Who leads? Who follows? Temporal dynamics of political dogwhistles in S wedish online communities (2025)

Abstract: A dogwhistle is a communicative act intended to broadcast a message only understood by a select in-group while going unnoticed by others (out-group). We illustrate that political dogwhistle behavior in a more radical community precedes the occurrence of the dogwhistles in a less radical community, but the reverse does not hold. We study two Swedish online communities – Flashback and Familjeliv – which both contain discussions of life and society, with the former having a stronger anti-immigrant subtext. Expressions associated with dogwhistles are substantially more frequent in Flashback than in Familjeliv. We analyze the time series of changes in intensity of three dogwhistle expressions (DWEs), i.e., the strength of association of a DWE and its in-group meaning modeled by Swedish Sentence-BERT, and model the dynamic temporal relationship of intensity in the two communities for the three DWEs using Vector Autoregression (VAR). We show that changes in intensity in Familjeliv are explained by the changes of intensity observed at previous lags in Flashback but not the other way around. This suggests a direction of travel for dogwhistles associated with radical ideologies to less radical contexts.

## introduction political dogwhistles are "speech acts that explicitly convey a certain content to an audience, while simultaneously sending a different, concealed message to a specific subset of that audience" @xcite . since dogwhistles enable communication of controversial views to sympathizers while not alienating a wider audience, dogwhistles are an efficient strategy in political communication to mobilize support, such as votes @xcite @xcite @xcite . dogwhistles thus pose a problem for democracy by obscuring electoral mandates @xcite . additionally, dogwhistles are used online by citizens @xcite . since dogwhistles are used for concealed expressions of intolerant discourse @xcite while evading accountability, they align with "dark" participation online @xcite , such as hate speech and disinformation @xcite . there is a growing interest in applying computational methods to such behaviors (e.g. @xcite @xcite . since the words used in a dogwhistle have a "conventional" out-group meaning, vector-space measures of meaning change can be used to determine when it is more likely that a dogwhistle is being used with an in-group meaning. recognizing the temporal nature of dogwhistles @xcite , recent studies have modeled the meaning change in dogwhistle expressions (dwes) over time. they showed that the rate of change of dwes diverge between communities @xcite and that the variability of dogwhistle intensity, i.e., the strength of association between dwes and their in-group meaning, is predictable from the general patterns of semantic change of these expressions @xcite . previous studies of dogwhistle behavior as a form of semantic change use yearly time series data, which is of limited length. with finer granularity, the non-deterministic nature of dogwhistle behavior becomes apparent. that is, while dwes online often show a trending behavior (both in terms of frequency and intensity), there is a fluctuating pattern where values rise and fall cyclically relative to a baseline (see figure 1 ). such a pattern can be modeled as a function of the external forces that cause the series to vary with other temporal features, such as auto-regression @xcite . little is currently known of what explains such patterns of dogwhistle behavior online. our aim is to explore one factor in explaining variation of dogwhistle behavior online, namely how past changes in one community could explain present changes in dogwhistle behavior in another community. we study the communities of two discussion forums: flashback and familjeliv (family life). the main content of these is general discussion about life and society, but flashback, unlike familjeliv, has a strong anti-immigrant subtext @xcite @xcite . thus, flashback is expected to have a larger proportion of anti-immigrant in-group members than familjeliv. unsurprisingly, dwes are more frequent in flashback than in familjeliv, see accordingly, we test the following hypotheses: we find partial support for these. our findings confirm previous work on the mainstreaming of hate speech online that shows that radicalization of far-right ideas is mediated by semi-radical settings such as flashback @xcite @xcite ). ## related work the use of dogwhistles online can be seen as a sub-process of the radicalization of mainstream media @xcite . the features of the internet have provided subversive social movements with tools to normalize hate speech @xcite . by mimicking legitimate sources of information like news media, extremist movements have succeeded in legitimizing their causes @xcite . in this mainstreaming of hate, the role of "gateways" has been explored @xcite . radicalization evolves through "pipelines" @xcite of interactions in increasingly extreme communities @xcite . in her detailed, two-decade case study of the dogwhistle "cultural enricher" across the swedishspeaking internet, @xcite showed that flashback served as a gateway to mainstream radicalization, by mediating far-right discourse. framing content in neutral ways @xcite and concealing racist sentiment are key strategies to inject extremist ideas into less radical contexts. although still limited, there is growing interest in computational methods for studying dogwhistles, including formal semantics @xcite @xcite and data annotation @xcite . using data from a replacement test in which subjects were instructed to replace dwes in sentence contexts with what they thought they meant, @xcite showed that the support vector machine (svm) classifier could reliably separate "in-group" and "out-group" interpretations of the terms, based on their sentence embeddings @xcite . @xcite tested the ability of the large language model (llm) gpt-3 @xcite to identify in-group meanings of dogwhistles, under various conditions. similarly, @xcite tested the ability of several contemporary llms to identify and define dogwhistles, showing high accuracy for all the models. by applying computational methods of lexical semantic change (lsc) detection @xcite , @xcite found that the rate of change of dwes in the two different online discussion diverged, thus showing that dogwhistle evolution is community dependent (cf. @xcite . @xcite showed that general measures of lsc @xcite predicted the semantic change of the in-group relative to the out-group meaning. methodologically related to our study is the work that uses time series models to explain patterns of agenda-setting, i.e., the process of change in political priorities @xcite and media @xcite . using social media data, @xcite explored who is more likely to lead or follow in discussions on public issues via a vector autoregressive (var) model; they found that politicians tend to follow the priorities of the public. ## data we study the temporal dynamics of dogwhistle meaning using two data sources: (1) a survey of swedish citizens asked to perform a word replacement task for dwe-containing sentences (section 3.1) and (2) corpus data of the posts of the online discussion forums flashback and familjeliv (section 3.2). @xcite have provided data from a word replacement task that quantified variability in how individuals understand the meaning of dogwhistles. swedish citizens (@xmath0, pre-stratified in terms of age, gender, and education) were instructed to read sentences and replace a potential dwe in each sentence with one or more words so that the meaning of the sentence remained intact. potential dogwhistle words were collected from swedish media, of which twelve were identified and included in the survey. ## semantic modeling we consider a dwe to be more "intense" at a given moment if its observed context of use is more similar to the non-dogwhistle terms that represent the same in-group meaning (e.g., "re-migration" vs. "deportation"). we define the intensity of a dwe w at time period t as the similarity of the contextual embedding of w to the embeddings of the in-group replacements from the word replacement task (i w ; section 3.1). 2 the contextual embedding is taken from the contexts of w found during t in the corpus. i w is converted to a single vector representation by taking the mean of the replacement task responses. we use sentence embeddings from sentence-bert (sbert) @xcite . sbert is bert @xcite fine-tuned for predicting the semantic similarity of two sentences. sbert has a bi-encoder architecture to reduce the computational cost of sentence pair-regression in original bert. @xcite show that a bi-encoder with fine-tuning reaches state-of-the-art performance on sentence similarity. swedish sbert (rekathati, 2021) is trained with transfer learning following the procedure by @xcite , where the objective is to make a student model @xcite foot_2 (of an under-resourced language, here: swedish) match the sentence embeddings of a highperforming teacher model foot_3 (developed for a wellresourced language, here: english) in a parallel corpus. the mean vector of the sentence embeddings for w at t constitutes -→ w t . the similarity of -→ w t and -→ i w is measured by the angular similarity @xcite . angular distance is the normalized angle between two vectors, which can be calculated from the cosine similarity of the two vectors, see equation 1. angular similarity is the complement of the angular distance. accordingly, intensity of a dwe w at t is defined as follows: unlike cosine similarity (sim), which ranges from -1 to 1, angular similarity ranges from 0 to 1, where values close to 1 indicate strong similarity and values close to 0 indicate strong dissimilarity. see appendix a.1 for examples with high and low intensity calculated for individual cases. ## time series modeling the dynamic relationship between the intensity of a dogwhistle in the two communities is modeled by vector autoregression (var). var is multivariate generalization of the univariate autoregressive (ar) model for time series analysis. like ar models, var models estimate how the present value of an included variable, x t , depending on previous values of the variable, x t-1 , ..., x t-p , where p is the order of the model, i.e., the lag. moreover, for each variable included, var models capture its relationship to the lagged values of other variables in the model. by modeling the relationship between the k variables in the model, a var model can be described as a system of equations @xcite , where the equation for each variable includes the variable's own lagged values, the lagged values of the other variables, and an error term. formally this can be defined as: ## results in total we estimate six models: two types of imputation for the three terms (but note that for berika the two versions are basically the same since there are only two missing values). we here present the results for the mean imputed data in more detail, but refer to appendix a.2 for details on the locfimputed data. in general, the results of the two methods implies the same conclusions, but there are some differences related to globalist, which are mentioned below. given the complexity of many var models, the model coefficients are seldom communicated in studies. however, in our case, an overview of the model is possible ( second, in support of h2, previous lags of changes in intensity in familjeliv do not significantly predict changes in intensity in flashback. this is observed for both the mean and the locfimputed data ( a third observation is that in flashback and familjeliv, there is a strong autocorrelation process, i.e. the present changes of intensity are significantly explained by previous changes of the same variable (again, this is observed for both the mean and the locf-imputed data). ## discussion currently, there is only limited work that attempts to explain the changing patterns of dogwhistle behavior online @xcite . our results suggest that changes in dogwhistle behavior in communities with strong sympathies for the meaning encoded by the in-group meanings (in our case, racial bias and anti-immigration sentiment) predict the same in other less radical communities. more precisely, we find, first, that the intensity in familjeliv can be predicted from the intensity three to nine months earlier in flashback. as shown in the irfs (figure 2 ), a change in intensity in flashback is followed by a response in familjeliv in the following months. in the case of berika and återvandring, the test for granger causality further confirms that changes in dogwhistle behavior in flashback explain changes in dogwhistling in familjeliv. second, the opposite relationship does not hold. changes in intensity in flashback cannot be predicted from changes of intensity in familjeliv. these observations support h1 and h2, although in the case of globalist we find only partial support. flashback leads dogwhistle behavior online, while familjeliv follows. this finding corroborates those in @xcite . more generally, our findings support a process of mainstreaming farright discourse @xcite @xcite . media such as flashback may be acting as a gateway for adoption of anti-immigrant dogwhistling in the more mainstream communities such as familjeliv. the imputation of data used for återvandring and globalist is a concern for the robustness of the findings. however, the dynamics observed for berika, where there are only two zero-frequency quarters, supports our hypotheses. although caution is required in interpreting the results for återvandring and globalist (due to imputation), they mostly point in the same direction as the more robust findings of berika. one possible explanation for the difference between globalist and the other dogwhistles is that it is already associated with a long-standing anti-semitic conspiracy theory associated with the financier george soros @xcite . this is particularly prominent in online media. members of familjeliv may have already started to ac-tively avoid it because of an association with anti-semitism and conspiracy theory. @xcite picked it up as a dogwhistle because their population sample may include many members of the public less exposed to online political debate. while we interpret the above findings in general framework of mainstreaming far-right discourse, much remains to be known about the underlying generative nature of this process. in the following, we raise two questions for discussion. a first question is what explains the pattern of intensity in the more influential community, i.e. flashback. in flashback, temporal changes of dogwhistle usage are likely to be responses to prominent events, such as, elections, refugee flows, and the salience of political issues in mainstream media. moreover, flashback is part of a larger context of the dissemination of far-right content online @xcite , where it serves as a gateway between more radical racist forums and the mainstream media @xcite . thus, the salience of political events and issues can in turn be further amplified by "spin-doctors" @xcite and intentional manipulation of digital discourses @xcite . moreover, internal dynamics of flashback in terms of influential users is likely to drive the observed dogwhistle behavior @xcite . the role of these factors (e.g., influential users, infiltration from extremist social movements, political events and the agenda setting of mainstream news media) and their interactions in explaining fluctuations of dogwhistle behavior in flashback could be explored in future research. another question concerns the relationship between leaders and followers. what is the mechanism of diffusion? previous work has found that radicalization of far-right ideas online occurs by internet users participating in incrementally more radical communities @xcite . for example, members of the so-called alt-lite movement consistently migrated to communities with more extreme views @xcite . participants of anti-feminist communities are likely to become members of far-right communities @xcite . by observing and learning communication practices in a more radical community (flashback), those behaviors can "brought back" to the origin community (familjeliv). however, these processes are difficult to study directly. although tracking user movement across communities within a platform is possible (but far from trivial), tracking user migrations between platforms (such as between familjeliv and flashback) will not be reliable due to pseudonymity.