The online hostility hypothesis: representations of Muslims in online media

ABSTRACT

Using a large data set of online media content in eight European countries, this paper broadens the empirical investigation of the online hostility hypothesis, which posits that interactions on social sites such as blogs and forums contain more hostile expressions toward minority groups than social interactions offline or in editorial news media. Overall, our results are consistent with the online hostility hypothesis when comparing news media content with social sites, but we find that negatively charged representations are common in both media types. It is instead the amount of attention to Muslims and Islam on social sites that most clearly differs and is the main driver of online hostility in the online media environment more broadly conceived.

KEYWORDS:

• Online hostility
• anti-muslims
• distributional semantics
• word embeddings

Introduction

Representations of Muslims and Islam in Western news media have long been recognized for their remarkably negative tone (Ahmed & Matthes, 2017; Bleich & van der Veen, 2022; Dixon & Williams, 2015). In the digital age, with the proliferation of online media, concerns arise about the potential exacerbation of negative depictions, particularly on platforms that lack traditional journalistic filters and editorial control. The online environment, with its relative anonymity and absence of face-to-face communication (Brown, 2018; Duffy et al., 2005), could give rise to even more negative or hostile portrayals of minority groups. While this phenomenon has been empirically supported in case studies of specific online forums and blogs (e.g., Miller, 2017; Törnberg & Törnberg, 2016), there remains a dearth of research on representations of minority groups in the broader online landscape beyond platforms known for their hostility.

This study addresses these knowledge gaps by delving into the representations of Muslims and Islam across a vast corpus of online data encompassing eight European countries where Muslims are a significant and growing minority. Existing studies have primarily focused on specific contexts or the anglosphere, leaving a considerable void in cross-national coverage analysis (Bleich & van der Veen, 2022, p. 9). Our research seeks to bridge this gap by examining online hostility toward Muslims in established European democracies.

Conceptually, we emphasize a crucial distinction in the forms and prevalence of negativity in the representation of Muslims when exploring the online hostility hypothesis. We posit that the online hostility hypothesis can manifest through either or both of two empirical patterns. First, it could be reflected in more hostile forms of representation on social sites. Second, there may be a disparity in the amount of attention paid to Muslims on social sites compared to news media. If negativity is pervasive in both realms, differences in attention levels may influence the perception of a more hostile media environment on social sites. Hence, we pose two key questions: (1) Are representations of Muslims and Islam more hostile on social sites? (2) To what extent does content mentioning Muslims or Islam occur more frequently on social sites compared to news media?

In this study, we analyze a big data corpus comprising 41 billion words from various sources, including editorial news media and user-generated content on social sites such as blogs and forums. The dataset covers eight strategically selected European democracies where Muslims play an important role and are confronted with a growing presence of radical right populist parties: Norway, Sweden, Denmark, the Netherlands, Germany, France, Spain, and the UK.

Methodologically, we employ recent advances in computational linguistics and applied machine learning, more specifically, word embedding models. This approach allows us to move beyond the identification of explicit forms of hostile expressions and delve into more implicit manifestations of hostility (Bolukbasi et al., 2016; Garg et al., 2018). Unlike studies that rely on word co-occurrence counts, our approach enables us to comprehend the embedded meaning of words based on their semantic context within the corpus. Furthermore, it overcomes language barriers, allowing for cross-lingual comparisons and offering a broader understanding of media coverage of Muslims and Islam across different European languages.

In conclusion, this study aims to contribute substantially to the understanding of representations of Muslims and Islam in the ever-evolving digital media landscape. By examining both the prevalence of negativity and the attention Muslim communities receive in the online sphere, we aim to shed light on the role of online media in shaping public discourse and perceptions across European democracies. As online communication continues to shape societal attitudes and behaviors, insights from this research have critical implications for promoting inclusivity, countering stereotypes, and fostering constructive dialogue among diverse communities.

The online hostility hypothesis

Online media have brought significant changes to the way we create, distribute, and consume information. While traditional news media still play a central role in these processes, newer forms of online media such as blogs, forums and other user-generated content platforms have emerged without any editorial function or content moderation. Consequently, curbing online harassment, hostility, hate speech, and derogatory speech has become increasingly challenging (Sandberg & Segesten, 2022). In this environment, hostile representations have become a growing concern, especially with regards to ethnic or religious minority groups, as they are frequently targeted and marginalized in many online environments (Bilewicz & Soral, 2020; Soral et al., 2020).

Scholarly work has highlighted how the tone and style of online interactions often tend to be harsher, more hostile, and less civil than in other settings (Sandberg & Segesten, 2022; Sydnor, 2018). A key point of divergence within the research field is whether this pattern is primarily driven by an increase in hostile behaviors and the intensity of such expressions or if they simply become more observable and traceable. Proponents of the causality thesis argue that the absence of mass media gatekeeping processes and control over information makes it easier to express hostility toward out-groups. Less risk of legal and social penalties can in addition form an environment where the social repercussions of such speech are less severe, i.e., the social pressure to control prejudice weaken (Walker et al., 2015). The abundance of information available online and the resulting struggle for attention, could also drive content to become more extreme (Brady et al., 2017).

Online hostility is a concept covering a wide range of adverse expressions, including incivility, intolerance, cyberbullying, hate speech, trolling, and flaming (Sandberg & Segesten, 2022). These subcategories, in turn, vary greatly in their usage. For instance, flaming, a term that was popular in the past (Jane, 2015), is now rarely used, whereas incivility has become a more commonly used term, albeit one without an agreed-upon definition. Some have defined incivility loosely as ‘the set of behaviors that threaten democracy, deny people their personal freedoms, and stereotype social groups’ (Papacharissi, 2004, p. 267). Others, such as Rossini (2022), make a distinction between incivility and intolerance, viewing intolerance as an expression of negative stereotypes of groups based on race, sex, gender, or religion. Online hostility, in contrast, is a broader concept that encompasses a range of various negative expressions.

Overall, the drivers of hostility in the online public sphere is still a live research question. Conventional wisdom has it that anonymity and lack of face-to-face interactions make users less prone to act in accordance with social norms. Online interactions might be perceived as angrier and less civil than exchanges in offline settings due to a psychological mismatch between the impersonal online environment and human adaptations to face-to-face interaction (Sydnor, 2018). Recent studies, however, have questioned the alleged role of anonymity as drivers of online hostility (Bor & Petersen, 2022: Rossini, 2022). They instead argue that the ‘hostility gap’ can be explained by the public nature of online discussions and their increased visibility, which expose people to more hostile attacks directed against strangers than otherwise would be noticeable to them (Bor & Petersen, 2022). The motivation for sharing hostile content has further been associated with discontent rather than a genuine belief in the content, which has been referred to as a ‘need for chaos’ (Petersen et al., 2018). Thus, sharing hostile content about Muslims might be more strongly motivated by political and strategic considerations and less of a reflection of sincere beliefs. To sum up, the causes of online hostility are likely multiple, and we currently do not know exactly which factors weigh more heavily on the observed patterns. However, they jointly lead to a strong expectation of increased hostility in social forms of online media.

Representations of Muslims and Islam

The media plays an essential role in shaping the public sphere and public perceptions of minority groups, as it determines both visibility in the media, or lack thereof, as well as how religious or ethnic minority groups are portrayed in the coverage. In societies where the majority population has limited contact with Muslims, it is problematic, from a democratic representation perspective, when Muslims are persistently portrayed in a negative light or associated with threats and security risks. Such portrayals are likely to influence how this group is viewed by society at large, as well as how the group perceives its own place within that society (Bleich & van der Veen, 2022). One-sided negative reporting can therefore have a detrimental impact on the social cohesion of the wider community, particularly when it leads to stigmatization, discrimination, or prejudice toward Muslims (e.g., Saleem & Ramasubramanian, 2019; Saleem et al., 2017). In the first truly large-scale analysis of newspaper coverage of Muslims conducted recently, Bleich and van der Veen (2022) examine the tone of coverage in well-established traditional news media, mainly in the U.S. They found that coverage of Muslims is remarkably negative in tone by any measure and much more negative than the coverage of other groups. This pattern was consistent over time, with certain predictable changes occurring after the 9/11 terrorist attack in 2001. Even if the national newspapers they examine, such as the New York Times and the Washington post, may not set out to stigmatize Muslims, it is nevertheless the case that the tone in their aggregated coverage of Muslims stands out as especially negative (Bleich & van der Veen, 2022).

All the consequences of this negatively charged coverage are not known. But we do know that negative news has a disproportionate influence on cognition and attitudes (Kroon et al., 2021; Soroka & McAdams, 2015; Soroka et al., 2019). Thus, media coverage matters: ‘What the media communicate both reflects and reinforces perceptions of Muslims that shape social boundaries in our societies’ (Bleich & van der Veen, 2022, p. 4).

It has been suggested that more distinctive, group-specific constructs should be considered when studying the dynamics of prejudice toward Muslims as a minority group in Western countries where Muslims are perceived to represent a challenge to the modern Christian world (Oskooii et al., 2021). In that vein, Western societies view Muslims not only as a religious minority but also as a cultural minority (Kalkan et al., 2009). Representations linked to social identity in addition to religious traits could therefore be particularly relevant for Muslims, not least in relation to being perceived as a threat to the social identity of the majority population. At the same time, it is important to highlight that prejudiced views of Muslims should not be confounded with a legitimate critique of Muslim practices based on secular grounds (Imhoff & Recker, 2012; Tartaglia et al., 2019). When examining representations of Muslims, negativity could stem from either secular critique of Islam or prejudices building on negative stereotypes, however, jointly they might lead to essentially negative representations for a vulnerable group in society.

In the coverage of Muslims, patterns of representations that are not intentionally negative or explicitly hostile, are of importance. Of interest, therefore, is examining also more broadly the nature of representations of Muslims in large media corpora. Next, we turn our focus to how these representations might take form outside the journalistic newsroom, and in the broader online environment.

Online social sites

Social sites such as the blogosphere, social news, forums, and message boards represent a highly diverse and heterogeneous environment online. Previous studies have mainly been case studies focused on specific sites and forums where hostile content is expected. This limited scope has therefore left us uncertain about the extent and nature of depictions of Muslims in the broader online environment. Hostile expressions, in general, have been observed to gain traction on social sites. Studies of the portrayal of Muslims and Islam on specific sites have shown that discussions often revolve around the perceived political threat from Islam, with Muslims being depicted as an outgroup embroiled in conflict, violence, and extremism (Awan, 2014; Miller, 2017; Törnberg & Törnberg, 2016). Additionally, Islam is frequently portrayed as a religion oppressing women and homosexuals, and more extreme associations in social forums have been made between Islam, pedophilia, and child marriages (Törnberg & Törnberg, 2016). Taken together, these studies reveal a ‘toxic’ online environment where hostility toward Muslims is prevalent and reinforced. Hostility toward Muslims in online discussions also tend to increase after Islamist terror attacks (Álvarez-Benjumea & Winter, 2020), indicating the responsive nature of online discussions to real-time events. In such instances, particularly pernicious negative stereotypes about Muslims as potential terrorists and violent extremists are reinforced (Sides & Gross, 2013).

Although limited in their scope, the existing studies on individual social sites, suggest a widespread negative bias. We can therefore formulate a general expectation that negative outgroup stereotypes pertaining to Muslims at the very least are reproduced on social sites but that we might also expect to see a potentially stronger negative bias or other types of negative portrayals than in news media as these two media types naturally are used for different kinds of communication.

Turning our focus to a potential attention gap that together with the prevalence of negativity would support the online hostility hypotheses, we now consider how social sites online can be expected to drive attention to Muslims in Western countries. Previous research suggests that internet forums ‘serve as an online amplifier that reflects and reinforces existing discourses in traditional media’ of Muslims and Islam (Törnberg & Törnberg, 2016, p. 132). Hate speech directed at Muslims and anti-Muslim sentiment are not only explained by users’ motivations but also by a platform’s policy and the specific opportunities that arise for these types of expressions on a platform (Ben-David & Matamoros Fernández, 2016). Factors such as the absence of content moderation can therefore be expected to lead to more extreme expressions. The surges in hateful and xenophobic content online after terrorist attacks have, for example, been found to be highly dependent on which social norms are prevalent in online forums (Álvarez-Benjumea & Winter, 2020). Platforms where the level of monitoring is low or editorial function is absent are thus at higher risk of reinforcing prejudice. If others voice xenophobic opinions in online environments, subsequent comments also tend to become more extreme (Álvarez-Benjumea & Winter, 2020). For many online social sites, we can assume that attention to Muslims as a minority group is amplified. This is especially true if the news media is perceived to be neglecting or toning down issues related to Muslim integration. A need to bring such issues to light might then develop, while controversial aspects at the same time often drive attention online (positive or negative).

It should also be pointed out that there are additional effects of exposure to hostile sentiments in the broader online environment, than in traditional news media. In the absence of strong social norms online, negative stereotypes or hostile sentiments might be reinforced in a downward spiral. This kind of debate climate may also risk muting moderate voices and lead to (self-) exclusion of minority groups (Alkazemi, 2015; Bor & Petersen, 2022; Slater, 2007). Moreover, if certain expressions and attitudes are more visible than others, it might lead to the assumption that these views are more widespread than they are. It is therefore central to shed light on the potential differences in how Muslims as a minority group are portrayed across media types and to what extent the amount of attention differs. So even in the instances where the two types of media are equally negative (or displays variation in the type of negative context), the mere share of attention on social sites is important because it might lead to perceptions that some views are more widespread than they de facto are.

Based on our comprehensive review of the literature concerning online hostility toward Muslims in Europe, we have formulated a two-dimensional concept to understand the ‘online hostility hypotheses’ better. The first dimension focuses on the intensity or nature of expressed hostility, while the second dimension examines the amount of negative attention. By assessing the evidence in our study against these conditions, we aim to determine the level of support for the online hostility hypothesis in relation to Muslim representations.

To accomplish this, we have chosen to concentrate on eight European democracies, namely Norway, Sweden, Denmark, the Netherlands, Germany, France, Spain, and the UK. These countries were selected based on their shared experience of considerable growth within their Muslim minority populations, as well as the presence of radical right populist parties (RRPs) represented in their parliaments. The presence of RRPs in the political landscape indicates that the issue of migration is politicized and potentially extensively discussed in both editorial news media and unmoderated social sites. Utilizing online text data from these eight countries provides us with a robust and substantial dataset for conducting our empirical analysis.

Data and methods

For the analysis we have utilized a distributional semantic online lexicon developed by the Linguistic Explorations of Societies (LES) project (Dahlberg et al., 2023). The lexicon allows researchers to explore meanings and usages of words in online media across a substantial number of languages.¹ The LES distributional semantic online lexicon contains a massive number of news articles, blog posts, and forum posts that are divided into ‘news’ media and ‘social’ content. Along with the text content, each document contains metadata, including time stamps, source country, and source language. Overall, the lexicon covers data from 45 languages and 102 countries, and in combination, it covers 141 language-country combinations (for a detailed overview, see Dahlberg et al., 2023). Of these available data, we chose to focus on established democracies in Western Europe that have experienced considerable growth within their Muslim minority populations and in which questions related to Muslim minorities have been central to political campaigns and mobilization in the period of data collection by the presence of radical right populist parties. Further narrowing our scope, we have on top of the above mentioned criteria, chosen to include only those countries where both sufficient amount of data exists for the analysis and where the authors have the basic knowledge of the languages required in order to conduct sensible analyses and interpretations, which left us with a sample of eight countries: Norway, Sweden, Denmark, the Netherlands, Germany, France, Spain, and the UK. Within these countries, there are important variations that might influence our measurements such as media system, journalistic conventions, migration restrictiveness etc. In addition, we cannot fully account for the possible variations when it comes to media sites included or excluded in the corpus, as will be further discussed below. Our results should be interpreted with these possible caveats in mind.

In the lexicon, data is provided by the Swedish language technology company Gavagai. The lexicon is continuously updated with daily batches of structured online text data in the JSON format, each JSON-file contain several documents.² It should be mentioned that text data has become a profitable commodity and acquiring data from large technology companies is difficult and very costly, even for larger data distributors. Consequently, the LES lexicon only contains sources open to the public and free of charge. Web articles placed behind paywalls are omitted from the sample, as are data from closed platforms, such as Facebook, Instagram or Twitter (Dahlberg et al., 2023). Controlling the content of the different sources in the text database is practically impossible. Dahlberg et al. (2023) have, however, provided a list of the top ten URLs for each language, country, and media type available in the lexicon (see Table in Appendix 1). The list ranks in descending order all of the stem URLs sampled in the models according to the number of web documents originating from each particular URL. In spite of the large number of documents, it is important to remember that they do not represent the entire text corpus for a given language-country combination. Rather it is a large, although not random, sample of the largest sources from each language-country combination.

The classification between social sites and editorial media in our study is justified for several compelling reasons. The classification decision was made independently by technicians at the data provider, ensuring objectivity and consistency in the analysis. This pragmatic approach avoids potential biases that could arise if researchers subjectively classified the data based on their research interests.

News media, including traditional newspapers, are recognized as established editorial sources with content produced through professional journalistic practices. While some news articles may feature reader-generated content in comment sections, the primary content is authored by professionals and the comments sections are strictly moderated. Consequently, the vast majority of text data from these sites is expected to be editorial in nature, making it highly relevant for examining news media representations.

On the other hand, social sites comprise a diverse range of online platforms, such as forums and blogs, where communities of users actively generate and disseminate content. These platforms offer more participatory and decentralized spaces for discussions and exchanges. The data from social sites is open and accessible to a broader audience, which allows for greater anonymity and potentially more candid expressions. This characteristic is particularly important for exploring the online hostility hypothesis, as more anonymous spaces may facilitate more pronounced expressions of online hostility.

A drawback in our analysis is the fact that we do not have access to closed platforms like Facebook, Twitter, or Instagram. At the same time, these kind of media platforms often require user verification, reducing the level of anonymity. In contrast, open web forums, categorized as social sites in our study, offer a higher degree of anonymity, enabling users to maintain their privacy through the use of non-verified e-mail addresses. This feature may contribute to more candid expressions and a potentially heightened level of online hostility.

Additionally, the division between news media and social sites aligns with the evolving nature of online communication. Social platforms are known for their participatory nature, where user-generated content and interactions play a significant role. By distinguishing these participatory spaces from news media, we can focus on distinct aspects of online communication and identify patterns of semantic similarity in the representations of Muslims. The division in our study is thus both practical and methodologically reasonable.

From a methodological point of view, the lexicon builds on word vectors or word embedding models. These models are machine-learning methods used in natural language processing and distributional semantics in which each word is represented by a vector that captures the semantic relations between terms. According to the distributional hypothesis, semantic similarity is defined as linguistic distributions in which the degree of semantic similarity between two linguistic terms depends on how similar the linguistic contexts in which they appear are (Lenci, 2008, p. 3). In this respect, it is not the co-occurrence of words per se that is of interest but rather the co-occurrence with the same other words. That is, it is the shared context that matters as two words that co-occur with the same cluster of other words is considered semantically similar (Garg et al., 2018). Few social science studies have yet to utilize these recent advances in natural language processing (Rheault & Cochrane, 2020), but they show clear advantages over more traditional lexicon-based approaches. Crucially, these models have proven to be a useful tool for studying subtle aspects of out-group hostility in mass-mediated content (Kroon et al., 2021).

The LES distributional semantic online lexicon is an implementation of a specific type of word2vec model, Continuous Bag of Words (CBOW), which was developed by Mikolov et al. (2013). The selection of this model is motivated by its particularly suitable properties, including effective computational implementation and high-quality vectors. By assigning context and target vectors to words via an initial randomization of vectors, the CBOW model is computationally efficient and to compare the semantic similarity of different words, the vectors are subsequently updated and trained to satisfy the distributional hypothesis. More specifically, by gathering and processing numerous samples of text data, the CBOW model updates the vectors. To determine a score for the target word and its context, the components of the single target and context vectors are multiplied and compiled (Dahlberg et al., 2023). There are, however, some drawbacks associated with word2vec models. Antoniak and Mimno (2018) used a smaller corpus to demonstrate that different word embedding methods yield unstable results. In the case of small corpora, it is unclear whether this result reflects corpus characteristics or language constants. This variance is, however, significantly lower for larger corpora sizes (Pierrejean & Tanguy, 2018). Given that the median corpus size of all models in the LES lexicon (~140,000,000 words for news media data and ~ 110,000,000 for social data) is considerably greater than the largest corpus used by Antoniak and Mimno, one can expect less variability of this type.

Our analytical strategy builds on using the word embeddings themselves as a measure and the output from the word2vec model in terms of similarity scores for neighboring terms. When the cosine similarity is higher than around 0.7 this usually shows a robust semantic similarity in terms of how two terms are used (Dahlberg et al., 2023).For our analysis of negatively loaded words, we observed that the top 30 most semantically similar terms to ’Muslims’, all had a similarity score exceeding 0.7. We therefore chose to narrow our analysis of negative terms to this subset of the top 30 across countries and then proceeded to qualitatively interpret and categorize these terms based on their negative connotations. In the section of our analysis where we examine several negative terms across media types, the variation in the similarity scores between the same terms are of importance. For this part of our analysis, therefore, we included the top 100 most semantically similar terms.

Results

Representations of Muslims in online social and news media

In the first part of the analysis, we focused on examining evidence of a difference in the tone of Muslim representation between the two media types. First, we explored if ‘Muslims’ tended to be associated with a larger set of negatively charged terms in social content. Next, we examined the intensity of negatively charged words, if they were more strongly associated with ‘Muslims’ in social content than in news media content.

Negatively charged words

In the first test, which focused on whether representations of Muslims on social sites are characterized by a larger variety of negative associations, we examined the 30 words with the highest cosine similarity scores for each of the two media types. We made a qualitative interpretation of a ‘negatively loaded’ term as these often appeared as ‘extremists’, ‘terrorists’ or ‘Islamists’ while other terms where neutral such as nationalities or general religious terms such as priests or imam (see Appendix 2). Cosine similarity indicates the level of semantic similarity between two terms as represented by the vectors. This reflects the semantic context in which the terms occur and should not be confused with document context. Thus, important to keep in mind when interpreting the results is that any negative term reflects a semantic context rather than a co-occurrence of words, why the terms are not necessarily used directly to describe Muslims but used in a similar way.

Figure 1 shows that to the extent that there is a systematic difference between social and news content, it is in the direction of the online hostility hypothesis. In most of the countries, there were slightly more negative semantically similar words to ‘Muslims’ in social forums compared to news media (see Figure 1). In the Nordic countries and in the UK, there were more negatively loaded words on social forums, with Sweden displaying the largest difference between media types. Sweden is also the country with the largest number of negatively loaded words in social content (7 out of 30), followed by Denmark (6 out of 30), and Norway and the UK (5 out of 30). Nevertheless, negatively loaded words exhibited greater prominence in social content only within five out of the eight examined countries. Additionally, the findings indicated a notable statistical significance solely in Sweden.³

Figure 1. Online hostility—number of negatively loaded terms.

Note. The figure shows the number of negatively loaded words among the top 30 words with the highest similarity score in social and news media content. See Appendix 2 for the complete list of most similar terms.

Across both media types, there emerged a notable coherence among negatively loaded terms such as ‘Islamists,’ ‘extremists,’ ‘Nazis,’ ‘terrorists,’ and ‘jihadists’. This pattern signifies a discursive trend related to extremism and terrorism, wherein these terms shared analogous semantic contexts with the term ‘Muslims.’ This consistent association was observed across nearly all countries and within both media types. However, it is worth noting that Spain diverged from this prevailing trend.

In countries where fewer negatively loaded words appeared in news media (Denmark, Sweden, Norway, UK, and Germany), we found a clear pattern of a linguistic context relating to terrorism and extremism, but no other negatively charged words appeared in the news content. Taken together, the following words appeared in news media content in these countries: ‘nazis,’ ‘extremists,’ ‘terrorists,’ ‘jihadists,’ ‘militants,’ ‘right-wing extremists,’ ‘fundamentalists,’ and ‘mørkemænd’ [roughly religious fundamentalists].

In social content, we found additional negatively loaded words with high similarity scores such as ‘negroes’ in Sweden and Germany,⁴ ‘barbarians’ in the Netherlands, ‘invaders,’ in Germany and Spain,⁵ ‘communists’ in Denmark and Spain, and ‘fascists’ in the UK (see Appendix 2). This suggests a larger depository of negatively loaded terms in the semantic context pertaining to social content although the patterns of differences across media types were not consistently present across all contexts.

Out of the top 30 terms with highest linguistic similarity, we also noticed a clear tendency across countries toward fewer religious words related to ‘Muslims’ on social sites. Instead, we note connections to terms about minority groups, immigration or national identity in general, such as the Roma, Yazidis, or Palestinians. Other social identity-related terms, such as ‘women,’ ‘feminists,’ ‘homosexuals,’ and ‘transgender,’ were also found, as well as ‘oppression’. Apart from minority groups and immigration-related terms, these other identity-based markers foremost appeared in the Nordic countries and the Netherlands. This could indicate a stronger discourse in these countries around the perceived conflict between gender and sexual rights and Islam.

To sum up, we find that the most prevalent negative associations in news media are centered around the linkages to terrorism and extremism, whereas additional negatively charged terms emerged in the social forum data in a majority of the countries but not in all (e.g., barbarians, invaders, and negroes). This renders some support to the online hostility hypotheses when it comes to a larger span of negative sentiment. The first part of this analysis of tone thus finds some support for the online hostility hypothesis. That noted, the most prominent negative association between Muslims and terrorism is present in both media types and not specific to social forums. For the next part of the analysis, we examined if these negative associations were stronger in social content.

Cosine similarity

In the second step of our analysis, we took full advantage of the cosine similarity metric to examine to what extent the similarity score differed between sources for a chosen negatively charged term (the higher the similarity score, the more similar the use of the two terms). A similarity score that is close to 1 indicates that two words are used more or less synonymously. We examined how this score for the most common negatively charged representation of Muslims – the association with terrorism and extremism – differed across media types. As these associations were found in all countries and in both social and news content (among the top 100 neighboring terms with the highest similarity score), we were provided with the opportunity to compare the similarity scores of an identical set of corresponding words across media types. In previous studies, the association to terrorism and extremism has been found in both news media and on various internet forums. However, according to the hostility hypothesis, this link could be stronger, i.e., have a higher cosine similarity score on social sites than in the news media.

Figure 2 shows that nearly all the words related to terrorism/extremism had higher similarity scores in social content, overall supporting the online hostility hypothesis, although the cosine similarity scores are high in both media types. This finding generalizes across all countries, but there are some notable country differences that could be further explored in future work. In Spain, the similarity scores were the most similar between media types for merely two phrases: ‘fundamentalists’ and ‘extremists.’ We found the biggest difference between media types in Sweden, the cosine similarity scores for social content were high – all phrases scored between 0.83 and 0.88—and news media had among the lowest scores. Compared to Sweden, Denmark and Norway both showed a smaller gap between media types but otherwise displayed a similar pattern: a tendency toward more hostile portrayals on social sites. However, the differences in similarity scores between media type was only significant for Sweden.⁶

Figure 2. Cosine similarity scores.

Note. The figure shows cosine similarity scores for terms related to extremism/terrorism that were found in both media sources in the eight countries of interest. The maximum value along the y-axis is 1.

To sum up, Figure 2 also provides some, albeit modest, evidence that is consistent with the online hostility hypothesis. The main finding is that the semantic similarity between Muslims and terrorism and extremism are quite high in both media types.

The attention gap

To examine the second part of the online hostility hypothesis, the potential attention gap, which holds that Muslims and Islam are discussed more often on social sites than in news media, we compared differences in attention between media type across countries. Both the amount of textual data and the total term count varied by country, depending upon the number of unique words and the size of the textual corpora. As an indicator of amount of attention, we therefore calculated the relative term frequency per thousand occurrences of the terms ‘Muslim’, ‘Muslims’ and ‘Islam’ in each country for both social sites and news media sources (the translated term count divided by the total term count multiplied by 1,000).

Figure 3 shows the amount of attention directed toward Muslims and Islam in news media and social sites. The x-axis illustrates the relative frequency of mentions in the whole corpora (per thousand), the countries are sorted in descending order given the value of attention in social forums.

Figure 3. Attention gap between social and news media sites.

Note. The figure displays the relative term frequency of the terms ‘Muslim’, ‘Muslims’, and ‘Islam’ in social and news media across eight European countries. The relative frequency values in the figure were calculated by dividing the total number of occurrences of the terms by the total number of words in each corpus, then multiplying by a constant of 1000. This allows for a comparison of term usage across different corpus sizes. Taking Denmark as an example, the corpus size for social sources is 287,515,778 and the term frequencies 28,269, multiplied with a constant of 1000 the value is 0.0983. The corpus size for news sources in Denmark is 306,396,404 and the term frequencies 44,341, resulting in a value of 0.1447 (multiplied with 1000).

The main takeaway from Figure 3 is considerable evidence of an attention gap as ‘Muslim/Muslims’ and ‘Islam’ made up a larger share of the total content on social sites than in news media. This implies that the broader online media environment contributes to increased visibility of content about Muslims and Islam in Europe.⁷

There were some differences across countries. To start with, Sweden had a somewhat larger gap than Norway, and ‘Muslim(s)/Islam’ occurred more often in Norwegian news media than in Swedish. However, the extent to which these terms appeared on social sites were equivalent (the relative term frequency was about 0.11 in both countries). In the Netherlands, news media wrote even less about Muslims and Islam, although these topics occur far more often in social content (just as in Sweden). In the UK, the relative share of mentions was much higher in social content, resulting in the largest gap despite mentions in news media not being exceptionally low (0.08). In France, there was a slightly smaller attention gap between social and news media sites, as the use of the terms ‘Muslim(s)/Islam’ occurred relatively frequently in both. The relative share of mentions in Germany was similar to that of France, with only a slightly lower representation of Muslims in social content. In news media content, the lowest relative share was observed in the Netherlands, followed by Sweden and Spain.

It is generally common in all these countries, then, for Muslims and Islam to attract considerably more attention on social sites than in news media; however, Figure 3 also shows that there are two exceptions to this general pattern. Denmark stands out as the only country where Muslims and Islam made up a much larger share of news media content than of social content. The difference is mainly the result of a comparatively high share of mentions in Danish news media. This raises interesting hypotheses about peculiarities in the amount of coverage about Islam and Muslims in traditional news media in Denmark. Spain stands out as a country-context with comparatively less content about Islam and Muslims in both media types.

Discussion and conclusion

In this study, we explored the online hostility hypothesis, which posits that user-generated content in online media might lead to an increase in hostile expressions, particularly affecting minority groups. By utilizing advanced word embedding models, we aimed to analyze not only explicit traces of online hostility but also the more implicit forms of biases in the representation of Muslims within the broader online environment.

An essential aspect of our research involved comparing data categorized into ‘social’ and ‘news’ media content. Despite acknowledging the inherent imperfections in this classification due to blurred boundaries between the two, such a challenge is common in studies focusing on online media. Nevertheless, it does not diminish the importance of our results, as our study still contributes valuable insights into the distinctions in online rhetoric between social sites and news media. By centering our analysis on social sites such as blogs and forums, we gained unique access to representations in more participatory and user-generated spaces, offering unparalleled insights into how minority groups are discussed in online environments where users actively generate content.

We formulated a conceptualization of the general online hostility hypothesis as a two-dimensional framework that distinguishes between whether: (1) representations of Muslims on social sites take on more hostile forms and (2) the amount of negative attention given to Muslims is greater.

Regarding the first dimension, our results are mainly influenced by representations linked to extremism and terrorism in social forums, which (re)produce and reinforce existing representations in the news media, rather than Muslim representations being considerably more hostile on social sites. In most contexts studied, the number of negatively loaded terms on social sites tended toward a larger repertoire of derogatory terms, possibly reflecting a larger presence of extremist content in social forums. Although these terms were not numerous, the fact that some quite extreme wordings, such as ‘barbarians,’ were found among the terms with the highest similarity scores says something about the semantic context in which Muslims appear in social forums. In addition, our results showed that the linkages to terrorism and extremism were overall more strongly pronounced on social sites in many countries, but not all.

Regarding the second dimension, our study yielded intriguing findings concerning the attention gap. We discovered that representations of Muslims and Islam received more attention on social sites than on news media sites in most of the countries under study. This discrepancy underscores the significant role social platforms play in shaping the discourse around minority groups, potentially contributing to a more hostile public sphere.

Moreover, the selection of countries allowed us to explore diverse socio-political contexts, contributing to a comprehensive analysis. While acknowledging country-level differences, we highlight common patterns of negative representations and attention given to Muslims on social sites across these European democracies. Muslims and Islam were represented more often in social forums than in news media in most of the countries studied. However, Denmark and Spain were two exceptions. In Denmark, the relative frequency of the terms ‘Muslim/Muslims’ and ‘Islam’ was much higher in news media than in social content, and in Spain, the relative frequency was similar between media types. We interpret these findings as the Spanish political discourse around Muslims and Islam being less charged regardless of media type, whereas in Denmark, the news media stands out because of the comparatively high amount of coverage. Such forms of variations need to be followed-up in future research.

In conclusion, our results are consistent with the online hostility hypothesis and more strongly support an attention gap in the two types of media. It is important to note that hostility in online media is not less prevalent than what previous research has indicated. The sheer size of the data analyzed here is not well suited to capturing the harm that could be done by the most aggressive or hostile expressions found on various unedited blogs and forums. Nonetheless, our findings should be interpreted in the context of representations of Muslims in a very broad and popular ‘social’ online environment.

This study contributes to a larger body of work that documents how social sites with user-generated content, such as social media, blogs, and forums, have contributed to a fundamental reshaping of the public sphere. A common challenge in this study and most others is the lack of access to data from some of the largest social media companies such as Facebook, Instagram and Twitter. It is possible, but ultimately remains to be established if the results of our study would have supported the online hostility hypothesis even more strongly if such data could be included. Also, as mentioned, a limitation of our study is the challenging task of distinguishing between news media and social sites in the online realm. News media content is integrated into social platforms, and vice versa. Despite the imprecise categorization of the sources, our results show clear differences between the two types of online media.

Large platforms today have active policies against content that is intolerant or hostile toward minority groups. A potential consequence of these policies might be representation that is increasingly similar to legacy news media. Nonetheless, the negative effects of one-sided angles and representations, such as Muslims being predominantly portrayed as extremists or terrorists, remain. The increased attention paid to Muslims on social sites therefore contributes to a more hostile public sphere.

Our findings underscore the importance of addressing negative portrayals of minority groups in online media. By emphasizing shared challenges and overall trends, our study enriches the understanding of the impact of user-generated content on public discourse. As we acknowledge the limitations and challenges faced in analyzing online representations, future research could explore data from larger social media platforms to enhance the understanding of online hostility. Despite these limitations, our contribution highlights the need for increased attention from media companies, regulators, and researchers to promote more inclusive and respectful online environments for minority groups.

Acknowledgments

This work was supported by the Research council of Norway under Grant number 287592.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Norges Forskningsråd [287592].

Notes

1. Data Availability Statement: The data is freely available through the University of Gothenburg deposit: https://www.gu.se/en/linguistic-explorations-of-societies/online-lexicon

2. The data is continuously updated and results might therefore change over time. The data we rely on in this study was accessed in May 2020.

3. The one-tailed paired-sampled t-test for Sweden showed a t-value of 1.83 and a p-value of .036.

4. In a European context, this word can be assumed to be used derogatory in social content (note that it did not appear in news media), as a reaction to non-European immigrants to Europe and is used as an ethnic slur word (see e.g., Gullestad, 2005).

5. Spain appears to be a rather exceptional case regarding semantical similar terms to ‘Muslims’. It is possible that the reason ‘Invaders’ appear in the Spanish data is different from that seen in Germany. Given Spanish history, ‘Invaders’ could refer to the Muslim conquest of the Iberian Peninsula, whereas in Germany it more likely reflects far-right discourse about Western societies being invaded by Muslim immigrants and the ‘Islamization’ of Europe (Carr, 2006).

6. The one-tailed paired-sampled t-test for Sweden showed a t-value of 2.87845 and a p-value of .005.

7. The one-tailed paired-sampled t-test of the difference in terms frequencies between the two types of media in absolute numbers showed a t-value of 2.53884 and a p-value of .012.

Appendix 1

Top Ten Web Domains and Number of Web Documents by Language and Country

Table

		News		Social
Language	Country	URL	Web-doc	URL	Web-doc
Swedish	Sweden	www.aftonbladet.se	29709	www.flashback.org	187392
		www.svt.se	28201	www.familjeliv.se	79379
		sverigesradio.se	27263	www.avanza.se	66071
		www.expressen.se	25675	klocksnack.se	46531
		www.gp.se	24366	bloggbevakning.se	31946
		www.hd.se	23016	happyride.se	29608
		www.corren.se	21622	www.jagrullar.se	26625
		www.sydsvenskan.se	21489	petterssonsblogg.se	22451
		www.nt.se	21319	loading.se	21649
		www.nsd.se	20794	www.minhembio.com	15974
Norwegian	Norway	www.nrk.no	87168	forum.babyverden.no	193073
		mylder.no	70054	www.diskusjon.no	61720
		www.folkebladet.no	24416	www.hifisentralen.no	44317
		www.nettavisen.no	21973	www.klikk.no	39536
		www.dagbladet.no	21464	resett.no	34625
		www.ung.no	20760	www.foreldreportalen.no	29983
		www.aftenposten.no	19902	www.meetv.no	19727
		www.fvn.no	19671	elbilforum.no	18764
		www.abcnyheter.no	19479	www.smp.no	14460
		www.sol.no	17605	forum.flyprat.no	14038
Danish	Denmark	news.dk	36426	www.hardwareonline.dk	83729
		www.trendsales.dk	35623	www.eroguide.dk	77372
		sn.dk	27003	forum.recordere.dk	55301
		www.jv.dk	21442	nyheder.dk	48176
		www.fyens.dk	21057	slyngebarn.dk	47929
		www.dr.dk	18874	www.slaegtogdata.dk	39303
		www.bt.dk	17704	www.dailyrush.dk	37793
		stiften.dk	16435	localeyes.dk	23808
		vafo.dk	16301	www.thailand-portalen.dk	20046
		viborg-folkeblad.dk	14465	www.astro-forum.dk	19053
English	United Kingdom	www.dailymail.co.uk	17322	www.thestudentroom.co.uk	393157
		pharma.id.informa.com	16779	www.mumsnet.com	174694
		investorshub.advfn.com	16628	forums.frontier.co.uk	33578
		www.newsdump.co.uk	11457	uk.answers.yahoo.com	25440
		uk.news.yahoo.com	11209	forums.overclockers.co.uk	24094
		www.briefreport.co.uk	10194	nearwen.com	18849
		www.bbc.co.uk	8411	www.thefretboard.co.uk	18461
		www.tvguide.co.uk	8015	www.hibs.net	16453
		www.folowsite.com	7923	forums.superherohype.com	14873
		www.pistonheads.com	7152	www.railforums.co.uk	13771
French	France	www.vinted.fr	73198	www.jeuxvideo.com	231672
		www.paruvendu.fr	37199	forum.hardware.fr	196259
		www.lesiteimmo.com	12881	www.titrespresse.com	57443
		infos.niooz.fr	11604	fr.answers.yahoo.com	32435
		www.actualites-du-jour.eu	10265	glowbi.net	23692
		www.wynfoot.fr	10076	forum.canardpc.com	21529
		www.jobintree.com	10048	www.bladi.info	16442
		www.boursorama.com	9262	www.courseapied.net	14876
		fr.news.yahoo.com	8545	forums.futura-sciences.com	13104
		portail.free.fr	8342	carrefour-dentaire.clicforum.fr	11162
Dutch	Netherlands	www.headlines24.nl	79764	forum.fok.nl	126658
		www.speurders.nl	68301	forums.marokko.nl	102250
		www.nieuwsdump.nl	63943	forum.beneluxspoor.net	49286
		www.alarmeringen.nl	54587	www.autoblog.nl	17556
		www.ad.nl	39859	www.circuitsonline.net	11880
		www.yeldo.nl	38501	www.iex.nl	11221
		www.marktplaats.nl	36291	www.ouders.nl	10442
		www.afspraakjes.com	32341	www.bokt.nl	10413
		www.hetwarenhuis.shop	31757	www.musicmeter.nl	10388
		www.vacatures.nl	28533	www.zegmaaryes.nl	9608
German	Germany	www.mamikreisel.de	25253	www.gutefrage.net	257509
		www.aktuelle-nachrichten-online.eu	13456	www.konsolentreff.de	30254
		www.focus.de	9172	www.aktiencheck.de	26232
		erotik.markt.de	7655	www.ariva.de	17782
		www.aktiencheck.de	7544	www.produktfrage.net	15087
		www.tvinfo.de	7042	www.computerbase.de	12727
		www.newsdeutschland.com	6655	extreme.pcgameshardware.de	12172
		www.tvdigital.de	5726	www.mikrocontroller.net	11978
		www.welt.de	5359	www.dslr-forum.de	10444
		www.markt.de	5133	forum.mods.de	9667
Spanish	Spain	es.wallapop.com	45570	es.answers.yahoo.com	58708
		www.europapress.es	13201	www.elotrolado.net	23945
		www.ivoox.com	11528	www.burbuja.info	23605
		www.fotocasa.es	9829	www.mediavida.com	21125
		www.gentedigital.es	8161	www.bmwfaq.org	11315
		www.20minutos.es	8093	www.foroazkenarock.com	9668
		www.alamaula.com	7740	www.forocoches.com	8615
		www.eleconomista.es	6800	electomania.es	7037
		us.ivoox.com	6740	www.mundodvd.com	6509
		www.eldiario.es	4914	www.mexicoarmado.com	6234

Source: Dahlberg et al. (2023).

Appendix 2

“Muslims” Query Term Results

Table

	News				Social
	Total and Query Term Count	Neighbor Term	Similarity score	Neighbor Term Count	Total and Query term count	Neighbor Term	Similarity score	Neighbor Term Count
DK
	287515778	jews	0.8984252214431763	2346	306396404	jews	0.8799843788146973	2441
	6349	islamists	0.869289755821228	801	5846	immigrants	0.8014253377914429	6662
		immigrants	0.8534156680107117	3659		imams	0.7727760076522827	1167
		muslims	0.8403010964393616	1313		women	0.769815981388092	61174
		americans	0.822698175907135	1732		europeans	0.7581225633621216	953
		imams	0.8136165142059326	620		arabs	0.7569618821144104	375
		europeans	0.8113242387771606	802		catholics	0.756178081035614	427
		catholics	0.8099439144134521	366		christian	0.754461407661438	5843
		feminists	0.8092725276947021	572		atheists	0.7419930696487427	201
		minorities	0.801950991153717	576		turks	0.7391348481178284	1033
		terrorists	0.8009817600250244	1270		terrorists	0.7382638454437256	2749
		jihadists	0.7962552309036255	354		nazis	0.7356525659561157	513
		arabs	0.7938454747200012	404		gay	0.7341813445091248	2948
		danes	0.7938428521156311	21653		immigrants	0.7336133122444153	944
		nazis	0.7928537130355835	504		priests	0.7310820817947388	2529
		turks	0.7894397974014282	455		non-Muslims	0.7305962443351746	100
		russians	0.7870850563049316	623		muslims	0.7259365320205688	613
		atheists	0.7852877378463745	195		people	0.7231340408325195	122905
		extremists	0.784034788608551	307		individuals	0.7224493026733398	1417
		kurds	0.7824223041534424	439		preachers	0.7222427129745483	145
		christian	0.7816625833511353	5446		the population	0.7215588092803955	11456
		ukrainians	0.7783262133598328	159		roma	0.7214122414588928	350
		immigrants	0.7780593037605286	592		americans	0.7209521532058716	3072
		politicians	0.7774834632873535	11311		fundamentalists	0.7202831506729126	74
		palestinians	0.7761326432228088	530		politicians	0.718032717704773	27830
		refugees	0.7728846073150635	10062		danes	0.7177664637565613	36324
		africans	0.7716609239578247	369		asylum seekers	0.7163331508636475	8898
		communists	0.7716572284698486	451		palestinians	0.7140147089958191	1836
		people	0.7685297727584839	108016		mørkemænd	0.7126976847648621	62
		islam	0.7662104368209839	6281		africans	0.709459662437439	422
F
	343325686	jewish	0.9305332899093628	11249	175157444	jews	0.9132997989654541	2681
	15200	christians	0.9269692301750183	7302	4082	christians	0.8881218433380127	1779
		non-muslims	0.870125412940979	269		catholics	0.8725447654724121	1347
		catholics	0.8520673513412476	3253		protestants	0.8206795454025269	260
		believers	0.8499165773391724	2176		armenians	0.8181478977203369	228
		maghreb	0.8374475240707397	641		haitians	0.8113550543785095	185
		freemasons	0.8292096853256226	348		palestinians	0.8100022077560425	3271
		protestants	0.8279759883880615	819		immigrants	0.8005399703979492	800
		mohammedans	0.8269538879394531	79		mauritanians	0.7972291111946106	247
		zionists	0.8143895864486694	1041		believers	0.796298623085022	342
		arab	0.8110591173171997	7581		salafists	0.7927200198173523	190
		salafists	0.8095986843109131	654		oppressed	0.7797596454620361	124
		christians	0.8059050440788269	159		druze	0.7791774868965149	83
		atheists	0.8018824458122253	581		yazidis	0.7790405750274658	52
		palestinian	0.8001854419708252	6102		roma	0.7788379192352295	377
		apostates	0.7944056987762451	157		sunnis	0.7787591218948364	330
		sunnites	0.7931498289108276	848		algerians	0.776371955871582	1332
		proletarians	0.7926740646362305	308		settlers	0.7752077579498291	452
		colonizers	0.7912195920944214	196		priests	0.7750893831253052	901
		shiites	0.7907232046127319	942		north africans	0.7748298645019531	93
		islamists	0.7894303798675537	3508		native american	0.7739346623420715	184
		peul	0.7890667915344238	444		illegal immigrants	0.7703803777694702	357
		immigrants	0.7880880236625671	2213		copts	0.7703430652618408	178
		peoples	0.7878926992416382	8077		traitors	0.7698951363563538	165
		kabyle	0.7863555550575256	280		greek	0.7697272896766663	731
		unbelievers	0.7845651507377625	546		tunisians	0.7643290758132935	851
		Jewish	0.7841501235961914	400		jihadists	0.7636706829071045	1550
		algerian	0.7833181023597717	3295		comorians	0.7632008194923401	85
		fundamentalists	0.7831102013587952	435		serbian	0.7617343664169312	470
		native american	0.7831073999404907	426		shiites	0.7610283493995667	751
D
	304004474	christians	0.9173802137374878	5603	274854920	christians	0.8673284649848938	3309
	5476	muslim	0.9165597558021545	2084	3757	jews	0.8560304641723633	3715
		jewry	0.8945310115814209	7535		foreigners	0.8026889562606812	3607
		diddle	0.8643320798873901	3300		turks	0.8021324276924133	2565
		catholics	0.8585966229438782	971		believers	0.7748012542724609	921
		palestinian	0.8558247089385986	1036		immigrant	0.772157609462738	1106
		believer	0.8465038537979126	956		catholics	0.7666035890579224	1216
		israelis	0.8464958667755127	795		muslim	0.7627832889556885	161
		arab	0.8452801704406738	1247		refugees	0.7564051151275635	28126
		foreigners	0.8402968645095825	3949		german turks	0.7512156963348389	211
		mohammedan	0.8347672820091248	204		rohingya	0.7498397827148438	658
		european	0.8309387564659119	2632		arab	0.7407301664352417	472
		refugees	0.8307251930236816	15366		israelis	0.7406657934188843	891
		islamists	0.8292256593704224	1545		german-turks	0.7397850155830383	151
		immigrant	0.8284182548522949	913		hindus	0.734607458114624	183
		migrants	0.8267512321472168	5837		europeans	0.7303206324577332	3404
		immigrant	0.8220546245574951	1389		palestinians	0.7230066061019897	2297
		kurds	0.8174975514411926	1596		salafists	0.7219949960708618	492
		invaders	0.815171480178833	536		kurden	0.7209416627883911	1961
		atheists	0.8144359588623047	698		citizen	0.7209010124206543	25088
		african	0.8107448816299438	979		imame	0.7188147306442261	285
		minorities	0.8105522394180298	1249		jesid	0.7176294326782227	229
		extremists	0.8093643188476562	775		muslim women	0.715847373008728	96
		feminists	0.7998329997062683	423		migrants	0.714080810546875	9845
		muslim inside	0.7945363521575928	108		bishops	0.7126398682594299	698
		imams	0.7935082912445068	314		minorities	0.7119889259338379	1046
		salafists	0.7922035455703735	384		uyghurs	0.7086376547813416	126
		war refugees	0.7885769605636597	196		eu citizens	0.7064191102981567	1095
		hindus	0.7842832803726196	187		politician	0.7024248838424683	13610
		negro	0.7807798385620117	785		fundamentalists	0.7019829154014587	180
NL
	307695467	christians	0.9140777587890625	5243	166863184	christians	0.906104564666748	1798
	13876	jews	0.904579758644104	8219	2569	jews	0.8682273626327515	1806
		non-muslims	0.8973133563995361	531		believers	0.83478844165802	532
		turks	0.8852408528327942	3545		turks	0.8343409299850464	1260
		muslims	0.8845597505569458	360		immigrants	0.8265764713287354	776
		moroccans	0.8744070529937744	4185		palestinians	0.8159639239311218	864
		arabs	0.8701925277709961	1826		salafists	0.8143287897109985	124
		europeans	0.8670443296432495	1671		catholics	0.8142178058624268	194
		kurds	0.8659932613372803	1592		gays	0.8122844696044922	571
		mohammedans	0.864692211151123	238		foreigners	0.8002344369888306	798
		believers	0.864571213722229	3014		surinamese	0.8001985549926758	154
		palestinians	0.863066554069519	2682		jihadists	0.7968693971633911	694
		salafists	0.857874870300293	374		kurds	0.7895827293395996	701
		converts	0.8349413871765137	216		transgender people	0.7842847108840942	687
		immigrants	0.8332260847091675	1131		europeans	0.7810553312301636	723
		extremists	0.8323765397071838	647		refugees	0.7778508067131042	8855
		atheists	0.8318689465522766	389		africans	0.77425616979599	200
		zionists	0.8303549289703369	856		moroccans	0.7739830613136292	760
		unbelievers	0.8277046084403992	1404		homosexuals	0.7735870480537415	334
		immigrants	0.8267462849617004	1744		arabs	0.7685331702232361	232
		westerners	0.8243553638458252	606		minorities	0.7675958871841431	418
		foreigners	0.8239244818687439	1703		activists	0.7624831199645996	896
		terrorists	0.8190228343009949	3125		migrants	0.7623451948165894	3501
		reefs	0.818793535232544	218		extremists	0.7616896629333496	424
		sunnis	0.818623960018158	360		rohingya	0.7609646916389465	396
		barbarians	0.8166621923446655	353		syrians	0.7547842264175415	615
		buddhists	0.8139550685882568	283		armenians	0.7530304193496704	138
		natives	0.8138200640678406	477		anti-Semitism	0.7453889846801758	384
		islamists	0.8109578490257263	253		hindus	0.7453230619430542	71
		minorities	0.8091484904289246	1184		islamists	0.7450093030929565	85
NO
	342798943	jews	0.9112447500228882	4441	207620486	jews	0.9097201228141785	1871
	10746	immigrants	0.8571208715438843	7776	4316	sami	0.8155105710029602	836
		muslims	0.8397707939147949	1801		catholics	0.8037387132644653	256
		europeans	0.8382073640823364	1189		minorities	0.8014937043190002	1089
		africans	0.8220085501670837	616		immigrants	0.8007974624633789	4766
		arabs	0.8198606967926025	619		muslims	0.7990990877151489	455
		islamists	0.8132514953613281	1835		feminists	0.7847152948379517	285
		minorities	0.8081362247467041	1480		americans	0.7795620560646057	1952
		jews	0.805367648601532	3271		europeans	0.7763980627059937	451
		americans	0.8023058772087097	2769		atheists	0.7711231708526611	187
		feminists	0.7998031377792358	842		nazis	0.7674612998962402	299
		extremists	0.7971507906913757	832		christians	0.7637782692909241	8127
		terrorists	0.7950811982154846	3224		priests	0.7617613077163696	1368
		non-muslims	0.7940764427185059	352		jews	0.7609983086585999	969
		sami	0.7890869379043579	921		buddhists	0.7532668709754944	112
		atheists	0.7885740995407104	595		citizens	0.7432736158370972	2223
		citizens	0.7844274640083313	3596		gays	0.7429388761520386	2956
		nazis	0.7838712930679321	1070		extremists	0.7422253489494324	434
		christians	0.7799941301345825	18607		transgender	0.7360787391662598	533
		islam	0.7746566534042358	13016		believer	0.7332581281661987	857
		politicians	0.7715606093406677	18393		islam	0.7308211922645569	3597
		converts	0.7651420831680298	140		terrorists	0.7299136519432068	1467
		working class	0.7626600861549377	988		indigenous	0.7224476337432861	494
		immigrants	0.7605640888214111	545		human beings	0.7134715914726257	59521
		imams	0.7605319619178772	488		politicians	0.712989091873169	14759
		norwegians	0.7533115148544312	25111		right-wing extremists	0.7111761569976807	114
		foreigners	0.7524709701538086	2703		individuals	0.7062369585037231	1125
		jihadists	0.7486145496368408	992		west	0.7057659029960632	2176
		nationalists	0.746896505355835	482		women	0.7029456496238708	46464
		priests	0.7468547224998474	1657		voters	0.7029101252555847	5132
ES	212808478				181345418
	2965	jews	0.8969880938529968	4621	2112	jews	0.9088650941848755	1331
		christians	0.8526670932769775	6857		christians	0.860785961151123	2243
		catholic	0.837313175201416	3454		protestants	0.8451530933380127	184
		palestinians	0.8318731784820557	1805		armenians	0.8316470384597778	87
		kurds	0.830816388130188	700		palestinians	0.824779748916626	1715
		syrians	0.8224096894264221	1145		kurds	0.8226420879364014	460
		jews	0.8168304562568665	254		catholic	0.8170410990715027	1448
		copts	0.8067457675933838	120		colonists	0.7998427748680115	359
		protestants	0.8051671981811523	627		syrians	0.7936455607414246	1495
		armenians	0.8033014535903931	202		believers	0.7858837842941284	493
		heretics	0.7989588975906372	284		rohingya	0.7850176692008972	158
		sunni	0.796869158744812	98		exiles	0.7748839855194092	333
		arabs	0.7965699434280396	2419		orthodox	0.7576808333396912	143
		shiites	0.790259599685669	92		emigrants	0.7541040182113647	719
		africans	0.7893579006195068	1127		clergy	0.7533023953437805	146
		invaders	0.7810227870941162	537		africans	0.7479836940765381	922
		lebanese	0.7764143347740173	103		shia	0.7478432059288025	265
		mapuches	0.7751464247703552	296		bosnians	0.7453662157058716	43
		serbs	0.7741817235946655	157		afghans	0.7423597574234009	287
		sunnis	0.7726525068283081	92		dictators	0.7422834634780884	174
		rohingya	0.7709133625030518	100		communists	0.7350902557373047	789
		turks	0.7707877159118652	869		fundamentalists	0.734321117401123	102
		clergy	0.768490731716156	399		cubans	0.7332668304443359	1817
		emigrants	0.7679081559181213	678		heathens	0.7328619956970215	92
		atheists	0.7668927907943726	499		colombians	0.7306805849075317	1763
		zionists	0.7664029598236084	272		haitians	0.7276077270507812	171
		colonists	0.7655462026596069	611		copts	0.7270801663398743	76
		rebels	0.7639992237091064	2087		evangelicals	0.725785493850708	162
		communists	0.7631212472915649	1914		venezuelans	0.7246309518814087	4624
		believers	0.7611820101737976	1888		extremists	0.7246192693710327	823
SE
	199445643	jews	0.9115906357765198	4074	200956614	jews	0.9053342938423157	2120
	7994	immigrants	0.8936266303062439	8733	4065	roma	0.8146832585334778	978
		swedes	0.8848695755004883	14513		immigrants	0.7972365617752075	3153
		foreigners	0.8819699287414551	991		lgbtq people	0.79604172706604	1036
		islamists	0.8794581890106201	1074		catholics	0.7848873138427734	546
		arabs	0.8687275648117065	2012		sami people	0.7752343416213989	764
		kurds	0.8654888272285461	621		christians	0.7739925384521484	4545
		the muslims	0.8617663979530334	1426		kurds	0.7589949369430542	650
		extremists	0.8588966131210327	617		arabs	0.7457972168922424	249
		nazis	0.8498010635375977	1969		the muslims	0.7334562540054321	167
		terrorists	0.8462136387825012	2047		turks	0.7298053503036499	405
		feminists	0.8410838842391968	2660		feminists	0.7269093990325928	495
		roma	0.8315713405609131	945		americans	0.7217720746994019	2518
		turks	0.8304921388626099	527		jews	0.7190685272216797	399
		men	0.8303588032722473	39935		nazis	0.7178959846496582	1638
		catholics	0.8281192779541016	521		shia muslims	0.7174474596977234	366
		americans	0.8278272151947021	1201		africans	0.7138596773147583	207
		jihadists	0.8277155756950378	412		african american	0.7074177861213684	124
		europeans	0.827559769153595	590		minorities	0.7042444944381714	1403
		nationalists	0.8233308792114258	522		extremists	0.7019384503364563	1460
		the immigrants	0.821483850479126	1092		women	0.7017626762390137	52221
		refugees	0.821476399898529	5713		refugees	0.6992135047912598	10854
		russians	0.8203059434890747	728		homosexual	0.6964198350906372	1794
		negroes	0.8196518421173096	921		dissent	0.6964141130447388	355
		atheists	0.8167799711227417	653		hindu	0.6933852434158325	165
		africans	0.8161040544509888	686		transgender	0.6916278600692749	1021
		muhammadan	0.81499183177948	96		mexicans	0.6874994039535522	341
		women	0.8134313225746155	48603		europeans	0.6863097548484802	443
		afghans	0.811549723148346	910		chinese	0.6833248138427734	427
		right-wing extremists	0.8033667802810669	364		imams	0.6803208589553833	154
UK
	192421887	christians	0.9270460605621338	2803	203963601	jews	0.89826500415802	1677
	9360	jews	0.8958547711372375	2994	2794	christians	0.8933975696563721	1162
		non-muslims	0.8865492343902588	523		immigrants	0.8299763202667236	2286
		sikhs	0.836709201335907	262		catholics	0.8161314725875854	507
		catholics	0.8324958086013794	513		americans	0.7829331159591675	5166
		islamists	0.8283140063285828	279		extremists	0.779312014579773	1381
		hindus	0.8251689076423645	378		kurds	0.776415228843689	445
		immigrants	0.821823000907898	2637		arabs	0.7723388075828552	189
		arabs	0.8212320804595947	695		palestinians	0.7685337066650391	1613
		feminists	0.8154028654098511	1160		afghans	0.7651612758636475	175
		foreigners	0.8149660229682922	1011		iranians	0.7508352994918823	367
		people	0.8105340003967285	285566		mexicans	0.7471039295196533	173
		americans	0.8037158250808716	5146		refugees	0.7466779947280884	4746
		shias	0.8035663366317749	109		terrorists	0.7463822364807129	2725
		terrorists	0.7984691858291626	2691		australians	0.736822247505188	1060
		europeans	0.7979004383087158	940		canadians	0.7361315488815308	389
		israelis	0.795586109161377	495		dissidents	0.7356256246566772	190
		extremists	0.7914788722991943	844		hindus	0.7335975170135498	160
		moslems	0.78607577085495	123		iraqis	0.7305430173873901	157
		somalis	0.7858065366744995	83		rohingya	0.7296386361122131	1771
		pakistanis	0.7835325598716736	253		syrians	0.7241469621658325	426
		racists	0.7801774740219116	566		jihadists	0.7228063344955444	404
		sunnis	0.7776196002960205	99		foreigners	0.7206810712814331	787
		homosexuals	0.7750779390335083	437		buddhists	0.7195806503295898	72
		believers	0.7740861177444458	838		people	0.7180768847465515	297766
		liberals	0.7732034921646118	1330		migrants	0.7179949283599854	5141
		syrians	0.7709720134735107	337		voters	0.7150535583496094	8727
		leftists	0.7686876654624939	328		civilians	0.7143624424934387	2901
		fascists	0.7672505378723145	277		nigerians	0.7118391990661621	340
		palestinians	0.7665683031082153	1405		militants	0.7104461193084717	2271