Multilingual Analysis of Narrative Properties in Conspiracist vs Mainstream Telegram Channels (2025)

Abstract: Conspiracist narratives posit an omnipotent, evil group causing harm throughout domains. However, modern-day online conspiracism is often more erratic, consisting of loosely connected posts displaying a general anti-establishment attitude pervaded by negative emotions. We gather a dataset of 300 conspiracist and mainstream, Telegram channels in Italian and English and use the automatic extraction of entities and emotion detection to compare structural characteristics of both types of channels. We create a co-occurrence network of entities to analyze how the different types of channels introduce and use them across posts and topics. We find that conspiracist channels are characterized by anger. Moreover, co-occurrence networks of entities appearing in conspiracist channels are more dense. We theorize that this reflects a narrative structure where all actants are pushed into a single domain. Conspiracist channels disproportionately associate the most central group of entities with anger and fear. We do not find evidence that entities in conspiracist narratives occur across more topics. This could indicate an erratic type of online conspiracism where everything can be connected to everything and that is characterized by a high number of entities and high levels of anger.

## introduction although many studies focus on hate speech, fake news, and political and religious extremism in isolation, these are connected phenomena. indeed, false information is often used to spread hate against political enemies @xcite @xcite , and rumors are often based on underbelly feelings about minorities @xcite . this connection is exemplified by conspiracism: a belief in 'sinister, all-powerful conspiratorial forces' that are behind all bad things happening in the world @xcite . conspiracism is typical for extremist groups that use conspiracies as a justification for their beliefs @xcite . 1 social media and the internet provide a platform for all kinds of extremism, including conspiracism. algorithms favor content that is interesting and engaging, regardless of truth value or offensiveness. unfortunately, undesirable content is often particularly interesting and engaging @xcite . conspiracy theories, for example, are compelling due to their narrative structure, that creates a greatly simplified and satisfactory projection of a world that is in reality complex and erratic @xcite . conspiracist narratives tend to emphasize people and actants over events and actions @xcite , tying everything back to the one group of antagonists that is held responsible for everything bad in the world, combining actants and events from different domains in one overarching narrative @xcite . on the other hand, @xcite and @xcite identify a new kind of conspiracism, based more on flaming, attacks, and memes, and less on in-depth discussions of the underlying worldview. many studies addressing hate speech, misinformation, and conspiracy theories deal with these topics at the level of individual posts, considering them in isolation. this is a limitation, because social media users and channels that spread a political ideology or conspiracist narrative almost never repeat their whole worldview in a single post @xcite . to address this issue, this paper aims to analyze conspiracist content at the channel level, allowing us to compare the properties of the narra-tives pushed in conspiracist and mainstream channels. we focus our research on telegram, a social medium noted for its wide range of content @xcite . we use well-established nlp approaches to characterize different aspects related to entities, topics, and emotions. while some of these features have been analyzed in the past, we are, to the best of our knowledge, the first to combine them to characterize different types of telegram channels. moreover, we perform a multilingual analysis (in italian and english). this work is structured around the following research question: what are the narrative characteristics that are typical for conspiracist telegram channels compared to mainstream ones? we address this question by exploring i) the emotions associated with the different types of channels, ii) co-occurrence networks of named entities to infer structural properties of the way these are introduced and mentioned throughout each channel type and iii) the distribution of entities over topics in the text. we use the term 'narrative' to refer to 'the ways in which we construct disparate facts in our own worlds and weave them together cognitively in order to make sense of our reality' @xcite . rather than doing narrative extraction, we develop tools to characterize general tendencies of the narrative developed in each type of channel, such as network analysis to characterize the way a channel introduces entities, and emotion analysis to understand the affect associated with each type of channel and with specific entities in the text. we introduce language-independent tools for the automatic narrative analysis of social media discourse. the way we use network analysis for the comparison of different types of political content draws inspiration from the work done by for example @xcite al. (2020); bleakley (2023 ## discussion the classical view of conspiracism is that they are all-encompassing theories that tie back actants and events from different domains to an omnipotent group of evil conspirors @xcite . several datadriven studies have found evidence for this type of narrative structure @xcite @xcite . however, social media is also said to have brought about a more erratic, posttruth type of conspiracism, that is less about providing an alternative theory and more about emotion and anti-establishment sentiments (rosenblum and muirhead, 2019). @xcite found this type of conspiracism to be more typical of a social medium like reddit, which is driven by a more likebased system than for example traditional internet message boards. our analysis provided evidence that conspiracist data on telegram shows characteristics of both types of narratives. throughout all analyses we find higher variability for the mainstream category of channels. although our mainstream channels were much more varied, these differences tended to persist when we split out the mainstream channels in the three identified subcategories. this points towards a high level of uniformity among conspiracist telegram channels. we expected the conspiracist channels to be characterized by negative emotions, as found by @xcite ; @xcite and others. we did find that conspiracist channels had much higher levels of anger than mainstream channels and marginally more sadness, but mainstream channels expressed more fear. this difference was only significant for the political/news mainstream channels, but not for the economy/crypto or sciencerelated channels. this might be due to the topics covered by this channels, such as war and the covid-19 pandemic. we hypothesize that these topics inherently include words associated with fear and sadness, as also illustrated by our informative word analysis. we did not find a significant difference in the amount of joy expressed between the two types of channels. our findings thus do not clearly support the idea that conspiracist content is more negative than mainstream content, but it is in line with earlier research identifying anger as a characteristic of extremist data @xcite and the relationship between conspiracism and anger as a personality trait @xcite . analysis of the most informative words per emotion showed that words relating to trump, elections, protest and the police were associated with anger, whereas topics like war and pandemic were more associated with sadness and fear. more research is needed to disentangle cause and effect here, as these findings can either indicate that conspiracist channels disproportionately discuss topics associated with anger, or that the way conspiracist telegram channels communicate triggers the emotion 'anger' in our model, which would lead to an overrepresentation of these topics in our informative word analysis. we quantified the way the different types of channels combine entities by analyzing networks of entity co-occurrences. we found that the entity co-occurrence networks for conspiracist channels are slightly but significantly more dense than mainstream channels. we theorize this indicates the tendency of conspiracist discourse to create narrative cohesion by combining more different entities. we split our channel networks in communities using the louvain algorithm and identified 'top communities', i.e. the communities with the highest group degree centrality. these top communities were more transitive for the conspiracist category, but (unexpectedly) not more dense. we hypothesize that these communities indicate the 'conspirors' in the conspiracy narrative. this is in line with our finding that the entities associated with the top community do not deviate from the emotionality of the channel as a whole for mainstream channels, but for conspiracist channels these entities are disproportionately associated with anger and fear. when looking at the most typical entities per channel type, we found that mainstream channels tend to mention more states and established entities, whereas the conspiracist channels were typified by more miscellaneous entities. finally, we expected conspiracist narratives to use entities to 'stitch together' different types of domains into one overarching narrative @xcite @xcite . however, we did not find evidence for that; we did not find a greater overlap in entities between topics for conspiracy channels compared to mainstream channels. this is more in line with the erratic anti-scientific style of online conspiracism @xcite . on the other hand, it should be considered that our keyword matching method of extracting topics might not have been fine-grained enough. another option is that many of our conspiracist channels were rather mono-thematic, centering around the covid-19 pandemic, resulting in one domain characterized by a wide variety of entities. we recommend that future studies further explore the relationship between entities and topics in online conspiracist discourse. ## conclusion the goal of this paper was to analyze the narrative tendencies of conspiracist channels on telegram. we collected a dataset of 300 conspiracist and mainstream channels in english and italian, a pseudonymized version of which will be released upon request. we conducted emotion classification and named entity extraction. we extracted topics based on keyword matching and created cooccurrence networks of the entities in each channel. our results show a narrative network where everything is connected to anything, and a group of central actants associated with fear and anger, but no clear evidence of actants stitching together different domains. we theorize that these characteristics contribute to a narrative structure that is cohesive due to actants co-occurring in different constellations, and attractive due to the emotional nature of the discourse.