institutions, for which it needs to capitalize on and politicize existing grievances. With regard to the pension reform, I therefore suspect that the KPRF’s framing largely spares the popular President Putin, instead attributing blame to the government, the vague “authorities” (vlasti), or capitalism as an economic system. If it does attribute personal blame, I expect the party to focus on Prime Minister Dmitry Medvedev, who is a much easier target due to his low approval ratings and his perceived expendability.

Navalny, by contrast, is not beholden to the political system because he is not allowed to participate in it. Since his second place with 28 percent of the vote in the 2013 Moscow mayoral elections (a success that likely ran counter to the authorities’ calculations), he has been barred from ballots, his party-building attempts have been blocked, and candidates whom he officially supports are often excluded from regional and local races on dubious formal grounds. Not having to lose a place inside the system, he is likely to use every opportunity to solidify his self-styled image as the only uncompromising opposition force in the country and an alternative to President Putin, and hence to strategically attribute the cause of the grievances directly to Putin himself.

Given actors’ different positions in the political system and the resulting goals of their politicization strategies, I therefore propose:

H2.1: Navalny attributes more personal blame than the KPRF.

H2.2: In their personal blame attribution, Navalny targets President Putin, whereas the KPRF (to the extent that it does so at all), focuses on Prime Minister Medvedev.

Hypotheses 3 and 4 focus on differences in the usage of two social media platforms—VKontakte (the Russian equivalent of Facebook) and Twitter. Due to the large differences in audience and functionality between these two platforms, this combination serves as a most-likely case for detecting cross-platform variation: If the actors do not differentiate their social media behavior between these two channels, they are unlikely to do so at all.

Hypothesis 3 concerns differences between the two platforms with regard to the two actors’ protest mobilization in the regions. VKontakte is much more widely used than Twitter: In a poll by the Levada Center from December 2017, 65 percent of respondents claimed to be using VKontakte, compared to only 7 percent who used Twitter (Levada Center 2018a). When active usage is concerned, Brand Analytics, a Russian company that provides data on social media, found that in late 2019 there were 30.7 million users who had written at least one public post in a given month—almost 50 times the respective number on Twitter (650,000). This active usage can be broken down by region, showing that the share of Twitter authors in Moscow and St. Petersburg is on average 5.5 to 6.5 times higher than in the regions. With regard to VK, Moscow is only 1.5 times the average, while St. Petersburg’s share of active authors is 2.8 times higher than the average region (Brand Analytics 2020). VKontakte, it follows, is much more present in the population and is more widely used in the regions compared to Twitter. Indeed, VKontakte has been used for political campaigning in the regions. For instance, during Navalny’s presidential campaign in 2017, VKontakte was the network of choice for coordinating the public part of his regional network (Dollbaum 2020). If actors act strategically, they can be expected to take these platform differences into account. Therefore,

H3: In the days before large regional protests in July and September 2018, both actors use VKontakte more heavily than Twitter to reach the regional audience.

Finally, Hypothesis 4 addresses the interaction of actor-specific and platform-specific factors. Research on the diffusion of new technologies as a political campaigning tool has proposed three groups of factors that explain early adoption of digital technologies: a candidate’s or party’s resources, characteristics of their targeted constituency (in particular the level of education at the targeted district), and environmental constraints, most importantly the degree of competitiveness of a race (Williams, Girish, and Gulati 2013, 53–54). Moreover, previous use of other new technology has been shown to be a strong predictor of social media adoption and intensity of use (Williams, Girish, and Gulati 2013).

Since the literature focuses on a different context, its direct applicability is questionable. However, the idea that the targeted constituency and previous practice are relevant for a political actor’s social media use appears plausible. For many years, Navalny has been employing various kinds of digital technologies and social media, both externally for his anti-corruption and strategic voting campaigns (Dollbaum 2019), and internally for coordinating his regional network (Greene and Robertson 2019). His core constituency, moreover, can be described as well-educated digital natives. Although he made a serious effort to expand his appeal to citizens from various socio-economic backgrounds, the visual style and the messages of his 2017–2018 nation-wide campaign were geared to attracting urban, young, and educated supporters (Dollbaum, Semenov, and Sirotkina 2018).

The KPRF, by contrast, has not made headlines as a social media enthusiast. Despite the fact that the party’s constituency in the 2000s gradually changed from elderly rural voters to including younger, better educated urban residents (Gel’man 2011), its number of followers on VK and Twitter (between 65,000 and 80,000) is still very low compared to its support in the polls, which since 2016 has fluctuated around 15 percent (Levada Center 2020). Navalny, by contrast, has 2.2 million Twitter followers with a hypothetical electoral support that is most likely lower than that of the KPRF.⁵

Moreover, the different position of the two actors in the political system means that the KPRF has several ways to raise awareness about its actions—including legally guaranteed airtime before elections and press coverage of its parliamentary activities—so that there is less of a need for developing a fine-grained social media strategy. Navalny, by contrast, is forced to use whatever channel is available that is not under the direct or indirect control of the authorities. For these reasons, the KPRF is likely to use social media in a less sophisticated way than Navalny does. If that is the case, we should observe different degrees of cross-platform variance between the two actors:

H4: Navalny’s social media content displays greater cross-platform variation than the KPRF’s.

Data and Methods

In a first step, all Tweets and VK posts were scraped that were sent from Navalny’s personal VK and Twitter accounts and the official VK and Twitter accounts of the federal party headquarters of the KPRF⁶ between June 14 and October 30, 2018, spanning the period from the announcement of the reform to about one month past the president’s signing of the bill. Retweets and reposts were excluded. In the given time period, the KPRF published 1,335 VKontakte posts and 485 Tweets. Navalny published 168 VK posts and 241 Tweets.

In a second step, all posts that included the stem “pension” were automatically labeled as relevant. The share of pension-related posts is higher for the KPRF than for Navalny on both platforms (56 percent on VK and 42 percent on Twitter vs. 27 percent on VK and 19 percent on Twitter), meaning that the KPRF devoted more content to the pension reform in both absolute and relative terms. These differences likely reflect the different target audiences and political orientations of the two actors: while social policy constitutes a core topic for the KPRF, it is an unusual one for a liberal in the Russian context.

In a third step, 40 posts from each account (160 in total) were selected for in-depth analysis. From each account, the first ten posts on the pension topic were included in this corpus in order to capture how the actors set up their framing of the topic at the onset of the public discussion. Then, 30 additional pension-related posts were randomly selected from each account. The posts chosen for in-depth analysis were coded according to the procedures of qualitative content analysis (Schreier 2012). At first, several categories were created that matched the theoretical interest of problem framing and blame attribution. Subsequently, 20 percent of the selected material was coded in an open fashion, where more subcodes were generated for problem framing and blame attribution (Schreier 2014). This full code system was applied to all 160 posts (Supplementary Materials).

Finally, in those cases where it was possible to operationalize the generated categories with single words and phrases, automated coding was applied to all 2,229 posts. As a result, there is quantitative evidence from the full text base pertaining to all four hypotheses.

The KPRF’s VK posts are often long⁷ and touch several topics, because they often paraphrase a party functionary’s comments on several recent events. For this reason, the quantitative analysis of mentions of specific words and phrases (hypotheses 1 and 2) is carried out on the paragraph level rather than the document level, so that only paragraphs are considered that explicitly mention the stem “pension.” This conservative procedure likely underestimates the absolute numbers of relevant mentions, but it ensured that all registered mentions concern the topic under study.

Results

Hypothesis 1: Problem Framing

I first examine the predictions concerning the problem framing, comparing descriptive quantitative evidence of (1) relative mentions of the key concepts of “theft” and “corruption” and (2) relative mentions of the concept of “social justice” in statements of both actors that relate to the pension reform. While theft and corruption are measured simply by the occurrence of the respective word stems, social justice is disaggregated into the two subcodes [(in)justice] and [antisocial]. The former is measured with its Russian stem spravedliv,⁸ the latter is measured with the terms “antisocial” (antisotsial’no) and “anti-people” (antinarodno). Table 1 lists the results.

Table 1. Mentions of Concepts in Social Media Posts by Actor and Platform (In Percent of All Pension-Related Paragraphs that Contain an Assessment)

		VKontakte		Twitter
		KPRF	Navalny	KPRF	Navalny
H1: Expectations for Navalny	theft	24.2%	72.2%	12.5%	44.4%
	corruption	2.9%	11.1%	0.0%	11.1%
H1: Expectations for KPRF	justice	4.7%	0.0%	0.0%	0.0%
	antisocial	32.0%	0.0%	35.0%	0.0%
exploratory	scam/lie	14.9%	11.1%	5.0%	44.4%
	cannibalistic^a	25.1%	0.0%	42.5%	0.0%
Paragraphs that contain assessment as % of all pension-related paragraphs		18.5%	16.5%	13.0%	17.3%
Number of pension-related paragraphs that contain assessment		765	18	40	9

^aThe Russian term is liudoedskii. Source: Author’s calculations based on full text base of both actors’ posts between June 14 and October 30, 2018.

Consistent with Hypothesis 1, Navalny focuses his problem definition on theft, using related words in 72 percent (VK) and 44 percent (Twitter) of paragraphs that contain a problem framing. Indeed, from the coded assessments this seems to be the most widely used, given that it is also found in the KPRF’s framing. Concerning corruption, the results are again formally in line with the hypothesis, but, due to their low absolute number, need qualitative evidence for backup.

Turning to the expectations about the KPRF, the data show that, contrary to expectations, the term “(in)justice” plays a secondary role in the party’s assessment of the pension reform. The concept itself, however, is quite salient: the [antisocial] subcode is heavily used by the KPRF on both platforms, while Navalny abstains from it, supporting Hypothesis 1.

The in-depth analysis reveals two more assessments that had not been included in a hypothesis (see the “exploratory” section in Table 1). Of these, the concept of scam/lie appears to be relevant for both actors. This is intuitively plausible, because accusing the authorities of deception is a moral appeal that lends itself to incorporation into various political positions. A surprisingly strong element is the word “cannibalistic” (liudoedski in Russian, literally “people-eating”), which is exclusively and quite heavily used by the KPRF. Its prominence on Twitter (42.5 percent of pension-related paragraphs), which it owes to its frequent usage in the headlines of tweeted press releases, suggests that it could be a strategically placed frame.

Another difference between the two actors concerns the breadth of claims. The KPRF’s posts often voice problems that appear to be driven by the respective speaker’s individual perspective. Some posts, for instance, predict future problems in the labor market due to the pension reform—sometimes voiced in not very socialist language:

What does raising the retirement age mean in the absence of economic growth capable of creating new jobs? It means that the labor market will be overcrowded. (KPRF, VKontakte, June 14)

Other claims include the alleged reduction of care time for children (when grandparents work rather than babysit), increased stress levels, increased poverty through the loss of pensions as additional income, and problems for elderly people to find work. In all, the KPRF’s posts feature a multitude of voices that emphasize different aspects, suggesting a rather low level of strategic framing—with the potential exception of the term “cannibalistic.”

By contrast, Navalny focuses strongly on his main points of theft and the connection to corruption. In several posts he argues that the pension fund is made up of people’s savings that are deducted from their paycheck:

If there were no such taxes and fees, your salary would be higher. This is from your salary, this is your money. (Navalny, VKontakte, June 25)

In Navalny’s argument, reducing the number of years one is eligible for pension payments means giving citizens less of these savings back, which he equates to theft. The cause of the reform, in turn, is portrayed as a consequence of previous embezzlement of public money:

Raising the retirement age is the greatest corruption. Or rather like this: the years of corruption by Putin and his entourage have resulted in raising the retirement age. (Navalny, VKontakte, August 14)

In this way, he ties the issue to his anti-corruption agenda and integrates it into his liberal perspective, stressing the wealth decrease for individual citizens in favor of those with personal connections to Putin:

Russian citizen, retire 5 years later so [loyal business magnate] Roman Abramovich can continue to avoid paying taxes in Russia by buying penthouses in London! (Navalny, Twitter, July 13)

In sum, the qualitative and quantitative data support the hypothesis: Navalny focuses on theft and corruption, while the KPRF places emphasis on social justice. In addition, Navalny is much more concentrated in his framing and mentions far fewer reasons to oppose the pension reform as compared to the KPRF, whose posts appear as a collection of voices from different sections of the party, with little evidence of directed efforts at driving home any particular interpretation.

Hypothesis 2: Blame Attribution

Hypothesis 2 holds that Navalny blames the pension reform on persons—in particular President Putin—to a greater extent than the KPRF does. Looking first at the descriptive statistics of the full text base (Table 2), the data corroborate that hypothesis. Of all Navalny’s paragraphs that contain a target, about two thirds refer to a person, while the same is true only for about one third for the KPRF. Moreover, in Navalny’s posts, Putin clearly outweighs Medvedev. These numbers clearly support the proposition that Navalny strategically seeks to tie the pension reform to President Putin himself.

Table 2. Mentions of Targets by Actor and Platform (In Percent of All Pension-Related Paragraphs that Contain a Target)

	VK		Twitter
Targets (sorted by increasing concreteness)	KPRF	Navalny	KPRF	Navalny
Capitalism/liberalism	4.7%	0.0%	4.4%	0.0%
Authorities (vlasti)	32.5%	16.7%	30.9%	0.0%
Governing party/government/Kremlin	62.9%	41.7%	45.6%	50.0%
Person^a	34.1%	66.7%	33.8%	72.2%
Putin	22.8%	61.1%	23.5%	72.2%
Medvedev	14.4%	8.3%	10.3%	5.6%
Paragraphs that contain target as % of all pension-related paragraphs	40.9%	33.0%	22.1%	34.6%
Number of pension-related paragraphs that contain target	1689	36	68	18

^aThis line refers to mentions of either Putin, Medvedev, or both. Source: Author’s calculations based on full text base of both actors’ posts between June 14 and October 30, 2018.

The distribution of person-related references is more even for the KPRF, with Putin featuring less prominently and Medvedev more prominently as compared to Navalny’s posts. However, even in the case of the KPRF, Putin is mentioned more often than Medvedev, disproving Hypothesis 2.2.

Examining the table as a whole, it stands out that the frequency with which Navalny mentions different targets increases with these targets’ concreteness. Expectedly, he does not mention capitalism or liberalism at all, and he also does not often use the very vague term vlasti (“authorities,” or “those in power”). Instead, he focuses either on relatively specific groups such as the government, the party of power, and the Kremlin—or on Putin directly. In the KPRF’s case, such a hierarchy is not discernible, and overall, its blame attribution is much less concrete. Although the economic system plays only a very marginal role in the KPRF’s discourse, the KPRF uses the vague category of “authorities” to a much greater degree than Navalny.

The quantitative comparison has provided support to the hypothesis that Navalny attributes more personal blame than the KPRF, and, within this category, puts more relative emphasis on President Putin. In addition, the in-depth analysis reveals that there are important qualitative differences between the two actors’ modes of personal blame attribution.

Making Putin personally responsible appears to be at the center of Navalny’s framing strategy. On June 14, the day of the government’s announcement, he explicitly set the direction of his future treatment of the topic:

The whole issue of raising the retirement age should be considered solely in the context of Putin’s violation of a promise he has publicly made many times. (Navalny, Vkontakte, June 14)

In this effort, he fused his main frame of theft with the concrete blame attribution:

The increase in the retirement age, planned by Putin, is not some abstract “failed [neudachnoe] decision of the government,” but an absolutely concrete robbery of every person. (Navalny, Vkontakte, June 25)

In July, in the context of first polls showing Putin’s support decline in connection to the pension reform, Navalny wrote very openly about his team’s framing goal to undermine the previously practiced strategy in which the government and Medvedev receive the blame for unpopular reforms while Putin emerges unscathed:

This time, the Kremlin is unable to play the ‘good Tsar—bad boyars’ card. The people are fed up with it. […] Putin no longer has an airbag for writing off all his failures. This is also the result of our joint efforts. (Navalny, Vkontakte, July 2)

Again, the KPRF’s online communication appears much less focused and coherent. On the one hand, consistent with Hypothesis 2, there are clear instances of calling for the president’s intervention rather than attacking him directly. This is especially apparent in the statements by party leader Gennady Zyuganov:

If I were the president, I would immediately announce a freeze on the cannibalist [liudoedskuiu] pension reform for the next five years. (KPRF, VKontakte, September 24)

At the same time, he blamed United Russia directly:

“As for the party of power,” G.A. Zyuganov further noted, “it decided on Thursday to drag this law along and press it [through parliament].” (KPRF, VKontakte, July 17)

In September, he again stressed the government as the main culprit and put Putin forward as the one who reacted to civic pressure and implemented concessions:

[T]he two waves of our protests have still produced a great result. The President had to confess that he has a bad government. He brought women’s retirement age down by three years. (KPRF, VKontakte, September 22)

On the other hand, other posts contain direct attacks on Putin himself:

How come, Vladimir Vladimirovich? You promised that as long as you’re president, no one will touch the retirement age. Turns out—a lie! (KPRF, VK, June 15)

The secretary of the Voronezh Communist Party Committee, Denis Roslik, called on citizens to discard their naive misconceptions that President Putin had nothing to do with the pension reform […]. He does, and the most direct one, because he is in charge of all officials and oligarchs. (KPRF, VKontakte, July 19)

These attacks usually come from lower-rank party figures, but the fact that they exist and make it into the party’s official social media communication underlines the diversity of positions inside the party and the absence of tight controls on the framing used.

There is one more difference in the approaches to blame attribution that the qualitative analysis reveals. In contrast to the KPRF, Navalny uses the topic to blame the authorities not just for the reform itself, but also for obstructing the resistance to it. His posts frequently refer to reactions of the state to his actions (while only very sporadically referring to others who organize protests, including the KPRF). Re-tweeting a journalistic investigation into an alleged counter-framing move, he wrote:

Ha-ha. Read this. The Kremlin has launched an entire bot farm in response to our campaign against raising the retirement age. (Navalny, Twitter, June 28)

In October, he tweeted:

People in Russia are supposed to work until they die. And whoever is against that is imprisoned and fined. A court fined an FBK [Anti-Corruption Foundation] lawyer 250 thousand rubles for organizing an unsanctioned [protest] action on September 9 in Moscow against the pension reform. (Navalny, Twitter, October 26)

In conjunction with his frequent mentions of the reform in connection to fierce criticisms of all levels of power, including allegedly corrupt Duma deputies and the Duma speaker, Vyacheslav Volodin (VK, August 16), bureaucrats in the Moscow’s mayoral office (VK, August 25), the police (VK, August 26), and the courts (VK, June 22), these attacks appear as part of Navalny’s ongoing effort to portray the state under Putin as an incompetent regime incapable of addressing the population’s concerns, a regime whose only response to legitimate criticism is repression (Dollbaum, Semenov, and Sirotkina 2018).

Overall, therefore, the results show important inter-actor differences. Navalny’s focus on concrete, mostly personal blame, and harsh personal criticism of Putin, appears to be part of a concerted, strategic effort. The KPRF, by contrast, is not focused on personal blame. While Putin, perhaps surprisingly, is mentioned twice as often as Medvedev and does receive direct criticism, these accusations are not coherent in their message: sometimes he is criticized for not intervening, sometimes he is framed as the main culprit. The findings on both actors’ communications therefore structurally mirror the results on Hypothesis 1, with Navalny following a coherent framing strategy, while the KPRF seems not to have any strategy on blame attribution whatsoever. Zyuganov’s statements are a carefully phrased walk on a tightrope, but the same cannot be said about the party as a whole. Instead, its online communications seem to reflect the relatively high independence of regional and local party branches, which is in line with earlier findings on the party’s regional protest behavior (Dollbaum 2017).

An alternative explanation could be advanced, arguing that, given the party’s co-opted and fragile within-system status, the lack of a coherent blame attribution could in itself be a strategy: party elites are careful, while the lower levels are allowed to speak out.⁹ The nature of the data does not allow discriminating between these two interpretations, but both converge in the assessment that the party sends unclear signals, making it harder for possible supporters to correctly gauge its stand on the issue—which is likely a liability when the goal is to increase its popular support.

Hypothesis 3: Protest Mobilization and Cross-Platform Variation

The focus now shifts from inter-actor differences in framing to inter-platform differences with regard to their usage in mobilization. Navalny, the KPRF, and several trade unions organized a first wave of protest events around July 1—usually in parallel rather than in collaboration (see Meduza.io 2018). The KPRF conducted two more Russia-wide waves of protest, one on July 28 and one on September 2. Navalny instead organized a regional protest on September 9 (Olimpieva 2018). Because in Russia, VKontakte is much more widespread than Twitter, especially in the regions, I expect actors to give VK preference over Twitter in times of mobilization—that is, in the days before these regional protest waves.

To test this proposition, Figure 1 plots the kernel density of pension-related posts in the whole covered time period.¹⁰ In the KPRF’s case, there are only minuscule and statistically insignificant differences between the curves of the two platforms (p =.87),¹¹ disproving Hypothesis 3 and, more generally, suggesting that the KPRF did not differentiate much between the platforms in its pension-related posting behavior. Moreover, the fact that the peaks are on the day of protest rather than shortly before suggests that the KPRF used social media chiefly to report about events rather than to mobilize for them. This is corroborated by the texts of the respective posts, which usually contain phrases like “Rallies were held in Samara as part of the All-Russian protest action [against the pension reform]” (KPRF, VK, September 2).

Figure 1. Kernel density plots of pension-related social media posts by actor and platform. A Kolmogorov-Smirnov test on the difference of the cross-platform distributions is statistically significant for Navalny’s posts (p=.01) but not for those of the KPRF (p=.84). Dates of regional protest waves are marked with vertical lines: Dotted = organized by the KPRF, dashed = organized by Navalny, dashed and dotted = organized by both in parallel. Source: Own calculations based on full text base of both actors’ posts between 14 June and 30 October 2018

In Navalny’s case, the differences between the curves is more substantial and statistically significant (p = .01), with the VK posts peaking before rather than on the day of the two regional protest waves his campaign was involved in (July 1 and September 9). His Twitter posts on the topic, by contrast, peak at the onset of the public discussion and then follow a downward trend. For Navalny, the patterns thus provide support for Hypothesis 3 with regard to his second wave of rallies, where the VK activity clearly outperforms the Twitter activity. More generally, the distribution of Navalny’s VK posts suggests strategic use of VK (as opposed to Twitter) for regional protest mobilization.

This interpretation is supported by the fact that Navalny was serving a 30-day arrest between August 25 and September 24, immediately followed by another 20-day arrest until October 14 (Deutsche Welle 2018). In this period, both accounts (and also his blog) continued posting, meaning that his team operates them when he is unavailable. The strong peak in the VK curve suggests that VK was considered to be of greater importance than Twitter for providing a high protest turnout in the regional protests on September 9.

In sum, the analysis reveals that VKontakte is preferred over Twitter for protest mobilization only in the case of the regional protest wave on September 9 that was organized exclusively by Navalny. The KPRF, by contrast, did not differentiate between its social media channels, and it focused its communication on reporting about protest events rather than on mobilization. These findings suggest that cross-platform differences are driven by cross-actor differences—which leads directly to the fourth and final hypothesis.

Hypothesis 4: Differences in Cross-Platform Variation

The similarity of the KPRF’s kernel density plots in Figure 1 suggested that the party does not differentiate much between the two platforms, at least with regard to timing: on days when it posts pension-related content on VK, it does so on Twitter, too. Computing the textual overlap between the two platforms in the full text base reveals that not only is the timing similar, but the content is to a large extent identical: of the KPRF’s 954 pension-related posts (751 VK posts and 203 tweets), at least 151 were used for both platforms.¹² This means that approximately 74 percent of the party’s tweets on the topic were posted also on VK in identical wording. The same is true for only 15 percent of Navalny’s tweets.

The qualitative analysis shows that the KPRF’s Twitter and VK overlap so strongly because the vast majority of the party’s social media content comes from the same source. Of the 80 posts that were analyzed in depth, only three VK posts appeared on this platform alone (see Table A2 in the Supplementary Materials). All others were reposts of other materials, usually press releases from the party’s central committee or from one of the regional branches hosted on the central party’s website. The consequence of posting lengthy content regardless of the platform’s technical setup is that, on Twitter, virtually all¹³ of the KPRF’s posts are arbitrarily cut off after 280 characters, often in the middle of a sentence, with a link provided to the full text on the party’s website. Given that Twitter and VK were selected so as to construct a most-likely case for cross-platform variation, it is likely that other social media channels will not strongly differ from this picture. Overall, the evidence thus shows that the KPRF hardly has a social media strategy.

The significant difference between the curves of Figure 1 already suggested that Navalny uses platforms more strategically. The qualitative analysis corroborates this finding. He uses Twitter as a tool of personal public communication by retweeting and commenting on online content, sharing memes and experiences, and making personal comments on news, allies, and adversaries. VKontakte is instead strictly used as a campaign tool. In this, not unlike the KPRF’s practice, the VK account mostly disseminates content generated and hosted on other sites, in this case on Navalny’s blog. Of the 40 VK posts analyzed in depth, 34 partly or fully reproduce the text of a blog post and provide a link to it, while only 6 are original posts that do not appear elsewhere (by contrast, of the 40 tweets, 31 are original content).¹⁴ Despite the relative lack of original content on Navalny’s VK account, however, the analysis has provided evidence that its content is deliberately placed for mobilizational purposes in the regions, while Twitter is not. Hence, the data show that differences between the uses of the two platforms are larger in Navalny’s case, corroborating Hypothesis 4.

Conclusion

How do oppositional actors in a restricted environment use social media to (1) frame their opposition to a social policy reform and (2) mobilize resistance against it? An important finding of this study is that for both framing and usage patterns, differences between actors are greater than differences between platforms.

Specifically, in its problem definition, the Communist Party appears not to follow a particular strategy. While overall it puts emphasis on concerns of social justice, frequently calling the reform “antisocial” or “anti-people,” it speaks with many voices and does not follow a strict Marxist, anti-capitalist rhetoric. The same is true with regard to blame attribution. On the one hand, blame is often shifted rather vaguely to “the authorities”; also, party leader Gennady Zyuganov strikes the expected, delicate balance between attacking the governing party and sparing the president. On the other hand, some lower-ranked party figures do blame Putin directly and their accusations are included in the federal party’s social media feeds. Therefore, what is most striking about the party’s framing is its decentralized, uncoordinated appearance. This impression is reinforced by the absence of any differentiation of platforms, suggesting that the party lacks a strategic approach to social media.

The evidence on Navalny suggests that he and his team approach social media much more strategically, regarding both framing and platform use. His posts display a clear orientation toward (1) framing the pension reform as theft of citizens’ savings, and (2) being as concrete as possible in his accusations, integrating the pension reform into his main theme of a corrupt and incompetent state led by a corrupt and incompetent president. While his VK posts mirror his blog and only his Twitter account produces original content, the two platforms are used in different ways, suggesting deliberate use of their respective affordances.

Several limitations have to be discussed. First, a potential problem arises from the significant difference in the absolute number of posts between the two actors. This underlines the structural differences between the two, one being a large and complex organization, the other being a single politician (albeit with an organizational base). However, the apparent imbalance appears also to be a result of the differences in the actors’ target audiences and their approaches to social media. For one thing, social policy is a left-wing party’s natural habitat, while it is hardly the core topic for a liberal—even taking into account Navalny’s attempts to tap into a center-left constituency. Moreover, the results have shown that the KPRF not only produces a large amount of content but applies a blanketing approach to it. Navalny’s team, by contrast, places content strategically and therefore more selectively. Hence, I argue that the difference in output quantity largely reinforces the study’s conclusions.

Second, the study is obviously limited in scope, having focused on only two actors, one issue, and two platforms. At least, the latter constraint is somewhat mitigated by the strategic selection of platforms, which makes it unlikely that major differences would be detected if functionally more similar channels were considered.

A third limitation is certainly the focus on top-down communication at the expense of interactions with the actors’ audiences. It is a worthwhile task to assess whether the activity profiles in the given environment match Stephanie Bor’s finding that candidates in the United States use social media chiefly to “generate campaign contributions, to control their campaign message, to humanize their candidate, to assess message effectiveness, and to promote messages on other communications platforms” (Bor 2014, 1206), and whether inter-actor differences play as strong a role for interaction as they do for top-down communication.

Finally, the study stops short of estimating the effectiveness of the actors’ mobilization and framing. There are several dimensions to this to consider in further research. First, concerning the actors’ standing with the electorate, data from the Levada Center suggest that Navalny’s more strategic approach may have been the more successful. While the KPRF’s electoral rating did not change between December 2017 and August 2018 (Levada Center 2020), Navalny’s “rating of distrust” fell between January and July 2018 from 13 percent to 6 percent (Levada Center 2018b). However, these data are too crude to claim them as an effect of Navalny’s pension-related actions. Second, it is possible that the protests discussed in the actors’ messages contributed to the substantial fall in Vladimir Putin’s support that all pollsters registered over the summer of 2018. But to causally attribute these changes to particular actions, one would need panel data on exposure to various frames and resulting changes in attitudes. Third, concerning the result, Putin’s announcement in August was likely part of a plan to shift blame to the government and absorb some praise for concessions. Since his ratings did not rebound, this was clearly a miscalculation. However, the fact that the mobilization was not able to halt the reform despite disapproval by the overwhelming majority of the population speaks to the difficulties of bottom-up policy influence under the given authoritarian conditions. Future research could investigate these three dimensions of effectiveness in greater depth.

Despite these limitations, the findings contribute to the literature in three ways. First, they suggest that even under conditions of restricted media freedom, where social media are potentially of even greater importance for oppositional actors seeking to campaign and organize, actors use it in very different ways. I have argued that this variance results from differences in target audience, previous use, and actors’ position in the political system (in-system vs. out-of-system opposition). The first two factors are known from the literature on democratic contexts. This suggests that social media agitation by political actors follows similar basic rules in democratic and electoral authoritarian contexts. At the same time, the third factor is specific to electoral authoritarian regimes, suggesting that a nuanced approach to “opposition” in authoritarianism (see, e.g., Gel’man 2008) is conceptually warranted, since such differentiations map onto behavioral patterns.

Second, the results provide insight into political campaigning in authoritarian regimes during the heavily under-researched episodes when oppositional actors try to capitalize on commonly held grievances but are far from threatening the distribution of power. An obvious way for both actors to increase effectiveness would have been to fuse their resources and to project an image of a united opposition ready to bridge strategic and ideological divides. However, the cross-actor difference on problem definition and blame attribution as well as their evident reluctance to cooperate on the organization of protest events suggest that neither of the two actors sought to seize the opportunity for constructing a cross-ideological movement. This could be a function of actors’ expectations of success: when regime overthrow appears unrealistic, it may be more rational to solidify and gradually expand one’s own follower base instead of reaching too far into others’ territories (see Wahman 2011 for a similar argument on electoral coalitions in authoritarian regimes). Future research could thus investigate the hypothesis that low expectations of success disincentivize negative coalition building (Beissinger 2013; Goldstone 2011).

Third, the study provides fruitful insights into the KPRF’s behavior. In particular, the breadth of views and interpretations that make their way into the party’s online communication bolsters previous findings on the considerable autonomy of local party branches and activist groups (Dollbaum 2017; Hutcheson 2005). At the same time, the apparent lack of cohesion in framing and strategic use of social media make the KPRF appear to be punching below its weight. However, in a context where the liberal opposition receives the lion’s share of both the journalistic and the academic attention, there is still too little evidence to make these claims with confidence. More systematic investigations into the party’s inner structures, its behavior, and its conditions for coalition-building are therefore worthwhile avenues for future research.

Acknowledgments

I thank Heiko Pleines for comments on an earlier draft of the manuscript.

Disclosure Statement

No potential conflict of interest was reported by the author.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/10758216.2020.1800492.

Additional information

Funding

This publication has been produced as part of the research project “Comparing Protest Actions–Part 2,” which is being organized by the Research Centre for East European Studies at the University of Bremen with financial support from the Volkswagen Foundation.

Notes

1. In connection to the ongoing World Football Championship, protest demonstrations between May 25 and July 25 were prohibited in cities where matches were held. As a consequence, protest events in July mainly took place in other cities. See Meduza.io (2018) for a list of events and their organizers between June 27 and July 9, 2018.

2. But see Wahman (2011) and Gandhi and Reuter (2013).

3. Focusing on one frame type instead of all three allows a more in-depth analysis within the given space constraints. Still, due to its two-part structure, the diagnostic frame covers a relatively broad range of both actors’ communications.

4. Since 2010, Navalny has built up a network of formal and informal organizations that coordinate his various projects and campaigns. At the center of this network is the Anti-Corruption Foundation (FBK), which conducts and produces investigations of the embezzlement of public funds.

5. Unfortunately, the latest reliable survey data on Navalny’s potential electoral support are from March 2017. Back then, about 17 percent of those who knew his name (about 54 percent of respondents) declared they would “definitely” or “perhaps” vote for him. Two months earlier, before the YouTube release of his very popular investigation into Prime Minister Dmitry Medvedev’s alleged corruption, that number stood at 10 percent.

6. In June 2018, Navalny’s VK and Twitter accounts had about 400,000 and 2.2 million followers respectively, the KPRF’s about 65,000 and 80,000 followers. In the case of Navalny’s VK and the KPRF’s Twitter account, it was not possible to trace the exact number of followers for June 14, 2018. These numbers are thus to be understood as estimates. The accounts are: https://twitter.com/navalny, https://twitter.com/kprf, https://vk.com/navalny, and https://vk.com/kprf.

7. The KPRF’s VK posts have a mean length of 534 words, more than three times the length of Navalny’s average of 172 words per VK post.

8. Any mentions of unrelated entities such as the Just Russia party are excluded.

9. I thank an anonymous reviewer for pointing this out.

10. Kernel density estimates compute probability distributions based on empirical samples. The plots can be read as smoothed histograms for continuous data.

11. For comparing the kernel distributions, two-sided Kolmogorov-Smirnov tests were used.

12. This is a conservative estimate (see Supplementary Materials for the calculation procedure).

13. Specifically, 98 percent of the pension-related tweets and 95 percent of all tweets in the given time period.

14. Some of these 31 original tweets still attach a link to a blog post or video, but in all cases, the tweet’s text was adapted to the platform.