Asian Communication Research
[ Original Research ]
Asian Communication Research - Vol. 21, No. 3, pp.457-474
ISSN: 1738-2084 (Print) 2765-3390 (Online)
Print publication date 31 Dec 2024
Received 23 Apr 2024 Revised 03 Sep 2024 Accepted 02 Oct 2024
DOI: https://doi.org/10.20879/acr.2024.21.029

Tracing the Roots of Online Hate Speech: Exploring Outgroup Prejudice

Ee-Sun Kim1 ; Minjeong Kim2
1Department of Sociology (BK21 FOUR), Yonsei University
2Division of Media and Communication, Hankuk University of Foreign Studies

Correspondence to: Minjeong KimDivision of Media and Communication, Hankuk University of Foreign Studies, (02450) 107 Imun-ro, Dongdaemun-gu, Seoul, Republic of Korea. Email: mkhufs@gmail.com

Copyright ⓒ 2024 by the Korean Society for Journalism and Communication Studies

Abstract

Online hate speech has become a pressing issue in the era of information and communication technologies. This study examines the psychological roots of prejudice in online hate speech from the perspectives of contact theory and intergroup competition theory. By analyzing social survey data collected from 1,000 Korean online users in 2022, this study finds that social network size and online hate speech experiences influence prejudice, emphasizing how interactions and perceived competition shape attitudes toward out-groups. According to the results, social network reduced prejudice against people from different regions or with different religions, but increased prejudice against groups perceived as competing for resources, such as immigrants or feminists. These findings show that the mechanism linking contact and hate may vary depending on the specific context -particularly in the context of resource competition. At the same time, they suggest which groups are at risk of becoming targets of prejudice in contemporary Korean society. These findings suggest that eliminating hate requires more than intergroup interactions or policies alone; addressing underlying existential threats and fostering empathy are crucial.

Keywords:

hate speech, prejudice, contact theory, competition theory

Online hate speech has become a pressing issue in the era of information and communication technologies. The global nature of the Internet facilitates the spread of these harmful messages across borders, making it a worldwide concern (O’Regan, 2018). This pervasive problem thrives in part because of online anonymity. This allows individuals to express hateful views without revealing their true identities and without being held responsible for their actions (Barlett et al., 2016; Lee, 2021). Online anonymity also enables individuals to bypass social norms and legal constraints that typically regulate face-to-face interactions.

In addition, the structure of online platforms, driven by complex algorithms, significantly contributes to the proliferation of hate speech (Goel et al., 2023). These algorithms often create echo chambers, where users are predominantly exposed to information that aligns with their preexisting beliefs and biases. This reinforcement of existing prejudices can lead to the amplification of hate speech within closed online communities. In such environments, dissenting opinions are often filtered out, leading to a homogenous and polarized discourse that can escalate into more extreme forms of hate speech.

To effectively combat online hate speech, it is crucial to gain a deeper understanding of the underlying prejudices that fuel these harmful behaviors. Prejudice, as a psychological concept, has been extensively studied in various contexts. Gordon Allport’s seminal work, “The Nature of Prejudice,” provides a foundational framework for understanding the different levels of prejudice. Allport categorizes prejudice into five levels, ranging from antilocution to extermination (Allport, 1954). The first level, “antilocution,” specifically refers to verbal expressions of prejudice, which encompass hate speech.

Allport's theory also emphasizes the natural human tendency to categorize individuals into in-groups and out-groups (Allport, 1954). Ingroups consist of people with whom individuals identify and feel a sense of belonging, while outgroups comprise those perceived as different or outside the circle of familiarity. This categorization often leads to in-group favoritism and out-group derogation, with hate speech serving as a tool to assert the perceived superiority of the in-group over the out-group. In essence, hate speech is not just a simple expression of like or dislike, it is an expression of a belief that one's group is inherently superior to another. To understand the mechanisms behind hate speech, we need to understand how people's group categorization and prejudice work.

Driven by this discern, this study draws on contact theory and intergroup competition theory and aims to examine the impact of online hate speech experiences on outgroup prejudice. The main theories explaining the factors that influence outgroup prejudice have been developed based on Allport's work. First, contact theory posits that direct and personal interactions between members of different groups can reduce prejudice by promoting mutual understanding and empathy (Pettigrew & Tropp, 2006). Increased interpersonal contact is believed to diminish social distance and counteract negative stereotypes perpetuated by media and societal narratives. Conversely, a lack of such contact can lead to the formation and reinforcement of inaccurate and hostile attitudes toward out-groups.

Intergroup competition theory, on the other hand, suggests that prejudice arises from competition between groups for limited resources and opportunities (Sherif, 1966). When minority groups, such as immigrants, are perceived as threats to the economic and social interests of the majority, it can lead to heightened prejudice and discrimination. This theory highlights the role of perceived competition in fostering negative attitudes and behaviors toward out-groups.

Contact theory and intergroup competition theory suggest that understanding the prejudices underlying hate requires a very specific approach: it is necessary to explain the mechanisms by which prejudice is formed based on the context of each society and its members' perceptions of the outgroup and discrimination. Grounded in these theories, the study aims to examine how social networks and prejudice levels operate within the specific context of South Korean society in the 2020s.

South Korea is currently undergoing major social and demographic changes. Korean society has gone through a process of rapid national development referred to as 'compressive modernization'), which has resulted in various social fissures (Chang, 2010). In addition, new dynamics are affecting the social structure due to the increasing influx of migrants in the context of globalization (Lim & Song, 2021). These changes have led to increased social diversity.

While diversity can lead to greater tolerance and cultural enrichment, it also has the potential to generate prejudice and discrimination, particularly when rapid social changes outpace the development of inclusive attitudes and policies (Berry, 2005). South Korean society is also grappling with polarization on various fronts, including political, social, and economic issues (Han, 2022). This polarization can exacerbate feelings of insecurity and competition, further fueling prejudiced attitudes and hate speech. By examining the specific context of South Korea, this study aims to provide insights into the broader mechanisms of prejudice and hate speech in a rapidly changing society.

This study also seeks to offer valuable policy implications. By identifying the factors that contribute to the development and perpetuation of hate speech, policymakers can design targeted interventions to mitigate these issues.


ONLINE HATE SPEECH AND PREJUDICE

In order to understand how the factors that influence members' prejudice against perceived social sub-groups in Korean society, this study references two theoretical frameworks: Contact Theory and Intergroup Competition Theory. These theories provide a comprehensive framework for understanding the dynamics of prejudice and the factors that can either exacerbate or mitigate it. Grounded in these theories, the study aims to examine how social networks and prejudice levels operate within the specific context of South Korean society in the 2020s. To address this objective, the paper poses a central research question and develops three sub-hypotheses to investigate it. Each hypothesis is supported by a review of relevant theories and previous research.

  • RQ: How does contact between specific groups influence levels of prejudice in South Korean society in the 2020s?

Contact Theory and Prejudice

First, contact theory suggests that direct and personal contact between members of different groups can weaken prejudice and promote more accurate perceptions of out-groups. This theory, originally proposed by Gordon Allport in 1954, has been supported by numerous studies over the decades (Cheong et al., 2011; Silverstein & Flamenbaum, 1989). According to contact theory, increased interpersonal contact is believed to reduce social distance—the emotional and psychological gap between different groups. This reduction in social distance can help mitigate the effects of negative media stereotypes, which often portray out-groups in a biased or unfavorable light (Roh, 2013; Silverstein & Flamenbaum, 1989).

For instance, when individuals have direct, personal interactions with members of an out-group, they are more likely to see them as individuals rather than as stereotypical representatives of a group. This personalization process helps in breaking down negative stereotypes and building more nuanced and accurate perceptions. Moreover, such contact can foster empathy and understanding, further reducing prejudicial attitudes. Conversely, a lack of personal contact may result in a shortage of information and experience about the other group. This lack of direct knowledge can lead to the formation of inaccurate attitudes and reinforce existing negative stereotypes (Tan et al., 1997).

The media often plays a significant role in shaping public perceptions of different groups. Negative media portrayals can create and perpetuate stereotypes, leading to increased prejudice (Ahmed et al, 2021). However, personal contact with out-group members can counteract these media-induced stereotypes (Roh, 2013; Silverstein & Flamenbaum, 1989). For example, if media frequently depicts a particular ethnic group as prone to violence, individuals who have positive personal interactions with members of that group are less likely to believe such stereotypes (Ahmed, 2017). Therefore, this study posits that direct personal contact reduces prejudice, as it allows for the humanization of out-group members and challenges the simplistic and often negative portrayals presented in the media.

  • H1: Direct, personal contact with members of out-groups will be negatively associated with levels of prejudice against these groups.

However, the effects of such contact are likely to vary depending on the specific groups involved. Allport (1954) discussed that certain conditions must be met for contact to reduce prejudice: equal status among group members, common goals, and institutional support. Consequently, extensive empirical research has developed on what types of contact reinforce or weaken prejudice (Kwon, 2024; Pettigrew & Tropp, 2006). If these conditions are not met, studies have reported that increased contact between outgroups can lead to heightened anxiety, thereby reinforcing prejudice (For example: Park, 2015, p. 84; Roh, 2013). The following section will introduce conflict theory to examine how intergroup competition can strengthen prejudice and discuss its implications for contemporary South Korean society.

Intergroup Competition Theory and Prejudice

Second, intergroup competition theory can provide another lens through which to examine the dynamics of prejudice. This theory proposes that when different groups compete for limited resources and opportunities, it can lead to conflict, prejudice, and discrimination. The theory suggests that as minority groups, including immigrants, increase in number, the majority group may perceive them as a threat to their own interests and well-being (Quillian, 1995; Scheepers et al., 2002). This perceived competition can trigger negative attitudes and behaviors towards the minority group.

Intergroup competition theory is rooted in the realistic group conflict theory (Hwang et al., 2007; Joo & Im, 2022; Seok, 2023; Sherif et al., 1988), which posits that competition for tangible resources such as jobs, housing, and social services can lead to intergroup hostility. For instance, if members of a majority group believe that immigrants are taking away job opportunities, they may develop negative attitudes towards immigrants, viewing them as competitors rather than fellow community members. This perception of threat can be exacerbated during times of economic downturn or resource scarcity, where the competition for limited resources becomes more intense.

Building on this theory, this study predicts that prejudice will increase when individuals are perceived as rivals competing for limited resources and opportunities. The perceived threat to the majority group's social and economic standing can lead to heightened levels of prejudice and discrimination against the minority group. This perspective highlights the need to identify which groups are perceived as instigators of social competition or threats within that society. This study aims to determine which groups are recognized as competing in South Korean society in the 2020s through their relationship with prejudice variables.

Furthermore, the media can amplify perceptions of intergroup competition by highlighting stories of conflict and competition between groups. For instance, news reports that focus on job losses among the majority population due to the influx of immigrant workers can fuel perceptions of competition and threat. These media narratives can reinforce existing prejudices and make it more difficult for positive intergroup relations to develop.

  • H2. Perceived competition for limited resources and opportunities will be positively associated with levels of prejudice against out-groups.

The answer of H2 is going to be linked to the research question of this paper (RQ) and will demonstrate which groups are perceived as competing in South Korean society in the 2020s, thereby illustrating the negative relationship between contact and prejudice. This will allow for a specific examination of the empirical status of the relationship between contact and prejudice in South Korea.

Impact of Online Hate Speech on Prejudice

Finally, this study examines the impact of being targeted by online hate speech on prejudice levels. Online hate speech can have a profound psychological impact on its victims, leading to increased levels of stress, anxiety, and fear. These psychological effects can, in turn, influence attitudes toward out-groups. When individuals are subjected to hate speech, they may develop heightened sensitivities and defensive attitudes, which can exacerbate existing prejudices.

The experience of being targeted by online hate speech can also drive individuals to seek support from their in-group, further entrenching ingroup biases and increasing prejudice against outgroups. This phenomenon can be understood through the lens of social identity theory (Tajfel, 1982; Tajfel & Turner, 2000), which suggests that individuals derive a sense of identity and selfworth from their group memberships. When their group is under attack, individuals may rally around their in-group, leading to stronger in-group cohesion but also increased hostility towards out-groups.

Moreover, online hate speech often involves dehumanizing and derogatory language, which can strip the target of their individuality and reduce them to negative stereotypes (Citron, 2014; Wachs et al., 2022). This dehumanization process can make it easier for individuals to justify their prejudiced attitudes and discriminatory behaviors (Citron, 2014; Kopytowska & Baider, 2017).

Therefore, this study posits that being a target of online hate speech increases prejudice against outgroups. The harmful effects of online hate speech extend beyond the immediate psychological impact on the victims; they also contribute to the broader societal problem of prejudice and discrimination. Understanding the mechanisms through which online hate speech influences prejudice is crucial for developing effective interventions to combat it.

  • H3. Experiencing online hate speech will be positively associated with increased levels of prejudice against out-groups.

In summary, the theoretical framework of this study combines contact theory and intergroup competition theory to provide a comprehensive understanding of the factors that influence prejudice. By examining the role of personal contact, perceptions of competition, and the impact of online hate speech, this study aims to shed light on the complex dynamics of prejudice and offer insights into potential strategies for reducing it.


METHODS

Data

This study collected data through a survey titled “A Survey of Korean Citizens’ Perception on Online Hate Speech,” which was conducted in February 2023 with the assistance of a polling company, M-Lab.1,2 The survey aimed to gather detailed information on the perceptions and experiences of online hate speech among Korean adult internet users. This data was compiled from responses of volunteers participating in a survey conducted by M-Lab, using a respondent pool representative of the adult population in Korean society. Quota sampling was conducted to ensure that the sample reflects the overall population distribution. The dataset included responses from 1,000 Korean adult online users. The distribution of the sample data by gender and age, along with the distribution of the population, is presented in Table 1.

2023 Population Distribution of Data (population in 20’s-60’s)

Measures

Prejudice

The dependent variable in the study is prejudice. Prejudice starts with categorizing people in society based on a single characteristic. When people objectify a group of people and condemn that group, prejudice exists. Prejudice is defined in various ways across studies, and psychological research that identifies its components and mechanisms measures prejudice multidimensionally (Olson, 2009). Nevertheless, it is common to view prejudice as involving the categorization of specific groups and their negative evaluation. In particular, there is a tendency to perceive outgroup members as homogeneous rather than as diverse individuals, and to attribute negative traits or blame to these groups (Kwon, 2024, p. 4; Seo, 2024, p. 547; Seok, 2023, p. 137) . Based on this discussion, this study employs the following items as proxy variables for measuring prejudice succinctly. The prejudice variable was measured by asking respondents to provide ‘yes’ or ‘no’ responses to the statement “OO deserves to be blamed,” where “OO” represents twelve distinct groups, such as immigrants, people with disabilities, and others. These groups were carefully selected to cover a broad spectrum of societal segments often targeted by prejudice. These twelve groups are also those that have been discussed as easily targeted by hate speech in Korean society (Kakao, 2021). The dependent variable- level of prejudicewas determined by counting the number of 'yes' responses to these statements for each respondent, thereby quantifying their prejudice levels. The more people are targeted, the higher the level of prejudice.

Social Network Size

The study's main independent variables include (1) social network size and (2) the experience of being the target of online hate speech, referred to as online attack experience. The social network size variable was assessed by asking respondents how many people they personally acquainted with each other in each of the twelve mentioned groups. The responses were measured on a scale from 0 (none) to 4 (more than 6 people). This variable aims to capture the extent of personal contact and exposure respondents have with members of the groups in question. Many empirical studies on contact theory suggests that greater personal contact can reduce prejudice by fostering understanding and empathy (Ahmed, 2017; Pettigrew et al., 2007). However, these effects are not always universal. Researchers such as Allport (1954) and Pettigrew and Tropp (2006) have argued that prejudice reduction can be expected when contact occurs on an equal footing and in an interdependent manner. In addition, intergroup competition theory suggests that intergroup contact can lead to conflict in situations of intergroup resource competition (Kim et al., 2011; Sherif et al., 1988). In order to fully explore the complexity of these contact effects, this study has conducted the analysis in two steps. First, individual networks were included in the model. Second, based on the variable effects and prior research on South Korean society, those networks were classified into three types and reintroduced into the second model. The specific details are presented in Table 2 below.

Social Network Components of Contact Group Types

The network variables for each group were standardized by adding up the number of “acquaintances” in that group and then normalizing the values.

Online Attack Experience

The experience of being the target of online hate speech is referred to as online attack experience. This variable was quantified by nine items, where respondents were asked if they had ever been the target of online hate speech because of certain aspects of their identity. These aspects included gender, age, sexual orientation, place of origin, nationality/race, economic status, disability/illness, religion, and education level. Responses were recorded on a scale from 0 (no experience) to 4 (all the time), providing a detailed measure of the frequency and intensity of hate speech experiences. This variable is crucial for understanding the psychological impact of online hate speech and its potential to reinforce prejudicial attitudes.

Control Variables

Additionally, the study incorporated several control variables to account for other factors that might influence prejudice. These control variables included gender, age, education, economic status, political orientation, and online media use. By including these controls, the study aimed to isolate the effects of the main independent variables more accurately. Additionally, the respondents' group affiliation was intended to reflect whether they belong to an outgroup as a control variable. For this purpose, identity related to health and sexual orientation identity variables were included as control variables, in addition to the sociodemographic variables previously mentioned.

Model of Analysis

The analytical approach employed in this study involved the use of a negative binomial regression model. This model was chosen because the dependent variable, prejudice, exhibits characteristics of a count variable and is overdispersed—meaning that the variance exceeds the mean. Count variables are those that represent the number of times an event occurs, and in this study, it refers to the number of ‘yes’ responses indicating prejudice. Overdispersion is a common issue in count data and can lead to inefficient estimates if not properly addressed.

The dependent variable, ‘level of prejudice’, has a skewed distribution as shown in Figure 1.

Figure 1.

Distribution of the Prejudice LevelNote. Unit of the analysis: Person

To ensure proper model estimation and validation, we validated the presence of overdispersion as follows. In addition, the null hypothesis of “mean and variance are equal” assuming a Poisson distribution was tested, and the null hypothesis was rejected at a significance level of.001 (z = 8.61), and a dispersion value of 3.03 was obtained (based on the final model). Since the null hypothesis of a Poisson distribution was rejected, this study adopted a negative binomial model to analyze the data. This is because the negative binomial model is a popular alternative when a Poisson model cannot be used to estimate a regression model for additive data (Cox, 1983). The model helps in understanding how the independent variables, particularly social network size and online attack experience, influence the level of prejudice among the respondents.


RESULTS

Descriptive Statistics

The basic descriptive statistics of each variable are presented in Table 3.

Sociodemographic Characteristics of the Survey Respondents (N = 1,000)

The data includes a total of 1,000 respondents, comprised of adults aged 18-69. 51% of the respondents are male and 49% are female. Looking at the distribution of education, 68% of respondents were enrolled in college or had graduated from college, and 12.2% were enrolled in graduate school or had graduated from graduate school. 19.8% have a high school diploma or less. The distribution of economic class consciousness averaged 2.65 (SD = 0.79) on a scale of 1 for the lowest class and 5 for the highest class. Political affiliation averages 2.98 (SD = 0.75) on a scale with 1 being very conservative, 5 being very liberal, and 3 being moderate. Respondents spent an average of 287 minutes per day on online media. Of the 10 types of online hate speech, the average number of times they experienced an attack was 1.83 (SD = 2.45).

The extent to which the respondent knows people from a particular group in their social network was measured on a scale of 1 to 4, ranging from “none” to “six or more.” Looking at the scale scores for each of these networks, the lowest scoring group is the LGBTQ+ category (M = 1.19, SD = 0.51), which can be interpreted as the least visible group in the respondent's social network. Other groups with low scores include feminists, foreigners, the economically disadvantaged, and people with disabilities. The highest scoring groups were people from other regions (M = 2.88, SD = 1.05), people of different religions, and young adults.

The correlations between each variable are presented in Table 4.

Intercorrelations of Indicators

Af ter model estimation, we checked multicollinearity with the VIF index and found that there was no problem with the criterion of VIF < 5.0.

Impact of Social Network Size on Prejudice

The first-level results are summarized in Table 5. This table presents the coefficients showing the influence of each social network size on prejudice level. Each estimated value is derived from the individual analysis when the respective social network variables were included alongside the control variables.

The Coefficient Effects of the Network Variables

The results show that the direction of coefficients varies depending on the network with which group members are connected. Among the statistically significant variables, contact with foreigners, the impoverished, feminists, and the LGBTQ+ community shows positive (+) values, meaning that as contact increases, levels of prejudice also rise. On the other hand, contact with people from other regions, those of different religions, and young people in their 20s and 30s yields negative (-) values, indicating that as contact increases, levels of prejudice decrease.

It indicates that, also in South Korean society in the 2020s, contact with outgroups does not solely function to reduce prejudice. Previous researches on outgroups suggest that when people are perceived as competing groups or when members experience status anxiety, contact tends to reinforce discrimination and hatred. Based on this discussion, this study categorized the nine social networks into three groups. The results shown in Table 5, along with the context of contemporary South Korean society, allowed for the classification of social networks into the following three categories: (1) traditional welfare recipient groups, the so-called beneficiary groups, (2) emerging competing groups, and (3) diversity groups (see Table 2).

First, the beneficiary groups are categorized as individuals with disabilities, seniors aged 70 and older, and those living in poverty. These groups have traditionally been considered welfare recipients. In terms of coefficient effects, these groups show either no significant impact (individuals with disabilities, seniors aged 70+ or a positive value.

Second, the emerging competing groups are classified as foreigners and feminists. South Korea had been in low levels of foreign migration; however, the number of foreign workers and immigrants is steadily increasing. This influx is often perceived as bringing diverse vitality to society. However, South Korea, like other societies, faces issues of prejudice and discrimination against foreigners. In this discourse, foreigners are frequently associated with “job scarcity” and “increased crime” (Bang et al., 2010).

The feminist network is categorized as competing group based on the emerging gender issue in South Korea. Studies on hate speech indicate that since the 2010s, “feminism” has emerged as a new keyword in gender conflicts (Jung & Cho, 2021; Park et el., 2020). Notably, the sentiment of “anti-feminism” is often linked to the perception that men are “suffering losses” (Seok, 2023). Research by Lee and Kim (2021), which performed topic modeling analysis on hate discourse in post-COVID-19 South Korea, reveals that feminism and immigrants are uniquely associated with job-related keywords, unlike other social minor groups.

Third, other groups, including people from different regions, different religious backgrounds, young adults, and LGBTQ+, are classified as ‘diversity group’. These groups are not traditional welfare recipients and are not currently perceived as competitors for jobs in Korean society. Furthermore, except for the LGBTQ+ group, as shown in <Table 5>, these groups tend to exhibit reduced prejudice level with increased contact.

This classification is based on individual analysis results and social context; therefore, the following section will validate whether these three network types provide statistically adequate explanatory power.

Impact of Social Network Type on Prejudice

The final analysis of the research model is presented in <Table 6>. ‘Model 1’ is the base model with only control variables. ‘Model 2’ is the research model with explanatory variables. According to the LR test of the fit of the two models (-2LL), the fit of Model 2 is significantly improved compared to that of Model 1. Also, when comparing both AIC and BIC indicators, the value of Model 2 is smaller, so it can be evaluated that the introduction of explanatory variables has improved the research model appropriately.

Results of Base Model(1) and Final Research Model(2)

Table 6 also presents the regression coefficient values converted to exp(β), or ‘incidence rate ratio’ (IRR), for easy interpretation of the analysis results. If the IRR value is greater than 1, the number of occurrences of the dependent variable increases when the independent variable increases.

In this part, we first discuss the results for Hypotheses 1 and 2 of the study’s three hypotheses. Hypothesis 1 predicted that when outgroup members are present in an individual's social network of direct contacts, prejudice will decrease as those contacts increase. The study categorized the types of network contacts into rival groups, beneficiary groups, and diversity groups and tested the effect of each network variable. The variables that proved to be statistically significant were competitor group and diversity group, but their effect coefficients were in opposite directions.

For the diversity group, an increase in network contacts has a decreasing effect on bias. Interpreting the IRR values, a one-unit increase in network contacts for the diversity group reduces bias by 20%. In contrast, for the competing group, an increase in network contacts has an increasing effect on bias. A one-unit increase in network contacts to the rival group is associated with a 17% increase in bias. The beneficiary group effect fails to reject the null hypothesis. In other words, the effect of social network size on prejudice varied significantly depending on the type of network. These results provided robust support for both contact theory and intergroup competition theory, indicating that these frameworks are valuable for understanding the dynamics of prejudice.

First, the effect of the diversity group network variable is consistent with contact theory. This finding aligns with contact theory, which posits that increased personal contact with diverse groups can reduce prejudice by promoting more accurate and empathetic perceptions of outgroups. In this context, knowing more individuals from different regions or age groups likely facilitates positive interactions and reduces social distance, thereby mitigating prejudicial attitudes.

However, the study found that knowing more groups that respondents perceive to be in competition with them was linked to a significant increase in prejudice against this group. This outcome supports intergroup competition theory, which suggests that when different groups are seen as competing for limited resources, prejudice and conflict are likely to arise.

Foreigners and feminists, who have been categorized as competing groups, have recently been called the new conflict groups in Korean society. As the influx of foreigners continues to increase and the number of migrant workers has grown significantly, the perception that foreigners are taking jobs is spreading rapidly in Korean society. At the same time, there has been an increase in the number of demeaning and hateful remarks against foreigners. Although they may seem like very different groups, hostility toward feminism and women who advocate for it is also rooted in competition for social resources. While there is no direct discourse of “women taking jobs” as there is with foreigners, the perception that feminism and gender equality discourse threaten men’s socioeconomic status is rapidly spreading in South Korea. In other words, these groups are seen as threats to the existing social system's resource allocation order and as competitors in the resource allocation arena. They are positioned differently from the disabled, the elderly, and the poor, who have traditionally been the beneficiaries of the social welfare system. Social welfare recipient groups can also be considered competitors as groups that take social resources. Therefore, the coefficient effect of the beneficiary group variable is also positive. However, this coefficient fails to reject the null hypothesis at the p<0.05 level.

Taken together, these results highlight that an individual's prejudice against an outgroup is influenced by whether that group is perceived as a competitor for social resources. If a group is viewed as competitors for limited resources, this perception can fuel negative attitudes and discriminatory behaviors towards them. This finding supports the group competition theory. On the other hand, contact theory was more applicable to groups that are unrelated to resource allocation and not seen as beneficiaries of the social welfare system, such as individuals from different regions and younger age groups. Increased interpersonal contact with these groups was associated with reduced prejudice, supporting the idea that personal interactions can break down stereotypes and foster more positive perceptions.

The above analysis shows that the context of a society plays an important role in shaping prejudicial attitudes, and therefore contact theory and intergroup competition theory should be applied differently depending on the specifics.

Effects of Online Hate Speech on Prejudice

The study also examined the impact of experiencing online hate speech on prejudice levels. In the analysis, the variable of experience of being targeted by online hate speech was found to be significant (p<0.001). Translating this into an IRR value, we can expect a 13% increase in the level of prejudice against outgroups for a onetime increase in the experience of being targeted by hate speech. This indicates another harmful consequence of online hate speech, which not only affects the immediate psychological wellbeing of the victims but also influences their attitudes towards out-groups.

When individuals are subjected to hate speech, it can lead to increased stress, anxiety, and defensive attitudes, which may exacerbate prejudicial views. Individuals exposed to hate speech, or those who perceive themselves as targets of such speech, may experience feelings of degradation, as well as negative emotions such as stress, anger, and anxiety. These emotions can undermine their selfesteem and lead to a perception that their in-group is under threat. This perception can, in turn, result in defensive mechanisms towards the in-group and an exclusionary attitude towards the outgroup. Additionally, victims of online hate speech may seek support from their in-group, leading to stronger in-group biases and increased hostility towards out-groups. Because the fundamental structure of hate speech inherently involves the process of othering, which involves distinguishing oneself from a different group. Online hate speech, inherently negative and damaging, has the potential to reinforce existing prejudices and create new ones.

The study's findings suggest that online hate speech acts as a catalyst for prejudice, amplifying negative attitudes and contributing to a more polarized and divided society. This underscores the urgent need for effective interventions to combat online hate speech and mitigate its harmful effects on both individual attitudes and broader social cohesion.


CONCLUSIONS AND LIMITATIONS

This study aimed to explain the mechanisms of hate and prejudice as a social issue of increasing importance in contemporary Korean society. The results of this study provide compelling evidence for the roles of contact theory and intergroup competition theory in understanding prejudice. The differential effects of social network size and online hate speech experiences on prejudice highlight the complex interplay of personal interactions and perceived competition in shaping attitudes towards out-groups. The results of this study are an empirical analysis that demonstrates how contact theory and intergroup competition theory work specifically in the context of hate in Korean society in the 2020s.

However, this study has several limitations. First, the measurement of each respondent's prejudice, social connections, and experience with hate speech was crude at a qualitative level. For example, a respondent's level of prejudice was not measured on a qualitative level, such as severe prejudice against one group, mild prejudice, or no prejudice, but rather by how many groups of people they were prejudiced against. This is because the study sought to make prejudice and aversion to different groups abstract concepts to explain the phenomenon in a simpler model. However, a more qualitative analysis of each variable is requested for future research.

Second, the model only includes experiences with online hate speech and does not account for experiences with offline attacks, as the primary focus of this study is on experiences with online hate speech. This study used only the online hate speech experience variable because it is assumed that the likelihood of experiencing hate speech is higher online than offline in the current Korean society. However, if more data is available, a follow-up study may be needed to analyze both the online and offline experience variables.

Despite of these limitations, the findings of this study provide substantial evidence that outgroup prejudice, hostility, and hate speech are intricately connected to perceptions of threat. In contexts involving competition for resources, increased interaction is perceived as a threat to individuals, thereby exacerbating outgroup prejudice. The perception of threat is further amplified when individuals experience hate speech, which not only reinforces negative stereotypes but also heightens the sense of existential danger posed by outgroups.

Furthermore, these findings indicate that the roots of hate speech extend beyond mere emotions of hatred, violence, and ostracism. They suggest that when an individual's sense of existential threat and instability is triggered, such as through economic uncertainty or social upheaval, it can manifest in a negative manner, leading to the reinforcement of deep-seated prejudices. These prejudices then create a fertile ground for the emergence of hateful sentiments and actions, suggesting a cyclical relationship between perceived threats, prejudice, and hate speech.

These findings offer valuable insights for developing strategies to reduce prejudice and promote more inclusive and empathetic social environments. The findings suggest that approaching online hate speech merely as a matter of freedom of expression or regulation may have significant limitations. In societies where members are easily categorized by rigid cultural or cognitive structures, and where persistent or cumulative existential anxiety exists, distinctions, prejudices, and hatred towards others are more likely to flourish. This study implies that within such a combination of factors, eliminating out-group prejudice and online hate cannot be effectively achieved solely through intergroup interactions or policy regulations. Instead, comprehensive approaches that address underlying existential threats and promote deeper understanding and empathy across groups may be necessary to foster lasting social cohesion and reduce the prevalence of hate speech.

Specifically, it is important to consider the significant role that media coverage of minority groups can play in reducing prejudice. The media has the power to shape public perception and can either reinforce negative stereotypes or help dismantle them. When media outlets choose to present minority groups in a more balanced and humanizing manner, they can contribute to the reduction of prejudices by fostering empathy and understanding among the broader public. For example, media portrayals that highlight the individual stories, achievements, and challenges faced by members of minority groups can help counteract the dehumanizing effects of hate speech. By showcasing the diversity and complexity within these groups, the media can help audiences see minority members as individuals rather than as faceless representatives of a stereotype. This, in turn, can weaken the cognitive structures that support prejudice and discrimination.

Moreover, the media’s role in providing accurate and context-rich information is crucial in preventing the spread of misinformation and fear, which often fuel prejudiced attitudes. Responsible reporting that avoids sensationalism and instead focuses on nuanced, fact-based narratives can reduce the sense of existential threat that often underlies prejudices and hate speech. By promoting a more informed and empathetic public discourse, the media can act as a counterbalance to the divisive and harmful narratives that exacerbate social tensions. In this way, this study theoretically explains the dynamics of hate and prejudice experienced in contemporary Korean society and provides directions for solutions.

Acknowledgments

This work was supported by Hankuk University of Foreign Studies Research Fund of 2024. This research was also supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF).

Disclosure Statement

No potential conflict of interest was reported by the author.

Notes
1 The data collection was carried out in line with Kakao’s self-regulatory measures pertaining to online hate speech, and Kakao supported the survey’s execution.
2 This study was approved by the Institutional Review Board (IRB) at the Seoul national university (IRB No. 2301/003-012).

References

  • Ahmed, S. (2017). News media, movies, and anti- Muslim prejudice: Investigating the role of social contact. Asian Communication Research, 27(5), 536–553. [https://doi.org/10.1080/01292986.2017.1339720]
  • Ahmed, S., Chen, V. H. H., Jaidka, K., Hooi, R., & Chib, A. (2021). Social media use and anti-immigrant attitudes: Evidence from a survey and automated linguistic analysis of Facebook posts. Asian Communication Research, 31(4), 276–298. [https://doi.org/10.1080/01292986.2021.1929358]
  • Allport, G. (1954). The nature of prejudice. Addison-Wesley.
  • Bang, H. J., Row, K. R., & Jung, S. J. (2010). The development of implicit and explicit race attitudes in Korean in the multi-cultural age. The Korean Journal of Developmental Psychology, 23(2), 125–140.
  • Barlett, C. P., Gentile, D. A., & Chew, C. (2016). Predicting cyberbullying from anonymity. Psychology of Popular Media Culture, 5(2), 171–180. [https://doi.org/10.1037/ppm0000055]
  • Berry, J. W. (2005). Acculturation: Living successfully in two cultures. International Journal of Intercultural Relations, 29(6), 697–712. [https://doi.org/10.1016/j.ijintrel.2005.07.013]
  • Chang, K. S. (2010). The second modern condition? Compressed modernity as internalized reflexive cosmopolitization. British Journal of Sociology, 61(3), 444–464. [https://doi.org/10.1111/j.1468-4446.2010.01321.x]
  • Cheong, Y. G., Song, H. J., Yoon, T. I. & Shim. H. (2011). The effect of media stereotyping of immigrants on attitudes toward multiculturalism in Korea. Korean Journal of Journalism & Communication Studies, 55(2), 405–427.
  • Citron, D. K. (2014). Hate crimes in cyberspace. Harvard University Press.
  • Cox , D. R. (1983). Some remarks on overdispersion. Biometrika, 70(1), 269–274. [https://doi.org/10.2307/2335966]
  • Goel, V., Sahan, D., Dutta S., Bandhakavi, A. & Chakraborty, T. (2023). Hatemongers ride on echo chambers to escalate hate speech diffusion. PNAS Nexus, 2(3), pgad041. [https://doi.org/10.1093/pnasnexus/pgad041]
  • Han, S. (2022). Elite polarization in South Korea: Evidence from a natural language processing model. Journal of East Asian Studies, 22(1), 45–57. [https://doi.org/10.1017/jea.2021.36]
  • Hwang, J. M., Kim, Y. S., Lee, M. J., Choi, H., & Lee, D. J. (2007). Research on the multiethnic and multicultural orientation of Korean society. Korean Women Development Institute.
  • Joo, J. W., & Im, I. (2022). Mediated exclusive discourse and reproduction of hateful public sphere: Focusing on analysis of portal news articles and comments related to Jeju Yemeni refugee issues. Locality and Globality, 46(1), 97–134. [https://doi.org/10.33071/ssricb.46.1.202202.97]
  • Jung, H. J., & Cho, A. (2021). A study on online gender based hate speech: Focusing on the keyword ‘Kimchi-nyeo’ used by teenagers. Journal of Future Oriented Youth Society, 18(2), 65–91. [https://doi.org/10.34244/JFOYS.2021.18.2.4]
  • Kakao. (2021). Kakao’s principles for combating hate speech.
  • Kim, H. S., Kim, D. Y., Shin, H., & Yi, J. (2011). Psychological adaptation of Koreans in the multicultural era: The effect of social identity, acculturation-related ideologies, and intergroup contact on prejudice against migrants in Korea. Korean Journal of Social and Personality Psychology, 25(2), 51–89. [https://doi.org/10.21193/kjspp.2011.25.2.004]
  • Kopytowska, M., & Baider, F. (2017). From stereotypes and prejudice to verbal and physical violence: Hate speech in context. Lodz Papers in Pragmatics, 13(2), 133–152. [https://doi.org/10.1515/lpp-2017-0008]
  • Kwon, N. R. (2024). A study on moral unification education strategies based on parasocial contact theory. Journal of Korean Elementary Moral Education, 88, 1–27.
  • Lee, H. S., & Kim, S. (2021). Hate-related discourse in Korean society after COVID-19: An analysis of media coverage using structural topic model. Journal of East Asian Social Thoughts, 24(2), 261–296. [https://doi.org/10.17207/jstc.2021.3.24.2.8]
  • Lee, J. (2021). The effects of racial hate tweets on perceived political polarization and the roles of negative emotions and individuation. Asian Communication Research, 18(2), 51–68. [https://doi.org/10.20879/acr.2021.18.2.51]
  • Lim, T. C., & Song, C. (2021). Editors’ introduction: Socio-cultural and political changes in South Korea through a discursive lens. International Journal of Korean History, 26(2), 1–9.
  • O’Regan, C. (2018). Hate speech online: An (intractable) contemporary challenge? Current Legal Problems, 71(1), 403–429. [https://doi.org/10.1093/clp/cuy012]
  • Olson, M. A. (2009). Measures of prejudice. In T. D. Nelson (Ed.), Handbook of prejudice, stereotyping, and discrimination (pp. 367–386). Psychology Press, Taylor & Francis Group.
  • Park, M., Yoon, S. W., & Baek, Y. M. (2020). Perception of feminists and the #Metoo movement: An empirical investigation of the subtle and implicit gender discrimination in the workplace. Asian Communication Research, 17(2), 10–40. [https://doi.org/10.20879/acr.2020.17.2.10]
  • Park, S. (2015). An analysis of the mediating effect between contact frequency and prejudice of out-group in search of the direction for the prejudice-reducing education. Journal of the Korean Association of Geographic and Environmental Education, 23(1), 83–99.
  • Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90(5), 751–783. [https://doi.org/10.1037/0022-3514.90.5.751]
  • Pettigrew, T. F., Christ, O., Wagner, U., & Stellmacher, J. (2007). Direct and indirect intergroup contact effects on prejudice: A normative interpretation. International Journal of Intercultural Relations, 31(4), 411–425. [https://doi.org/10.1016/j.ijintrel.2006.11.003]
  • Quillian, L. (1995). Prejudice as a Response to perceived group threat: Population composition and anti-immigrant and racial prejudice in Europe. American Sociological Review, 60(4), 586–611. [https://doi.org/10.2307/2096296]
  • Roh, S. H. (2013). Increased foreigners and fear of crime: Focusing on group threat theory. Korean Criminological Review, 24(3), 151–184.
  • Scheepers, P., Gijsberts, M. & Coenders. M. (2002). Ethnic exclusionism in European countries: Public opposition to civil rights for legal migrants as a response to perceived ethnic threat. European Sociological Review, 18(1), 17–34.
  • Seo, J. (2024). A literature review to explore the use of contact theory, in exploring integration strategies between students who have defected from North Korea and South Korean students. Journal of Learner-Centered Curriculum and Instruction, 24(7), 541–555. [https://doi.org/10.22251/jlcci.2024.24.7.541]
  • Seok, S. H. (2023). Generational victimism and gender conflict: The dynamics of attribution styles and hatred to neighboring groups. Association of East Asian Social Thoughts, 26(2), 137–178.
  • Sherif, M. (1966). In common predicament: Social Psychology of Intergroup Conflict and Cooperation. Houghton Mifflin.
  • Sherif. M., Harvey, O. J., White, B. J., & Hood, W. R. (1988). Intergroup conflict and cooperation: The Robbers cave experiment. Wesleyan.
  • Silverstein, B., & Flamenbaum, C. (1989). Biases in the perception and cognition of the actions of enemies. Journal of Social Issues, 45(2), 51–72. [https://doi.org/10.1111/j.1540-4560.1989.tb01542.x]
  • Tajfel, H. (1982). Social psychology of intergroup relations. Annual Review of Psychology, 33, 1–39. [https://doi.org/10.1146/annurev.ps.33.020182.000245]
  • Tajfel, H., & Turner, J. (2000). An integrative theory of intergroup conflict. In M. J. Hatch, & M. Schultz (Eds.), Organizational identity: A reader (pp. 56–65). Oxford University Press. [https://doi.org/10.1093/oso/9780199269464.003.0005]
  • Tan, A., Fujioka, Y., & Lucht, H. (1997). Native American stereotypes, TV portrayals, and personal contact. Journalism and Mass Communication Quarterly, 74(2), 265–284. [https://doi.org/10.1177/107769909707400203]
  • Wachs, S., Costello, M., Rieger, D., & Scheithauer, H. (2022). Youths as targets: Factors of online hate speech victimization among adolescents and young adults. Journal of Computer- Mediated Communication, 27(4), zmac012. [https://doi.org/10.1093/jcmc/zmac012]

Figure 1.

Figure 1.
Distribution of the Prejudice LevelNote. Unit of the analysis: Person

Table 1.

2023 Population Distribution of Data (population in 20’s-60’s)

Male Female
20-30’s 40-50’s 60’s 20-30’s 40-50’s 60’s
Seoul Sample data 3.8% 3.9% 1.6% 3.9% 4.0% 1.8%
Population 3.7% 3.8% 1.7% 3.9% 3.9% 1.9%
Busan Sample data 1.0% 1.4% 0.7% 1.0% 1.3% 0.8%
Population 1.1% 1.3% 0.7% 1.0% 1.4% 0.8%
Daegu Sample data 0.8% 1.0% 0.4% 0.7% 1.1% 0.5%
population 0.8% 1.0% 0.5% 0.7% 1.0% 0.5%
Incheon Sample data 1.1% 1.3% 0.5% 1.0% 1.3% 0.6%
Population 1.1% 1.3% 0.6% 1.0% 1.3% 0.6%
Gwangju Sample data 0.5% 0.6% 0.2% 0.5% 0.6% 0.2%
Population 0.5% 0.6% 0.2% 0.5% 0.6% 0.3%
Daejeon Sample data 0.6% 0.6% 0.2% 0.5% 0.6% 0.3%
Population 0.6% 0.6% 0.3% 0.5% 0.6% 0.3%
Ulsan Sample data 0.4% 0.5% 0.2% 0.3% 0.5% 0.2%
Population 0.4% 0.5% 0.2% 0.3% 0.5% 0.2%
Sejong Sample data 0.2% 0.2% 0.0% 0.1% 0.2% 0.0%
Population 0.1% 0.2% 0.0% 0.1% 0.2% 0.0%
Gyungi Sample data 5.1% 6.2% 2.4% 4.7% 6.1% 2.4%
Population 5.3% 6.3% 2.4% 4.6% 6.1% 2.5%
Gangwon Sample data 0.5% 0.7% 0.4% 0.4% 0.6% 0.4%
Population 0.5% 0.6% 0.4% 0.4% 0.6% 0.4%
Chungcheong Sample data 1.3% 1.7% 0.7% 1.0% 1.4% 0.7%
Population 1.4% 1.7% 0.8% 1.1% 1.5% 0.8%
Jeolla Sample data 1.0% 1.4% 0.8% 0.8% 1.4% 0.8%
Population 1.1% 1.5% 0.8% 0.9% 1.4% 0.8%
Gyeongsang Sample data 1.8% 2.7% 1.3% 1.5% 2.6% 1.3%
Population 1.9% 2.6% 1.3% 1.5% 2.4% 1.3%
Jeju Sample data 0.2% 0.3% 0.1% 0.2% 0.2% 0.1%
Population 0.2% 0.3% 0.1% 0.2% 0.3% 0.1%

Table 2.

Social Network Components of Contact Group Types

Group types Group identity
Beneficiary group People with disabilities, People aged 70+, People living in poverty
Competing group Foreigner, Feminist
Diversity group People from different regions, People of different religions, Young adults (20s and 30s), LGBTQ+

Table 3.

Sociodemographic Characteristics of the Survey Respondents (N = 1,000)

M SD Min Max
Gender (% of female) 0.49 - - -
Age 44.70 13.78 18 69
Education: High school graduate or below (%) 19.8 - - -
Education: Undergraduate (%) 68.0 - - -
Education: Graduate (%) 12.2 - - -
Economic status 2.64 0.79 1 5
Political orientation (progressive) 2.98 0.75 1 5
Online media use (minutes) 287.03 255.43 10 1250
Online attack experiences 1.83 2.45 1 10
Network: Foreigner 1.52 0.79 1 4
Network: Feminist 1.38 0.72 1 4
Network: People with disabilities 1.65 0.76 1 4
Network: People aged 70+ 2.39 1.01 1 4
Network: People living in poverty 1.65 0.81 1 4
Network: People from different regions 2.88 1.05 1 4
Network: People of different religions 2.78 1.07 1 4
Network: Young adults (20s and 30s) 2.85 1.03 1 4
Network: LGBTQ+ 1.19 0.51 1 4

Table 4.

Intercorrelations of Indicators

1 2 3 4 5 6 7 8 9 10 11 12
* p < .05. ** p < .01. *** p < .001.
Level of prejudice
Competing group .13***
Benefits group .11*** .41***
Diversity group -.07* .37*** .47***
Online attack experiences .26*** .28*** .20*** -.04
Gender -.17*** .00 -.03 .01 -.07*
Age .02 -.07* .24*** .00 -.07* .01
Education level .00 .16*** .14*** .23*** -.03 -.07* -.01
Economic status -.03 .12*** .09** .13*** .00 .02 .00 .23***
Political orientation -.04 .05 -.01 .04 -.03 .04 -.02 .02 -.02
Online media use -.01 .04 .05 .05 .02 .08* -.08* .02 -.04 -.02
Identity: Sexual orientation .05 .03 .00 -.05 .06* .00 -.06* .02 .03 -.06 -.01
Identity: Health .01 .02 .06 -.06* .06* -.04 .1** -.1** .03 .00 -.07* .04

Table 5.

The Coefficient Effects of the Network Variables

Estimate SE p-value
Foreigner 0.13 0.07 .05
People from different regions -0.13 0.05 .01
People of different religions -0.12 0.05 .02
Young adults (20s and 30s) -0.14 0.05 .01
People with aged 70+ -0.01 0.05 .80
People living in poverty 0.15 0.07 .02
People with disabilities 0.09 0.07 .22
Feminist 0.14 0.07 .05
LGBTQ+ 0.35 0.10 < .01

Table 6.

Results of Base Model(1) and Final Research Model(2)

Model 1 Model 2
Estimate SE IRR Estimate SE IRR
Note. IRR: Incidence rate ratio.
* p < .05. ** p < .01. *** p < .001.
Gender -0.58*** 0.11 .56 -0.56*** 0.10 0.57
Age <.01 <.01 1.00 <.01 <.01 1.00
Education -0.03 0.10 .97 0.02 0.10 1.02
Economic status -0.04 0.07 .96 -0.07 0.07 0.93
Political orientation -0.07 0.07 .93 -0.08 0.07 0.93
Online media use <.01 <.01 1.00 <.01 <.01 1.00
Identity: Sexual orientation 0.34 0.23 1.40 0.05 0.23 1.05
Identity: Health 0.03 0.13 1.03 -0.11 0.12 0.90
Online attack experiences 0.12*** 0.02 1.13
Competing group 0.16** 0.06 1.17
Benefits group 0.10 0.07 1.11
Diversity group -0.22*** 0.06 0.80
(Intercept) 0.69 0.38 0.37 0.38
Observations 1,000 1,000
Log Likelihood -3045.1 -2972.3
Theta 0.485 0.576
AIC 3065.1 3000.3
BIC 3114.2 3069.0