The Russian invasion of Ukraine selectively depolarized the Finnish NATO discussion on Twitter

Abstract

It is often thought that an external threat increases the internal cohesion of a nation, and thus decreases polarization. We examine this proposition by analyzing NATO discussion dynamics on Finnish social media following the Russian invasion of Ukraine in February 2022. In Finland, public opinion on joining the North Atlantic Treaty Organization (NATO) had long been polarized along the left-right partisan axis, but the invasion led to a rapid convergence of opinion toward joining NATO. We investigate whether and how this depolarization took place among polarized actors on Finnish Twitter. By analyzing retweet patterns, we find three separate user groups before the invasion: a pro-NATO, a left-wing anti-NATO, and a conspiracy-charged anti-NATO group. After the invasion, the left-wing anti-NATO group members broke out of their retweeting bubble and connected with the pro-NATO group despite their difference in partisanship, while the conspiracy-charged anti-NATO group mostly remained a separate cluster. Our content analysis reveals that the left-wing anti-NATO group and the pro-NATO group were bridged by a shared condemnation of Russia’s actions and shared democratic norms, while the other anti-NATO group, mainly built around conspiracy theories and disinformation, consistently demonstrated a clear anti-NATO attitude. We show that an external threat can bridge partisan divides in issues linked to the threat, but bubbles upheld by conspiracy theories and disinformation may persist even under dramatic external threats.

1 Introduction

Despite a period of momentum building, the Russian invasion of Ukraine on Feb 24, 2022 came as a shock to most observers. The shock was most acute in Ukraine but was felt also in countries bordering Russia. Finland, the militarily non-aligned European country that shares a 1344-kilometer border with Russia, witnessed a sharp shift in its public opinion on NATO membership, based on a reappraisal of the external threat posed by Russia. Traditionally, around 20 percent of the Finnish population had been in favor of joining NATO [1]. Russia’s invasion of Crimea in 2014 increased the number to 25–30 [1], but after the invasion of Ukraine in 2022, support for joining NATO soared as high as 70–80 percent [2].

Behind this major change in opinion, the rising external threat seems to have had a depolarizing effect on the Finnish NATO discussion. For long, Finnish opinions on NATO embodied a polarization that was largely partisanship-based: voters of the main right-wing party (National Coalition) were largely in favor of joining, whereas voters of left-wing parties were the most vocal opponents of NATO [3]. After the invasion, however, many left-wing supporters changed their opinion, and eventually the Finnish parliament almost unanimously voted in favor of joining NATO (188 for, 8 against).

Social media opens an unobtrusive observation window [4] into whether and how this depolarization took place among the more politically active and partisan segment of the population [5, 6], including political elites who often play an important role in steering the discussion [7, 8], as well as fringe communities that subscribe to conspiracy theories and disinformation [34] and diffusion networks [33]. Bessi et al. [35] showed that conspiracy news consumers are more focused on diffusing within-group content and interacting with within-group actors, which points to the potential stability of conspiracy-based polarization. Zollo et al. [50] further added that users within the conspiracy echo chamber rarely interact with debunking posts, and when they do, their interest in conspiracy content actually increases after the interaction. Echoing these findings, our results more concretely demonstrate the resilience of conspiracy-/disinformation-based polarization. We show that consumers of conspiracy theories and disinformation formed a separate retweeting bubble, and further, that they were reluctant to change their opinions or communication patterns even in the face of a dramatic external threat and otherwise bridged partisan divides. This alerts us to the fact that conspiracy theories and disinformation are consumed by polarized actors that are even more entrenched than partisan actors, and that it can be extremely difficult to pave a way toward conversation and consensus with them.

Appendix

1.1 A.1 Data collection keywords

Two of the authors who are experts on Finnish politics developed a list of keywords related to the Finnish NATO discussion. We here present the original keywords in Finnish along with their rough translations into English (many of the words are context specific): liittoutua (to ally), liittoutumaton (non-aligned), liittoutumattomana (as non-aligned), liittoutumattomuuden (non-alignment), liittoutumattomuus (non-alignment), liittoutuminen (allying), liittoutumisen (allying), nato (NATO), nato-kumppani (NATO partner), nato-kumppanien (NATO partners’), nato-kumppanit (NATO partners), nato-kumppanuus (NATO partnership), nato-yhteistyö (NATO cooperation), nato-yhteistyön (of NATO cooperation), nato-yhteistyössä (in NATO cooperation), nato-yhteistyötä (NATO cooperation), naton (of NATO), natoon (into NATO), natossa (in NATO), natosta (from NATO), puolustusliiton (defense alliance’s), puolustusliitosta (from the defense alliance), puolustusliitto (defense alliance), puolustusliittoon (into the defense alliance), sotilasliiton (military alliance’s), sotilasliitosta (from the military alliance), sotilasliitto (military alliance), sotilasliittoon (into the military alliance), suominatoon (Finland into NATO), natojäsenyyttä (NATO membership), natojäsenyyden (NATO membership’s), nato-trolli (NATO troll), nato-trollit (NATO trolls), nato-trollien (NATO trolls’), nato-trollaajat (NATO troll users), nato-trollaajien (NATO troll users’), nato-kiima (NATO heat), nato-kiiman (NATO heat’s), nato-kiimailijat (NATO enthusiasts), nato-kiimailijoiden (NATO enthusiasts’), natoteatteri (NATO theater), natoteatteria (NATO theater), and natoteatterista (from the NATO theater).

1.2 A.2 Tweet sampling statistics

For each group and each period, we sampled 42 tweets from those that got retweeted at least once in the group in the period. In total, 1800/221/416 tweets in the before period, 4188/343/1118 tweets in the right-after period, 2698/257/779 tweets in the 1-week-after period, and 1022/88/481 tweets in the 4-weeks-after period got retweeted at least once in respectively the pro, left-anti, and conspiracy-anti group.

1.3 A.3 Extra retweet network plots

Retweet networks in the 1-week-after and 4-weeks-after periods are plotted in Fig. 2.

Figure 2

Retweet networks 1 week after and 4 weeks after the invasion. Retweet network (A) 1 week after and (B) 4 weeks after the Russian invasion of Ukraine. Networks are drawn using the SFDP spring-block layout [43]. Node colors correspond to the three groups detected in the before network, and the statistics beside each network show the number of users, the number of external retweets of the pro group, the number of internal retweets, and the E/I ratio in each anti group

1.4 A.4 Statistical test of network structure change

Due to the varying user group size in the retweet networks across different time periods, the observed change in E/I ratio can potentially be explained by statistical fluctuations. Here, we conduct a statistical test to see if the observed E/I ratio change after the invasion is higher in the left-anti group than in the conspiracy-anti group despite statistical fluctuations.

We suppose the retweets by each anti group are generated by a hypothetical model where each retweet is an external retweet of the pro group with probability $p_{E}$, or an internal retweet with probability $1-p_{E}$. We assume the uniform $\operatorname{Beta}(1,1)$ prior on $p_{E}$, which leads to the posterior distribution of $p_{E}\sim \operatorname{Beta}(1+n_{E},1+n_{I})$, where $n_{E}$ is the observed number of external retweets, and $n_{I}$ is the observed number of internal retweets in a certain period. For respectively the before period and the right-after period, we calculate the posterior distribution of $p_{E}$ in respectively the left-anti group and the conspiracy-anti group. For example in the left-anti group, $p_{E}\sim \operatorname{Beta}(1+41,1+468)$ in the before period, and $p_{E}\sim \operatorname{Beta}(1+166,1+193)$ in the right-after period; in the conspiracy-anti group, $p_{E}\sim \operatorname{Beta}(1+96,1+1216)$ in the before period, and $p_{E}\sim \operatorname{Beta}(1+389,1+1946)$ in the right-after period.

We run 100,000 rounds of simulations. In each round, for respectively the before period and the right-after period, we sample $\hat{p}_{E}^{L}$ from the posterior distribution of $p_{E}$ in the left-anti group, and $\hat{p}_{E}^{C}$ from the posterior distribution of $p_{E}$ in the conspiracy-anti group. We then numerically calculate the expected E/I ratio in the left-anti group $R^{L}$ (resp. in the conspiracy-anti group $R^{C}$) based on the sampled $\hat{p}_{E}^{L}$ (resp. $\hat{p}_{E}^{C}$). Then we obtain the E/I ratio change induced by the invasion in the left-anti group $Q_{R}^{L}=R_{\mathrm{after}}^{L}/R_{\mathrm{before}}^{L}$ (resp. in the conspiracy-anti group $Q_{R}^{C}=R_{\mathrm{after}}^{C}/R_{\mathrm{before}}^{C}$). Finally, we obtain distributions of $Q_{R}^{L}$ and $Q_{R}^{C}$ over 100,000 simulations. We conduct a similar analysis also for the E/I ratio change in the 1-week-after period (as compared with the before period) and in the 4-weeks-after period (as compared with the before period).

As shown in Fig. 3 and Table 2, there is a certain range of variance in E/I ratio change that can be explained by statistical fluctuations, and the variance increases in later periods as the group size decreases. However, despite statistical fluctuations, the E/I ratio change induced by the invasion is still consistently higher in the left-anti group than in the conspiracy-anti group.

Figure 3

Statistical test results: distributions. Distribution of simulated E/I ratio change in respectively the left-anti group ($Q_{R}^{L}$) and the conspiracy-anti group ($Q_{R}^{C}$) in the (A) right-after, (B) 1-week-after, and (C) 4-weeks-after periods, over 100,000 simulations

Table 2 Statistical test results: distribution statistics. Mean and standard deviation of distributions of simulated E/I ratio changes induced by the invasion in respectively the left-anti group ($Q_{R}^{L}$) and the conspiracy-anti group ($Q_{R}^{C}$), over 100,000 simulations

1.5 A.5 Tweets with unclear stance

In our tweet stance coding, a tweet is labeled “unclear” if it does not explicitly express a positive or negative attitude toward NATO. Thus, in general, the label “unclear” does not necessarily imply an ambiguous attitude toward NATO, but rather that the tweet does not clearly indicate any attitude. For example, tweets labeled “unclear” can be reactions to what was currently taking place in the Ukraine war (while NATO was also mentioned) or in the Finnish NATO policy process.

More specifically in the pro-NATO group, many tweets were labeled “pro” in the earlier periods because they were advocating for two citizen initiatives that were pro-NATO; but later on, these initiatives became irrelevant because the needed signatures were collected, and the NATO policy process moved on. Thus in later periods, many clearly pro-NATO tweets disappeared from the pro-NATO group and, for example, many tweets condemning Russia’s actions in Ukraine took their place. The latter are often labeled “unclear” as they are less clearly in favor of NATO, even though such a stance might be implicit. In general, the increase of tweets with unclear stance does not suggest that the group moved toward an ambiguous stance on NATO.