Hate Speech in Times of Crises: a Cross-Disciplinary Analysis of Online Xenophobia in G reece (2025)

Abstract: Bridging NLP with political science, this paper examines both the potential and the limitations of a computational hate speech detection method in addressing real-world questions. Using Greece as a case study, we analyze over 4 million tweets from 2015 to 2022—a period marked by economic, refugee, foreign policy, and pandemic crises. The analysis of false positives highlights the challenges of accurately detecting different types of verbal attacks across various targets and timeframes. In addition, the analysis of true positives reveals distinct linguistic patterns that reinforce populist narratives, polarization and hostility. By situating these findings within their socio-political context, we provide insights into how hate speech manifests online in response to real-world crises.

## introduction although hate speech predates the digital erahaving historically served as a means of reinforcing stereotypes and dehumanizing individuals or groups, often leading to discrimination, marginalization, and, in extreme cases, genocide (graham and smith, 2024)-its manifestation in online spaces has significantly amplified both its reach and impact, fueling polarization and contributing to the erosion of democratic discourse @xcite . social media platforms, by affording users a degree of anonymity, often reduce accountability, thereby enabling the open expression of prejudiced views @xcite and hostile discourse, which in turn entrenches social and ideological divisions. τhe normalization of hate speech by influential figures (e.g., political leaders) has further legitimized hateful rhetoric, embedding it within mainstream discourse and leading to farreaching societal consequences, particularly in polarized or crisis-driven contexts; online platforms can serve as catalysts for offline violence, as exemplified by the january 6th attack on the u.s. capitol @xcite . similarly, the surge in online hate speech in europe has been linked to the refugee crisis @xcite and has coincided with a documented rise in anti-refugee hate crimes @xcite . during periods of crisis or perceived threat, there is a common tendency to scapegoat outgroups blaming them for societal problems and uncertainties which serve to activate and amplify stereotypes and prejudices @xcite . this pattern was evident during the covid-19 pandemic, which saw a surge in anti-asian sentiment on social media platforms @xcite . more recently, the israel-hamas conflict has triggered a global rise in both islamophobic and anti-semitic narratives, reflecting the reactivation of deep-seated prejudices @xcite . nlp research has made significant progress in detecting various aspects of hateful content (e.g., @xcite @xcite @xcite laying the groundwork for targeted interventions such as moderation, debiasing, and counter-speech @xcite . recent advances in llms can improve performance and interpretability, enabling more nuanced hate speech analysis (e.g., @xcite . however, realworld application of hate speech detection remains challenging. unlike many other nlp tasks, it is culturally sensitive @xcite , as hate speech is deeply embedded in the sociocultural contexts in which it emerges @xcite . ethically, it requires careful consideration of the risks involved in labeling communicative practices as hate speech, particularly for the communities implicated in such research @xcite . therefore, models must be rigorously validated to ensure they accurately capture complex social issues-especially since falsepositive errors can inadvertently censor online speech and further marginalize specific groups @xcite . a further challenge arises from the differing goals of nlp and social sciences @xcite . nlp focuses on developing new computational systems or improving existing ones, so it is important that these are evaluated on standard datasets using reproducible methods which, however, are optimized for restricted datasets and languages -most of them in english @xcite . social scientists seek insights stemming from research questions that are formulated using constructs relevant to their fields and not in terms of nlp. this divergence highlights the need for interdisciplinary approaches that tailor computational tools to domain-specific questions and real-world complexities @xcite . in this paper, we apply a rule-based nlp method on real-word questions in the context of political science research. taking greece as a case study, we present a large-scale yet finegrained analysis of online verbal aggression (va) targeting key groups: albanians and pakistanis (the largest migrant communities), muslims and jews (significant religious and ethnic minorities), and migrants and refugees (both statuses of foreign population). using a publicly available va analysis tool for the greek language @xcite we analyze over 4 million tweets posted on greek twitter/x between 2015 and 2022. greece is one of the few countries which experienced the concomitant turmoil of four different crises @xcite : a financial, a refugee, a foreign policy crisis (e.g., tensions with neighboring turkey), and the covid-19 pandemic crisis. in this context of polycrisis, beyond assessing the tool's performance, our study addresses two key research questions (rqs): rq1: which groups, situated within specific socio-political contexts, were the primary targets of hate speech on greek twitter/x during the examined period(s)? rq2: are there target-specific linguistic patterns, prejudices, or stereotypes? the contribution of our work is two-fold: first, we provide both quantitative and qualitative analyses of false positives, highlighting the challenges of accurately detecting different types of verbal attacks across various targets and timeframes with regard to domain-specific rqs. second, our analysis of the fluctuation and content of detected verbal attacks uncovers key linguistic patterns that reinforce populist narratives, polarization and hostility in greek online discourse. by contextualizing hate speech within real-world grievances and sociopolitical tensions, our findings illuminate how hate discursive patterns manifest, evolve, and interact with broader crises. furthermore, our findings can provide a framework for informed countermeasures and deeper exploration of the link between online aggression and offline political violence, particularly in times of crises. ## background most nlp approaches treat hate speech detection as a binary (e.g., @xcite or multiclass classification task (e.g., @xcite , typically relying on explicit linguistic cues. recent research has shifted toward addressing implicit hate speech @xcite , identifying different types of verbal attacks @xcite , analyzing group-specific targeting @xcite @xcite , and using free-text annotations to better capture the pragmatic implications of hateful messages @xcite . computational approaches focusing on the greek language include the development and evaluation of classifiers for tasks such as offensive tweet detection @xcite and abusive content moderation in user comments @xcite . perifanos and goutsos (2021) proposed a multimodal approach that combines computer vision and nlp to detect abusive contexts in tweets targeting refugees and migrants. pontiki et al. (2018; 2020 ) employed a linguisticallyinformed rule-based framework to identify and categorize specific forms of va-such as criticism, swearing, and calls for oustingagainst predefined minority and migrant groups on twitter. arcila-calderón et al. (2022) developed both shallow and deep learning models for detecting online anti-immigration hate speech in spanish, greek and italian. their models are incorporated within the pharm project interface @xcite developed with the goal to monitor and model hate speech against refugees and migrants in greece, italy, and spain. political science literature on xenophobia examines primarily fears and hostility towards 'foreigners', focusing on the motives and mechanisms of their mobilization mostly by populist-radical and far-right parties @xcite . it also explores the institutionalization of xenophobia through public policies on immigration and securitization @xcite . a notable gap persists in the academic discourse: while much attention has been paid to persistent forms of "non-violent discrimination" @xcite , less attention has been devoted to xenophobia "as a violent practice" @xcite . xenophobic attitudes in greece are fluid and context-dependent, historically targeting socio-economically marginalized-during the period first entered the country-migrants like albanians and pakistanis, while directing hostility at perceived "dominant" outgroups such as jews @xcite . although political motivated violence is grounded in the history and culture of greece, in constant presence since the transition to democracy in 1974 @xcite , antiimmigrant and xenophobic violence marks unprecedented levels during the financial and the refugee crises. τhe rise of the neo-nazi party golden dawn (gd) in the context of the economic crisis normalized anti-semitic and xenophobic discourse in mainstream politics @xcite . gd managed to emerge as the third largest party in the national elections of 2015, despite an ongoing judicial investigation into its involvement in violent attacks mostly against migrants and refugees. the rise of gd, which has not entered the national parliament since 2019, played a central role in mobilizing hate narratives and coordinating street-level violent attacks against 'foreigners' and left-wing activists, particularly during the 2015 refugee influx in the midst of economic crisis @xcite . during this period, alarmist coverage in traditional and social media amplified economic and cultural fears, reinforcing exclusionary attitudes toward refugees and other minorities. an analysis of 504 incidents of far right violence registered from 2008 to 2019 revealed high-escalation attacks primarily targeting humans, highly correlated with the fear of economic losses, sensitive to increases in immigration flows and fuelled by the representation of extremist parties in parliament @xcite . ## analysis of true positives the analysis of true positives includes the calculation of the va rate (i.e., number of verbal attacks per total tweets) for each tg, enabling the identification of the primary targets of twitter-based verbal attacks over the full period examined. we also perform a yearby-year analysis of va rates to track their evolution over time and identify potential peak periods. additionally, we investigate the fluctuation of individual va types per tg to gain further insights into the variation of verbal attacks both temporally and within each group. below, we present our findings in relation to the rqs; due to space limitations, most visualizations are included in appendix a. in line with previous research @xcite , our results suggest that groups framed as migrants are more likely to be verbally attacked than those framed as refugees, likely due to the differing connotations and sociopolitical implications associated with these two lexicalizations. the per year va rates (fig. 4 ) indicate a global increase of attacks from 2018 onwards. the increased rate of the attacks against migrants can be possibly attributed to the ongoing refugee crisis and mainly to the fact that the effect of this crisis has started to be tangible in greek society, especially at the severely overcrowded camps on the islands. there might also be a noticeable time lag between the actual processes of events and the discursive articulation of them suggesting a delay in verbalization of them in public and social media discourses @xcite . as greece officially exits the bailouts in the summer of 2018, increases in va against migrants over the same period might as well reflect a shift in the prioritization of perceived scapegoats for grievances and backlashes. the highest peaks for most tgs are nonetheless observed in 2020, an evolution certainly fueled by a series of migration-related critical incidents which occurred in evros in february and in lesvos in march and september, all of which increased and sustained the salience of immigration in the public sphere, prompting the government to adopt a tougher stance than its predecessor @xcite . greece was further placed in strict lockdown in march 2020. due to the restrictions on physical contact during the covid-19 pandemic, the internet came to function as the principal means of expression and communication. verbal attacks expressing criticism constitute the main type of va detected in our datasets, and are mostly directed against muslims, jews, albanians and refugees. pakistanis and migrants receive the most obscene messages. pakistanis are mostly evaluated as inferior beings with derogatory morphological variations of their nationality name (e.g., pakistania). the main recipients of ironic tweets are pakistanis, jews and refugees. calls for ousting constitute the third most frequent type of va targeting mostly pakistanis and migrants. pakistanis are also the main targets of messages calling for physical violence (fig. 10 ), a finding which highlights the association of the previous dehumanizing discourse towards this specific tg with the calls for real-world violence against it. focusing on criticism (fig. 6 ), the yearly distribution for each tg indicates a relatively stable frequency, highlighting it as the most consistently used linguistic strategy to target them throughout our datasets. significant peaks and drops are observed only for pakistanis and refugees. however, the decrease of criticism rates does not indicate a decrease of va against them in general, but rather a shift in the va type, with aggression moving from criticism towards swearing (fig. 7 ) and irony (fig. 8 ). fluctuation of swearing rates is not only observed across time, but also within each tg. compared to criticism, swearing is more emotional, driven by spontaneous reactions and strong feelings such as anger or contempt, e.g., as reactions/responses to news reporting crimes committed by pakistanis. ousting messages (fig. 9 ) display two significant peaks for pakistanis-in 2016 and 2020. the populist radical right party named greek solution, founded in 2016, launched a hardline antiimmigrant narrative, in which greece shall be a "fortress" without migrants. interestingly, the increase in 2020 relates to official online party discourse of the greek solution in 2020, whereas it is also associated with an ongoing crisis with turkey during the same period @xcite . another interesting peak is the one for albanians in 2018, which according to the qualitative analysis is mostly related to the murder of konstantinos katsifas, a member of the greek minority of northern epirus at the southern part of albania, who was killed by the albanian police. gd members were asking for mass deportations of albanians. finally, we can see a significant increase of ousting messages targeting refugees in 2021, triggered by online debates on toughening policies of other eu countries towards syrian refugees. the qualitative analysis of the attacks reveals that albanians are mainly framed as murderers indicating a continuity of the so-called stereotype of the balkanian criminal. another frequent set of attacks entails the perception of albanian nationalism and a particular enmity towards the greek nation. pakistanis are also frequently associated with crime (particularly sexual violence). the most frequent term in the construction of the image of muslims is the word fanatic; the attacks against them are often lexicalized through evaluative and dysphemistic terms of insult or abuse to debase core islamic values, practices, etc. indicating irrationalism, sexist behavior and fanaticism, and framing them as terrorists. jews are mainly framed as greek haters with the attacks against them entailing the perception of enmity towards the greek nation and christianity and blame attribution patterns (e.g., for the greek crisis). this rhetoric and mobilization drew on a symbolic competition between perceived victimised ingroups and outgroups, wherein the targeting of jews served to fuel antisemitic stances @xcite . the most frequent verbal attacks against migrants and refugees challenge their identity, framing them as illegal. this rhetoric aims to undermine their legitimacy, humanity, and right to asylum by questioning their motives, authenticity, and cultural compatibility. in this context, they are frequently framed as economic, cultural and national security threats. ## conclusions analyzing over 4 million greek tweets from 2015 to 2022-a period marked by overlapping crises-we assessed the effectiveness of a publicly available rule-based system in detecting different types of verbal attacks against various tgs. our findings indicate that while the tool performs well in terms of precision for explicitly targeted groups in our datasets, it struggles with more contextually complex content, particularly tweets involving jews and refugees. the high rate of false positives in these cases often results mainly from satire, irony, or quoted hate speech that aims to critique rather than endorse discriminatory views. moreover, instances of aggression and hate speech embedded in discourse that ostensibly aims to combat hate further complicate the analysis. despite its limitations, the method offers valuable insights into the ways hate speech manifests online in greece in response to realworld grievances and crises. yearly fluctuations in precision and verbal attack rates reflect the impact of external socio-political developments and shifts in public discourse. our research among others has shown the frequent association of the va against migrants with criminality; that dehumanizing specific ethnic groups also renders those groups as targets for physical harm; that the bundled crises in the period under study scapegoated migrants and refugees for grievances and insecurities triggered by crises unrelated to them. ultimately, our analysis underscores the need for interdisciplinary approaches that adapt computational tools to sociopolitical contexts, and incorporate human oversight-crucial for capturing nuance, intent, domain-specific and cultural specificities in language use, as well as the ethical ambiguities of hate speech detection.