Conspiracy Narratives in the Protest Movement Against COVID -19 Restrictions in G ermany. A Long-term Content Analysis of Telegram Chat Groups. (2022)

Abstract: From the start of the COVID-19 pandemic in Germany, different groups have been protesting measures implemented by different government bodies in Germany to control the pandemic. It was widely claimed that many of the offline and online protests were driven by conspiracy narratives disseminated through groups and channels on the messenger app Telegram. We investigate this claim by measuring the frequency of conspiracy narratives in messages from open Telegram chat groups of the Querdenken movement, set up to organize protests against COVID-19 restrictions in Germany. We furthermore explore the content of these messages using topic modelling. To this end, we collected 822k text messages sent between April 2020 and May 2022 in 34 chat groups. By fine-tuning a Distilbert model, using self-annotated data, we find that 8.24% of the sent messages contain signs of conspiracy narratives. This number is not static, however, as the share of conspiracy messages grew while the overall number of messages shows a downward trend since its peak at the end of 2020. We further find a mix of known conspiracy narratives make up the topics in our topic model. Our findings suggest that the Querdenken movement is getting smaller over time, but its remaining members focus even more on conspiracy narratives.

## introduction conspiracy narratives already existed way before the rise of social networks or messenger services (see @xcite , but their spread was generally modest. in the last decade, however, there have been recurrent debates about the rise of conspiracy narratives in public and media discourse. two factors in particular are made responsible for this: first, social networks have allowed so-called alternative news media to emerge, exposing the visibility of the widespread existence of conspiracy narratives in society; and second, the covid-19 pandemic was a catalyst for misinformation, conspiracy narratives, and populist protest @xcite over the last two years. research in the past has shown that conspiracy narratives emerge more likely when people feel loss of control and uncertainty @xcite . it was, therefore, not surprising that conspiracy narratives began to circulate relatively quickly at the onset of the covid-19 pandemic. in germany, several demonstrations against measures of the government to control the covid-19 pandemic began to take place in the middle of 2020. in the context of this movement, criticism of government measures often merged with the belief that conspiratorial secret organizations ultimately determine the actions of governments during the pandemic. over time, the so-called querdenken (transl. to "lateral thinking") movement emerged as the main collective that organised many of the protests and connected groups scattered throughout germany. in particular, the stuttgart initiative querdenken 711 was a role model for many smaller initiatives in numerous regions of germany. at the movement's demonstrations, the prevalence of common conspiracy narratives could not be missed. as @xcite have suggested, the messenger service telegram played a major role in the mobilization and organization of the protests in germany. furthermore, @xcite suggest that the affordances of telegram as a platform with lenient content guidelines led to networks forming around more radical content and the spread of conspiracy narratives in dutch-language public telegram channels discussing developments in the covid-19 pandemic. in this short contribution, we analyze conspiracy narratives in telegram groups in the specific context of the querdenken movement using supervised and unsupervised machine learning approaches for a systematic automated content analysis. we attempt to focus on conspiracy narratives, following a relatively basic operationalization: conspiracy narratives are beliefs and convictions that attempt to interpret historical and contemporary events and general societal changes as a conspiracy and/or secret plan by a group of powerful actors @xcite . scholars have pointed out that the prevalence of conspiracy narratives could be one key indicator of radicalization @xcite , as it could act as "radicalization catalysts" @xcite . we, therefore, address important concerns for social cohesion with our two research questions: rq1: how prevalent are conspiracy narratives in telegram groups that set out to organize protest against covid-19 measures in germany over time? rq2: what kind of conspiracy narratives make up the discussion in these groups? additionally, we want to know how to automatically detect conspiracy narratives from a technical standpoint in order to pave the way for broader scope research on the topic. ## data we use data from querdenken telegram chat groups that are publicly viewable without joining the groups (see appendix a for selection process and list of groups). there are also info channels where only selected people can post, while in the open chat groups anyone who joins can post. to protect the privacy of message senders, we only use the time and text of a sent message. we use all public chat groups that are advertised on a page of the main initiative. ## methods the manually labeled data is used to train different supervised machine learning models. the best performing model is a fine-tuned distilbert model @xcite . to evaluate the performance of the models, we use 5-fold cross-validation. we finetune an already fine-tuned model for german toxic comment @xmath0, 2022). our model classifies the messages in a 2-way classification (message shows signs of conspiracy / does not show signs of conspiracy). the average macro f1-score for this model is 0.851 and therefore outperforms other experiments (e.g. svm, naive bayes). however, the svm had an f1-score of 0.69 for the class "signs of conspiracy" (compared to 0.76 for the best performing model) while being less computationally expensive. the best performing method, the fine-tuned distilbert model is trained on all annotated data to get the final model, which we use to automatically label the remaining 822k messages. to analyze trends in the data, we perform a frequency analysis. in addition, we analyze the topics of messages showing signs of conspiracy by using a structural topic model (stm). ## temporal analysis over a period of more than two years, users in the groups we analyzed sent an average of 1080 messages per day. the number of messages, and thus the activity of the groups, had its peak towards the end of 2020. since then and especially the mid of 2021, the participation has been on a downward trend and the groups of the querdenken movement were no longer active by the same degree. in april 2022, the monitored groups averaged around 457 messages per day. concerning the prevalence of conspiracy narratives (rq1), our trained model identified 67,698 messages containing characteristics of conspiracy narratives, representing 8.24% of the total corpus. over the two years, the average was 89 messages per day. with regard to the distribution of all messages in the corpus, the identified messages containing conspiracy narratives follow a similar trend. the prevalence of classified messages is highly correlated with the total message volume, and peaked at the end of 2020 and has been on a downward trend since then, although not quite as steep as the total message volume. however, we found an increasing uptrend in the proportion of messages containing conspiracy narratives to the whole corpus. a look at these numbers confirms this impression: the share of messages containing signs of conspiracy narratives is increasing over time and is still ongoing. in particular, a further increase has been noticeable since february 2022 peaking at values around 20%. ## topic model we chose an stm model with 10 topics after following the approach outlined by @xcite to decide on an optimal number of k (see appendix c for details). what we find is that most of the topics describe different categories of common conspiracy narratives (rq2). the most prevalent topics describe how the "altparteien" (old parties) would control the media to stay in power (t5), how the government and other elites would conceal how damaging the corona vaccine is and use allegedly fake pcrtests to convince people they are sick (t3), and that the vaccination campaign and mandatory vaccination laws are illegal and constitute crimes against humanity that are supposedly already fought in several court cases (t9). two topics tie in with a collection of larger global-scale conspiracies narratives like the "great reset" (t1) and narratives in which bill gates, barack obama, joe biden or the "deep state" secretly control the pandemic, the vaccine as well as other crises in the world (t6). interestingly, russia's war on ukraine is lumped in here and the us or the aforementioned actors are made responsible for it ð essentially repeating some of the claims spread by russian news. consequently, t6's prevalence increases massively, after the start of the invasion on 24 february 2022 ð which is the only noteworthy shift in prevalence for a topic over time (see details in appendix c). in the less prevalent topics we see narratives talking about the obligation of "awake" citizens to resist against the elites who try to use corona to control the "sleeping" mainstream public of germany (t4); how the measures against the pandemic would secretly constitute a power grab similar to the "ermächtigungsgesetz von 1933" (enabling act of 1933) (t10); and narratives surrounding the alleged negative and harmful impact of masks (t2). overall, we are able to directly link most of the topics to known conspiracy narratives. the two exceptions are t8 and t7 which inform about future protest events and advertise alternative news con-tent, often with a reference to censorship and how the content was already removed from youtube or archives of tv-stations, allegedly because it contains the truth. ## conclusion in this paper, we explored conspiracy narratives in german telegram chat groups in which people organize protest against restrictions introduced due to the covid-19 pandemic (i.e. the querdenken movement). using an automated machine learning approach, we were able to analyze 822k text messages sent in open chat groups. despite the decrease in overall activity in the telegram groups since late 2020, we found an upward trend in the relative share of messages containing conspiracy narratives. the topic model maps the different types of conspiracy narratives that we encountered in the dataset and that play a role in the group discussions. moreover, the fact that almost all themes can be clearly linked to a conspiracy narrative shows the robustness of our approach to automatically detect conspiracy narratives despite remaining uncertainty in the distilbert model. our analysis suggests that the remaining core of people in the querdenken telegram groups is increasingly immersed in conspiracy narratives, which appear to become the ideological reference point of the movement after many of the measures implemented to control the pandemic in germany have been lifted. this might be a meaningful issue considering that beliefs in conspiracy narratives are a key element of radicalization dynamics @xcite . moreover, because the affinity for conspiracy narratives, or the individual "conspiracy mentality", as social psychologists @xcite refer to it, could lead the remaining core of the movement to shift to other topics, which are suitable for conspiracy ideological mobilization. we observe, for example, that much news regarding the russian invasion in ukraine are made sense of in the groups by falling back on previously common narratives of international cabals, predominantly from the us, who allegedly control crises in the world for their own gains. in the future, this increasing detachment from reality could bring with it the potential for further disintegration of social cohesion in germany. we acknowledge the limitation that our study excluded most protest groups from east germany, as some of these do not operate under the label of the querdenken movement, even if they share some of the same goals and ideologies.