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Abstract

The dissemination of weaponized information—defined as the intentional use of falsehoods (i.e. disinformation) and the harmful misuse of accurate information (i.e. malinformation) against a target—on social media represents a notable downside of these platforms, often linked to polarization. However, research on the relationship between weaponized information and polarization remains limited due to conceptual ambiguities and geographical context. This study uses cross-sectional survey data (N = 520) collected across eight provinces in Afghanistan to explore the direct and indirect association between exposure to disinformation and malinformation online and ethnic polarization. Findings through mediation analyses reveal that exposure to disinformation was not associated with ethnic polarization, either directly or indirectly. Conversely, exposure to malinformation was directly associated with ethnic polarization and indirectly linked to it through increased ingroup positive perception. These findings highlight the nuanced difference in how intentional falsehoods and the harmful misuse of accurate information shape polarization dynamics in ethnically diverse and polarized societies.

Keywords

disinformation ethnic polarization intergroup perceptions malinformation weaponized information

Introduction

One major drawback of social media is its role in spreading weaponized information—either factually inaccurate and intentionally harmful (disinformation) or factually accurate repurposed for malicious ends (malinformation). This proliferation is often linked with increasing polarization across societies, yet the mechanisms and consequences remain understudied, particularly beyond the Western context. Weaponized information broadly refers to the content that is factually false or accurate, yet intentionally deployed to harm the target (Santos-D’Amorim & de Oliveira Miranda, 2021; Singer & Brooking, 2018; Tong et al., 2020). Although weaponized information has evolved throughout history, the advent of social media has accelerated its reach and circulation (Santos-D’Amorim & de Oliveira Miranda, 2021). Within this domain—marked by intentionality and harmful nature—weaponized information can be categorized into disinformation and malinformation. Disinformation refers to deliberately created, shared, and promoted falsehoods intended to mislead or damage the target, or to serve political, financial, or ideological interests (Broda & Strömbäck, 2024; Wardle & Derakhshan, 2017). Conversely, malinformation denotes the strategic misuse of factual information, such as exposing private information to inflict harm or reframing accurate information to incite hostility (Santos-D’Amorim & de Oliveira Miranda, 2021; Wardle & Derakhshan, 2017). The power of disinformation lies in its deceptive fabrication, while malinformation capitalizes on the intentional distortion of truth (Santos-D’Amorim & de Oliveira Miranda, 2021; Tong et al., 2020; Wardle & Derakhshan, 2017).

Studies have highlighted how institutional and individual actors exploit social media to employ trolls, fake accounts, and social bots to spread weaponized information—manipulating narratives, eliciting public confusion, and deepening social division. In addition, social media algorithms, which prioritize virality over veracity, further amplify the dissemination of such information and its effects, intensifying fragmentation among social groups (Bradshaw & Howard, 2018; Singer & Brooking, 2018).

A growing body of literature links weaponized information online to sociopolitical polarization (Arora et al., 2022; Kubin & Von Sikorski, 2021). The spread of this type of information during the 2016 and 2020 US elections, Brexit, and the COVID-19 pandemic are notable examples demonstrating its impact in fostering uncertainty, confusion, and division (Allcott & Gentzkow, 2017; Lewandowsky et al., 2017; Marino & Iannelli, 2023; Recuero et al., 2021; Santos-D’Amorim & de Oliveira Miranda, 2021). Research suggests that weaponized information can solidify individuals’ positions on issues and elicit negative emotions toward outgroups, thereby driving polarization (Arora et al., 2022). Studies in the United States show that far-right actors use bots and coordinated networks to amplify divisive content, while politicians and hyper-partisans cultivate echo chambers that normalize falsehoods and entrench hostile outgroup attitudes (Schudson et al., 2017; Tucker et al., 2018).

Despite increasing scholarly attention, the conflated usage of terms like “fake news,” “misinformation,” and “disinformation”—each with conceptually distinct meanings—continues to hinder analytical precision (Aïmeur et al., 2023; Gonçalves-Segundo, 2022; Marino & Iannelli, 2023; Vicario et al., 2019; Wardle & Derakhshan, 2017). Moreover, while the link between disinformation and polarization received some attention (Au et al., 2022; Pérez-Escolar & Noguera-Vivo, 2021; Vasist et al., 2024), malinformation remains largely unexplored. In addition, most research has focused on links between weaponized information and dimensions of political polarization (attitudinal, ideological, and affective polarization) in Western contexts (Marino & Iannelli, 2023; Vasist et al., 2024), while this association with ethnic polarization, particularly in developing countries, remains underexplored.

This research aims to address these gaps by examining the association between exposure to weaponized information and ethnic polarization in Afghanistan, using cross-sectional survey data collected across eight provinces. Afghanistan is home to dozens of ethnicities, four of which—namely, Pashtun, Tajik, Hazara, and Uzbek—constitute the majority of the country’s population. However, the relations between these major ethnic groups—particularly between Pashtuns and non-Pashtuns—have been fraught since the establishment of Afghanistan as a state in the late nineteenth century (Rashidi & Göksel, 2019; Wafayezada, 2024). The state-initiated ethnonationalism since then politicized ethnicity as a determinant of social and political dynamics, producing persistent interethnic tensions. Historically, political power predominantly rested with Pashtun elites, with only a few interruptions—Habibullah Kalakani’s nine-month rule (1929), Babrak Karmal’s tenure (1979–1986), and Burhanuddin Rabbani’s presidency (1992–1996, effectively controlling the capital)—when non-Pashtun held office (Pamirzad et al., 2025). This dominance remained prevalent, and even the Western-backed semi-democratic government (2001–2021) failed to bridge divides and arguably contributed to the Taliban resurgence through ethnocentric policies (Wafayezada, 2024).

The Taliban’s return to power in August 2021, marked by authoritarianism, an ethnocentric posture, intolerant religious interpretation, and repressive policy toward women and minorities (Ibrahimi, 2023; Wani, 2024), has exacerbated ethnic division. As a movement with a predominantly Pashtun composition, the Taliban’s resurgence has been associated with widespread frustration and marginalization among other ethnic groups (Borthakur & Kotokey, 2020; Ibrahimi, 2023; Wafayezada, 2024). In addition, following 2021, large-scale emigration, particularly the elite segment of society, such as scholars, journalists, and civil servants, has contributed to a politically active yet ethnically divided diaspora in exile. These diasporic networks, spurred by rising domestic constraints on freedom of speech, have become key actors in shaping online discourse, justifying or defying the regime—often along ethnic lines—and fueling polarized interethnic exchanges.

This interethnic hostility, rooted in offline conflicts, is likely susceptible to online triggers, including ethnically framed weaponized information, proliferated on social media—used by younger cohorts (Wafayezada, 2024). This space, often dominated by fabricated false content and malicious use of facts, has potentially deepened existing ethnic divides, worsening interethnic relationships (Pamirzad, 2025; Wafayezada, 2024). Nonetheless, evidence on whether exposure to such information online exacerbates ethnic polarization remains limited. Hence, this study empirically examines whether exposure to disinformation and malinformation is associated with ethnic polarization and whether shifts in interethnic perceptions mediate this relationship. By situating the analysis in Afghanistan’s distinct sociopolitical milieu, this study not only enhances the generalizability of previous research but also advances the literature on social media polarization, particularly ethnic polarization, in non-Western contexts (Asimovic et al., 2021, 2023; Gelovani et al., 2025). The remainder of this article is structured as follows: the relevant literature is presented in the next section. The coming sections present research methodology, findings, discussion, conclusion, research implications, and limitations.

Literature Review

Weaponized Information and Ethnic Polarization

The association between weaponized information and social media polarization has remained contested. Some scholars have argued that weaponized information drives polarization on social media (Gonçalves-Segundo, 2022; Vasist et al., 2024), while others contend that polarized communities reinforce and facilitate the spread and acceptance of weaponized information (Jenke, 2024; Vicario et al., 2019). In this article, we primarily examine the first association—namely, that exposure to weaponized information drives polarization—while acknowledging the opposing view as a pressing question requiring further exploration.

Scholars have attributed the association of weaponized information with polarization to several interconnected factors. First, weaponized information can disinform people and create misperceptions among social groups, blurring the line between truth and falsehood. This confusion fosters deception, miscalculation, and disruption of the political process, leading to erroneous perceptions about issues and groups, which exacerbate rifts and divisions (Gonçalves-Segundo, 2022; Goyanes et al., 2025; Lewandowsky et al., 2017; Santos-D’Amorim & de Oliveira Miranda, 2021; Teruel-Rodríguez, 2023). In addition, weaponized information is often imbued with negative sentiments, verbal attack, and sensationalist intent, triggering negative emotions in consumers, provoking affective responses, contributing to emotional contagion, thereby driving polarization (Au et al., 2022; Brady et al., 2017; Weismueller et al., 2024).

Moreover, weaponized information contributes to the formation of echo chambers, consequently fostering polarization. This type of content reinforces intergroup perceptions, where individuals adopt an “us vs. them” mentality, cluster with those who share their beliefs and identities, and prioritize information that supports their group’s position. These communities, by tolerating ingroup-congruent falsehoods, shape distorted perceptions of outgroups and intensify intergroup rift and polarization (Au et al., 2022; Pérez-Escolar & Noguera-Vivo, 2021; Tucker et al., 2018).

Recently, scholars have paid attention to ethnic polarization, which refers to the divergence in the position of ethnic groups on social and political issues, or the salience of ethnic identity fostering ethnocentrism—a prevalent phenomenon in multiethnic societies (Asimovic et al., 2021, 2023; Bradley & Chauchard, 2022; Evans & Need, 2002; Pamirzad & Chen, 2026). In ethnically divided societies, people selectively promote information that aligns with their group’s interests and positions. Rather than prioritizing factual accuracy, they value ingroup congruency of content—especially when social and political positions diverge across ethnic lines (Flynn et al., 2017). Hence, weaponized information can be strategically deployed to defame, demonize, and dehumanize rival groups, thereby instigating intergroup hostility, eliciting negative emotions toward outgroups, and intensifying polarization (Grambo, 2018; Weismueller et al., 2024). Fabricated interethnic or racial content, often sensational and emotionally charged, has been shown to exacerbate intergroup prejudice, reinforce negative stereotypes, intensify polarization, and even, in some cases, incite physical violence (Au et al., 2022; Saadati et al., 2024).

Among the two categories of weaponized information—disinformation and malinformation—research has primarily examined the association between disinformation and various forms of polarization. According to Vasist et al. (2024), disinformation, by exploiting sensations and utilizing emotionally charged and provocative language, elicits strong affective responses and outrage, which not only contributes to its virality but also fosters societal polarization. Other studies linked disinformation to online incivility and personal attacks, which trigger negative emotions, particularly anger, thereby exacerbating ideological polarization (Au et al., 2022). Research further argues that it intensifies intolerance against dissenting voices, facilitating echo chambers that are prone to driving polarization (Pérez-Escolar & Noguera-Vivo, 2021; Tucker et al., 2018). Studies in the United States show that the spread of disinformation during the 2016 and 2020 elections not only created entrenched political positions within parties but also instilled hostility among the parties’ supporters, culminating in the Capitol riot. This line of research highlights that disinformation, by undermining intergroup trust, has far-reaching and divisive consequences for social groups (French et al., 2024). In addition, studies in the Netherlands noted that exposure to far-right disinformation was associated with increased support for far-right populist politicians, suggesting that it influences people’s attitudes toward politics, driving them in polarized directions (Hameleers, 2022). Another strand of research has highlighted the connection between disinformation and opinion extremity, which is a precursor to ideological polarization and a factor that negatively impacts intergroup relations (Hopp et al., 2020).

Regarding the association of malinformation with polarization, although the literature remains limited, existing studies have linked its divisive potential to the intentional misuse of factual information (Bruns et al., 2024; Hameleers, 2022). In ethnically divided contexts, malinformation can be understood as a sort of identity-based targeting that may reinforce stereotypes, revive grievances, and exacerbate identity-based polarization. It can re-trigger collective memories of discrimination and marginalization, and fuel resentment, mistrust, and intensifying defensive behavior (Turcilo & Obrenovic, 2020). In addition, this type of information elicits strong affective responses that can motivate retaliatory behaviors and further exacerbate intergroup hostility (Cooke, 2021; Lim, 2023). Given the limited and inconclusive literature on malinformation, we propose the following hypothesis and pose the research question:

H1: Exposure to disinformation is positively associated with ethnic polarization.

Q1: Is there any association between exposure to malinformation and ethnic polarization?

The Mediating Effects of Ingroup Positive and Outgroup Negative Perceptions

Group processes and categorization stem from the social identity approach, particularly social categorization theory, which argues that, driven by psychological and social needs such as belonging, validation, and social support, people tend to categorize themselves into groups. According to this theory, individuals tend to favor their ingroups, with whom they share commonalities, while differentiating themselves from outgroups, which are perceived as outsiders. This mere categorization contributes to ingroup positive perception and favoritism (Tajfel et al., 1982; Turner et al., 1979). Negative perceptions of outgroups often arise when intergroup comparisons are viewed through a competition lens or when groups turn into competitive entities vying for resources (Riley, 2022). This intergroup competitiveness escalates when groups perceive or experience a threat from the outgroup, culminating in ingroup favoritism and outgroup animosity (Brewer, 1999; Iyengar et al., 2012, 2019).

The relationship between weaponized information and group affiliation appears to be bidirectional. On the one hand, group affiliation significantly influences how individuals interpret and process weaponized information online (Deinla et al., 2022; Jenke, 2024; Kaiser et al., 2022; Tong et al., 2020). For instance, studies in the United States reveal that individuals’ political affiliation serves as a benchmark for labeling content as fake news (Tong et al., 2020). Similarly, findings show that left-wing individuals in the United States are more likely to block or unfollow those who share weaponized information. However, this tendency is stronger toward ideological opponents, suggesting that individuals tolerate false information within their ingroups while exhibiting a stringent stance toward outgroups (Kaiser et al., 2022). In addition, studies highlight that people’s interpretations of weaponized information align with their group identity, directed toward blaming or attacking opponents. It suggests that individuals may dismiss accurate information that criticizes their ingroup—labeling it as false or biased (Tong et al., 2020), while readily accepting ingroup-consistent false information (Deinla et al., 2022; Jenke, 2024).

On the other hand, exposure to weaponized information strengthens ingroup solidarity and cohesion, triggers affective responses toward outgroups, deepens intergroup division, and intensifies polarization (Gonçalves-Segundo, 2022; Tucker et al., 2018). Studies show that individuals exposed to weaponized information become more receptive, exhibit reduced concern about its veracity, and are more likely to engage with content that reinforces their group perceptions, thereby fostering polarization (Freelon et al., 2022). For example, Riley’s (2022) findings, which explored TheDonald.win, an online forum that supported President Trump during the 2020 presidential election, observed the prevalence of weaponized information processed through ingroup–outgroup dynamics. This study found that exposure to weaponized information facilitated ingroup solidarity through amplifying ingroup victimhood, simultaneously fostering violent rhetoric toward outgroups. These findings underscore that weaponized information mobilizes the ingroup against perceived threats from the outgroup, fostering negative outgroup emotions and perceptions. In addition, studies have linked exposure to right-wing disinformation with increased support for populist right-wing politicians, suggesting that it affects perceptions based on group affiliation rather than the accuracy of the content (Hameleers, 2022; Hameleers et al., 2022). Consequently, exposure to weaponized information serving as an ingroup mobilizing force amplifies positive ingroup perception while eliciting antagonism toward outgroups, thereby reinforcing identity-driven polarization (Gonçalves-Segundo, 2022; Tucker et al., 2018). This study argues that in ethnically polarized societies where ethnicity plays a dominant role in shaping intergroup dynamics (Bradley & Chauchard, 2022), exposure to ethnically weaponized information makes ethnic identity salient, which in turn affects intergroup perceptions and exacerbates ethnic polarization (Riley, 2022; Saadati et al., 2024). Hence, we posit the following hypotheses and raise this question.

H2: Ingroup positive (H2a) and outgroup negative perceptions (H2b) mediate the relationship between disinformation exposure and ethnic polarization.

Q2: Do ingroup positive and outgroup negative perceptions mediate the relationship between malinformation and ethnic polarization?

Furthermore, research into intergroup relationships indicates that while ingroup attachment does not inherently translate to outgroup animosity, factors such as ingroup moral superiority, perceived threat, intergroup distrust, and intergroup comparison are correlated with negative perceptions of the outgroup (Brewer, 1999). Similarly, in polarized societies, strong ingroup identification elicits negative outgroup perceptions and attitudes (Arora et al., 2022; Iyengar et al., 2019). Hence, we propose the following. Figure 1 summarizes the research model.

H3: Ingroup positive perception is positively associated with outgroup negative perception.

Figure 1.

Hypothesized model (IGPP = Ingroup positive perception, OGNP = Outgroup negative perception, and E. polarization is ethnic polarization. Solid arrows indicate research hypotheses, and dashed arrows represent the research questions).

Methodology

This study used cross-sectional survey data from Afghanistan to examine the association between exposure to weaponized information on social media and ethnic polarization. A pilot test was conducted with a sample of 70 participants using an online questionnaire in Persian to assess the internal consistency of the constructs. Although both Persian/Dari and Pashtu are the two official languages in Afghanistan, Persian was selected due to its role as the lingua franca, spoken by the majority of the population, regardless of their ethnic affiliation (Dinakhel, 2018; Mahboob et al., 2024). Following the pilot, a convenience-based snowball sampling strategy was employed to recruit respondents, based on availability and willingness to participate (Etikan, 2016). The finalized questionnaire was printed and distributed to students from eight provinces enrolled in the journalism department of Badakhshan University, where the first author served as a lecturer. Students were asked to complete the survey during their winter vacation in their home provinces. The questionnaire began with a brief overview of the research objectives, an informed consent statement requesting voluntary participation, and an assurance of the confidentiality of responses. No compensation was provided for participants.

The data collection began in mid-December 2024 and continued until mid-February 2025, yielding 527 responses. After excluding seven incomplete responses, the final sample comprised 520 valid questionnaires. Demographic data were collected on age (M = 24.93, SD = 6.50, range = 17–61), gender, ethnicity, education level, and monthly family income. As shown in Table 1, the sample was predominantly male, reflecting the systemic exclusion of women from public life under the Taliban rule. Ethnically, the sample was predominantly Tajiks, followed by Pashtuns, despite Pashtuns being the largest ethnic group nationally. This imbalance may be attributed to the distribution of the questionnaire in Persian, regional access constraints, and differential engagement across ethnic groups. In terms of educational level, the sample was dominated by undergraduates.

Table 1.

Demographic Information of Research Participants.

Variables	Frequency	Percentage %
Gender
M	444	85.7
F	74	14.3
Ethnicity
Pashtun	100	19.3
Tajik	272	52.4
Hazara	88	17
Uzbek	46	8.9
Others	13	2.5
Education level
High school graduates	185	35.6
Bachelors	308	59.3
Masters	18	3.5
PhD	8	1.5
Family monthly income (in Afghani)
Below 5 thousand	87	16.8
5–10 thousand	159	30.8
11–20 thousand	154	29.8
21–40 thousand	83	16.1
41–100 thousand	26	5
Over 100 thousand	8	1.5

Note: A small number of missing demographic values were imputed during analysis to maintain a complete dataset (N = 520).

Measurement of Variables

Disinformation Exposure

To measure this variable, we followed Wardle and Derakhshan’s (2017) conceptualization and Jones-Jang et al.’s (2021) measurement approach, proposing four items on a 5-point Likert-type scale (Strongly disagree = 1, Strongly agree = 5). “I frequently encounter social media posts that deliberately spread false information to escalate intergroup tensions,” “I occasionally come across organized streams of misleading and false posts intended to deepen intergroup hostility,” and “I frequently observe coordinated efforts on social media where groups spread false information to intensify intergroup tensions” and “I frequently notice individuals on social media who intentionally spread inaccurate information to deceive the public and widen social divides” (M = 3.48, SD = 0.84; α = .70).

Malinformation Exposure

Following Wardle and Derakhshan’s (2017) conceptualization and Jones-Jang et al.’s (2021) measurement approach, we developed four items to measure this variable on a 5-point Likert-type scale. “I occasionally encounter fact-based information on social networks that is deliberately shared to inflict damage to social groups,” “I frequently see accurate information on social media being used in misleading way to fuel intergroup conflict,” “I frequently come across distorted facts on social media, deliberately shared to provoke ethnic hostility,” and “I often see content on social media that misrepresents accurate information to damage the reputation of ethnic groups” (M = 3.45, SD = 0.84, α = .71).

Ingroup Positive Perception

Four items were administered to measure this construct, using a 5-point Likert-type scale, aligned with the social identity theory (Tajfel et al., 1982) and following Stephan et al.’s (2002) work. “I believe members of my ethnic group are trustworthy,” “I believe members of my ethnic group possess positive qualities,” “I feel proud to be part of my ethnic group,” and “I believe members of my ethnic group are generally good people” (M = 3.62, SD = 0.91, α = .78).

Outgroup Negative Perception

Aligned with social identity theory (Tajfel et al., 1982) and following Stephan et al.’s (2002) approach, three items were used to measure this construct on a 5-point Likert-type scale. “I believe other ethnic groups do not have positive intentions toward my ethnic group,” “I do not perceive members of other ethnic groups as trustworthy,” and “I believe other ethnic groups have lower status compared to mine” (M = 2.74, SD = 0.99, α = .72).

Ethnic Polarization

Following studies that measure political polarization based on the divergence of parties’ positions on polarized topics (Mason, 2018), and as noted in reviews of the literature (Kubin & Von Sikorski, 2021), this study measures ethnic polarization by examining the divergence of ethnic groups’ positions on ethnically polarized issues in Afghanistan. Drawing on previous research, five polarized issues were selected to measure ethnic groups’ positions on them using a 5-point Likert-type scale (Dinakhel, 2018; Leake, 2022; Pamirzad & Chen, 2026; Wafayezada & Shafiq, 2025). Power distribution: “The political power should be fairly distributed among all ethnic groups in Afghanistan.” National identity: “The term ‘Afghan’ as the national identity carries ethnic connotations and does not encompass all ethnic groups in the country (reverse-coded).” Political issue: “The Durand line should not be accepted as the legitimate border between Afghanistan and Pakistan (reverse-coded).” Language: “Danishgah (i.e., University in Persian) is a Persian word and should be used along with Pohantoon (i.e., University in Pashtu), which is a Pashtu word.” Ethnic issue: “The Hazara people’s systematic mass killing should be recognized as genocide” (M = 3.75, SD = 0.89, α = .73). Following Lee et al. (2014), the absolute value of each response was recoded such that 1 and 5 = 3, 2 and 4 = 2, and 3 = 1. This procedure created a half-folded unidirectional variable, with higher values indicating greater polarization (M = 2.33, SD = 0.45, range = 1–3).

Control Variables

Drawing upon social identity theory and previous findings, group affiliation is recognized as a key factor shaping polarization (Iyengar et al., 2012). In addition, studies have highlighted the influence of age, gender, income, education level, and frequency of social media use on sociopolitical polarization (Horne et al., 2025; Lee et al., 2014). Hence, the analyses include ethnicity, gender, age, education level, monthly income, and the frequency of social media use (coded on a scale from never = 1 to several times per day = 5) as control variables.

Analytical Approach

To answer the research questions and test the hypotheses, we conducted mediation analyses using PROCESS Macro for SPSS. Specifically, we employed Model 6, which enables the examination of a serial mediation pathway from the independent variable through mediators to the dependent variable (X → M1 → M2 → Y) (Hayes, 2009). This model estimates the direct effects of the independent variable on each mediator and the dependent variable, the indirect effect via each mediator (X → M1 → Y and X → M2 → Y), as well as the serial indirect effect (X → M1 → M2 → Y). Furthermore, the PROCESS Macro treats controls as covariates, accounts for their confounding effects, and supports bootstrap inferences (Hayes et al., 2017). We employed 5000 resamples to produce 95% bias-corrected and accelerated confidence intervals. Indirect effects were considered statistically significant when their two-tailed 95% confidence interval did not include zero (Hayes et al., 2017).

Given a substantial correlation between disinformation and malinformation exposure (r = .60), see supplemental appendix, we conducted two separate mediation analyses to test the unique effects of each predictor while statistically controlling for the other. In the first model, disinformation exposure was the independent variable, ingroup positive (M1) and outgroup negative perceptions (M2) were mediators, and ethnic polarization was the dependent variable, with malinformation exposure and other controls included as covariates. In the second model, the role of disinformation and malinformation was reversed. It is important to note that the regression coefficients remain identical across both models because they are derived from the same underlying multivariate equation. However, the differences arise in the calculation of direct and indirect effects, which are computed relative to the specified independent variable in the model. This is an established practice for testing the distinct effects of correlated predictors (Cohen et al., 2013).

Results

When testing disinformation exposure as the independent variable (controlling for malinformation exposure), findings reported in Table 2 reveal that while exposure to disinformation was not associated with ingroup positive perception (B = 0.05, t = 0.87, p > .05), it was marginally associated with outgroup negative perception (B = 0.11, t = 1.86, p < .10). However, its association with ethnic polarization (B = −0.03, t = −1.08, p > .05) was not significant. Therefore, our H1, suggesting a positive association between exposure to disinformation and ethnic polarization, was not supported (see Figure 1).

Table 2.

The Association of Disinformation Exposure With Ingroup Positive, Outgroup Negative Perceptions, and Ethnic Polarization (Controlling for Malinformation Exposure).

Variables	IGPP		OGNP		Ethnic polarization
Variables	B (SE)	t	B (SE)	t	B (SE)	t
Constant	2.37*** (0.29)	8.23	0.73* (0.30)	2.40	2.07*** (0.15)	13.77
Disinformation exposure	0.05 (0.06)	0.87	0.11† (0.06)	1.86	−0.03 (0.03)	−1.08
IGPP			0.17*** (0.04)	3.92	0.09*** (0.02)	4.14
OGNP					0.00 (0.02)	0.14
Ethnicity (r. Tajik)
Pashtun	−0.12 (0.10)	−1.16	−0.41*** (0.10)	−3.89	−0.16** (0.05)	−3.12
Hazara	0.08 (0.11)	0.68	0.07 (0.11)	0.65	−0.10† (0.06)	−1.85
Uzbek	0.33* (0.14)	2.27	0.14 (0.14)	0.97	−0.12† (0.07)	−1.72
Others	−0.53* (0.25)	−2.12	−0.11 (0.25)	−0.42	−0.18 (0.12)	−1.49
Gender (r. Female)	0.17 (0.11)	1.46	0.23* (0.11)	2.00	−0.07 (0.06)	−1.19
Education level	−0.06 (0.07)	−0.89	−0.20** (0.07)	−3.07	−0.05 (0.03)	−1.44
Monthly income	−0.05 (0.03)	−1.45	−0.01 (0.03)	−0.28	−0.05** (0.02)	−2.74
SM usage frequency	0.07* (0.03)	2.16	−0.01 (0.03)	−0.29	0.02 (0.02)	1.17
Age	0.01* (0.01)	2.23	0.01 (0.01)	0.98	−0.00 (0.00)	−0.55
Malinformation exposure	0.17** (0.06)	2.77	0.32*** (0.06)	5.41	0.10** (0.03)	3.20
R ²	0.10		0.26		0.13
f-test	(11, 508) = 4.93***		(12, 507) = 14.55***		(13, 506) = 5.65***

Unstandardized estimates are reported (IGPP = ingroup positive perception, OGNP = outgroup negative perception, SM = social media).

†

p < .1, * p < .05, ** p < .01, *** p < .001.

In addition, ingroup positive perception was associated with increased ethnic polarization (B = 0.09, t = 4.14, p < .001), while the association between outgroup negative perception and ethnic polarization was not significant (B = 0.00, t = 0.14, p > .05). Furthermore, as reported in Table 4, neither ingroup positive (B = 0.005, 95% confidence interval (CI) [−0.006, 0.016]) nor outgroup negative perceptions (B = 0.001, 95% CI [−0.005, 0.006]) mediated the relationship between disinformation exposure and ethnic polarization, thereby not supporting H2a and H2b. However, ingroup positive perception was positively associated with outgroup negative perception (B = 0.17, t = 3.92, p < .001), supporting H3.

Responding to research question 1, the results presented in Table 3 show that, controlling for disinformation exposure, malinformation exposure was positively and significantly associated with ethnic polarization (B = 0.10, t = 3.20, p < .01). It was also positively associated with both ingroup positive (B = 0.17, t = 2.77, p < .01) and outgroup negative perceptions (B = 0.32, t = 5.41, p < .001).

Table 3.

The Association of Malinformation Exposure With Ingroup Positive and Outgroup Negative Perceptions, and Ethnic Polarization (Controlling for Disinformation Exposure).

Variables	IGPP		OGNP		Ethnic polarization
Variables	B (SE)	t	B (SE)	t	B (SE)	t
Constant	2.37*** (0.29)	8.23	0.73* (0.30)	2.40	2.07*** (0.15)	13.77
Malinformation exposure	0.17** (0.06)	2.77	0.32*** (0.06)	5.41	0.10** (0.03)	3.20
IGPP			0.17*** (0.04)	3.92	0.09*** (0.02)	4.14
OGNP					0.00 (0.02)	0.14
Ethnicity (r. Tajik)
Pashtun	−0.12 (0.10)	−1.16	−0.41*** (0.10)	−3.89	−0.16** (0.05)	−3.12
Hazara	0.08 (0.11)	0.68	0.07 (0.11)	0.65	−0.10† (0.06)	−1.85
Uzbek	0.33* (0.14)	2.27	0.14 (0.14)	0.97	−0.12† (0.07)	−1.72
Others	−0.53* (0.25)	−2.12	−0.11 (0.25)	−0.42	−0.18 (0.12)	−1.49
Gender (r. Female)	0.17 (0.11)	1.46	0.23* (0.11)	2.00	−0.07 (0.06)	−1.19
Education level	−0.06 (0.07)	−0.89	−0.20** (0.07)	−3.07	−0.05 (0.03)	−1.44
Monthly income	−0.05 (0.03)	−1.45	−0.01 (0.03)	−0.28	−0.05** (0.02)	−2.74
SM usage frequency	0.07* (0.03)	2.16	−0.01 (0.03)	−0.29	0.02 (0.02)	1.17
Age	0.01* (0.01)	2.23	0.01 (0.01)	0.98	−0.00 (0.00)	−0.55
Disinformation exposure	0.05 (0.06)	0.87	0.11† (0.06)	1.86	−0.03 (0.03)	−1.08
R ²	0.10		0.26		0.13
f-test	(11, 508) = 4.93***		(12, 507) = 14.55***		(13, 506) = 5.65***

Unstandardized estimates are reported (IGPP = ingroup positive perception, OGNP = outgroup negative perception, SM = social media).

†

p < .1, * p < .05, ** p < .01, *** p < .001.

Addressing Q2, findings in Table 4 show that positive ingroup perception significantly and positively mediated the relationship between exposure to malinformation and ethnic polarization (B = 0.015, 95% CI [0.004, 0.030]). However, the mediating effect of outgroup negative perception was not significant (B = 0.001, 95% CI [−0.012, 0.016]).

Table 4.

The Mediating Effects of Ingroup Positive and Outgroup Negative Perceptions.

Indirect paths	Effect (SE)	95% CI (LL, UL)
Disinformation → IGPP → ethnic polarization	0.005 (0.006)	−0.006, 0.016
Disinformation → OGNP → ethnic polarization	0.001 (0.003)	−0.005, 0.006
Disinformation → IGPP → OGNP → ethnic polarization	0.000 (0.000)	−0.001, 0.001
Malinformation→ IGPP → ethnic polarization	0.015 (0.007)	0.004, 0.030
Malinformation → OGNP → ethnic polarization	0.001 (0.007)	−0.012, 0.016
Malinformation → IGPP → OGNP → ethnic polarization	0.000 (0.001)	−0.001, 0.001

The reported figures are unstandardized coefficients with standard errors in parentheses. The bolded CIs indicate the significance of indirect paths.

Regarding controls, the effects of ethnic affiliation (reference = Tajik) on intergroup perceptions varied; however, Pashtuns, Hazaras, and Uzbeks showed lower levels of ethnic polarization compared to Tajiks. In addition, male respondents (reference = female) reported higher outgroup negative perception but did not differ on ingroup positive perception or ethnic polarization. Higher education level was negatively associated with outgroup negative perception, while higher monthly income was negatively linked with ethnic polarization. Finally, older age and more frequent social media use were positively associated with ingroup positive perception, but neither was significantly associated with outgroup negative perception or ethnic polarization.

Discussion and Conclusion

Studies exploring the association between weaponized information online and polarization remain limited due to conceptual ambiguity and underdeveloped theoretical underpinnings (Aïmeur et al., 2023; Marino & Iannelli, 2023). Furthermore, this association with ethnic polarization—an area more often studied in the developing countries—has remained largely unexplored. This study used cross-sectional survey data collected from eight provinces in Afghanistan to examine the direct and indirect associations between exposure to disinformation and malinformation online and ethnic polarization. The findings revealed that while exposure to disinformation had no link to ethnic polarization, exposure to malinformation was significantly associated with increased ethnic polarization. In addition, malinformation was linked with increased ethnic polarization through the mediating effect of ingroup positive perception.

The results suggest that exposure to factually wrong and intentionally created and promoted content aimed to harm the target is not directly associated with positional extremity among ethnic groups. This finding contradicts previous studies, which suggest that disinformation, by triggering emotional responses—particularly anger—contributes to polarization (Au et al., 2022; Vasist et al., 2024). Two factors may explain this paradox. First, the falsehood inherent in disinformation allows individuals to dismiss misleading narratives through verification, thereby reducing its polarizing effects. Second, in this study, ethnic polarization was measured by the positions of ethnic groups on polarized issues, reflecting the cognitive dimension of polarization. By contrast, previous research has linked disinformation to increased affective polarization, which is driven by affective reactions (Vasist et al., 2024). Nevertheless, disinformation exposure was found to be linked to increased outgroup negative perception, aligning with previous studies suggesting that exposure to disinformation elicits negative attitudes toward the outgroup (Gonçalves-Segundo, 2022; Tucker et al., 2018).

In contrast, the direct association of malinformation exposure and ethnic polarization can be attributed to its intentional misuse of factual information (Bruns et al., 2024; Hameleers, 2022). Prior research suggests that malinformation functions as identity-based targeting, which can reinforce stereotypes, revive grievances, and provoke collective memories of discrimination. These dynamics elicit defensive and retaliatory behavior as well as strong affective responses, which exacerbate hostility (Cooke, 2021; Lim, 2023; Turcilo & Obrenovic, 2020). In ethnically polarized societies like Afghanistan—where historical grievances and stereotypes shape interethnic relationships—the malicious use of factual content can result in a stronger emotional and psychological impact, deepening divisions (Wafayezada, 2024). For instance, one notable case of misusing factual information in Afghanistan relates to the Afshar massacre that happened during the civil war in February 1993. According to independent reports from international organizations such as Human Rights Watch, the massacre involved Jihadi factions operating along ethnic lines (Human Rights Watch, 2005). However, this event has been transmitted to the younger generation through ethnic narratives of self-exoneration and blaming others, contributing to ethnic division—particularly between the Tajik and Hazara ethnic groups. Each year, during the remembrance of this event, members of these ethnicities repurpose this historical fact to blame one another while denying their ethnic involvement, fostering online hostility (Pamirzad, 2025). Similarly, the circulation of accurate yet ethnically charged historical information—such as accounts of ethnocentrism, land confiscation, and demographic engineering—has sparked contention among ethnic groups (Wafayezada, 2024). While marginalized groups may view such information as a quest for social justice, the dominant group may perceive it as malinformation, as it damages its reputation and status.

Furthermore, findings show that exposure to malinformation amplifies ingroup positive perceptions, increasing the salience of ethnic identity and driving ethnic polarization. This aligns with prior studies highlighting the role of group affiliation, in this case, ethnicity, in weaponized information processing (Deinla et al., 2022; Jenke, 2024; Kaiser et al., 2022). Individuals exposed to ethnically framed malinformation may interpret it as an attack on their social group, provoking reciprocal hostility, including sensational responses as well as retaliatory behavior (Cooke, 2021; Lim, 2023; Turcilo & Obrenovic, 2020). As a result, they may develop empathy toward their ingroup and adopt defensive, collective responses to counter identity-based targeting. In such settings, the misuse of factual information to target one’s ethnic identity—a core element of social belonging and heritage—can foster strong ingroup solidarity, defensiveness, and mobilization. This solidarity, in turn, may give rise to ethnically induced echo chambers that collectively discredit the outgroup, create entrenched positions, thereby intensifying interethnic division (Pérez-Escolar & Noguera-Vivo, 2021; Tucker et al., 2018). Conversely, the insignificant mediating effects of outgroup negative perception in this study may stem from its potential in evoking emotional reactions and hostile feelings rather than shaping concrete positions. Since ethnic polarization here is defined in terms of positional divergence on ethnically polarized issues—a primarily cognitive dimension—ingroup positive perception appears to play a more decisive role in driving polarization than explicit outgroup derogation, which operates mainly through affective response.

Implications and Future Research Directions

Previous studies have explored the association of disinformation with polarization in broad terms, often failing to independently measure misinformation and disinformation despite their conceptual distinction—a limitation highlighted in the literature (Marino & Iannelli, 2023). This study, through a conceptually distinct measurement of disinformation, offers a meaningful contribution to existing scholarship. In addition, to our knowledge, it represents the first exploratory attempt to measure malinformation and examine its relationship with social media polarization, providing novel insights into the field. Furthermore, by situating the analysis within a non-Western context and focusing on a distinct type of polarization, ethnic polarization, this study substantially enriches this strand of literature (Asimovic et al., 2021, 2023; Bradley & Chauchard, 2022).

This study also has certain limitations. First, the concepts “disinformation” and “malinformation,” while used by scholars and practitioners, are less familiar to lay respondents and may be difficult to distinguish, particularly when assessments rely on subjective perceptions. We observed a substantial correlation between these two constructs (r = .60) in our measurement, suggesting overlap in how participants interpreted and responded to their relevant items. Future research should address this challenge by providing contextual examples or vignettes to help respondents differentiate between malinformation and disinformation. Second, because this study uses cross-sectional data, it can only assess associations between variables and cannot establish causal relationships. Future research may benefit from experimental and longitudinal designs to explore causal links. Third, the use of a convenience sampling strategy and the research contextual dependence may limit the generalizability of these findings. The sample primarily consists of a younger, male, Tajik-heavy, and more educated population, which reflects a subpopulation of Afghanistan rather than the whole society. This necessitates further research with more diverse and representative samples to enhance generalizability. Finally, this study measured ethnic polarization based on interethnic polarized topics in Afghanistan, which, although it aligns with established practices in measuring polarization (Kubin & Von Sikorski, 2021; Mason, 2018), it remains highly context-dependent. At the same time, it introduces a conceptual shift in how polarization can be measured beyond the political and Western context. This perspective can guide future research to identify locally salient issues on which groups hold polarized views, providing a baseline against which the role of social media in intensifying or mitigating divisions can be assessed, particularly in societies where cleavages such as ethnicity, religion, or regional loyalty—rather than political parties—function as primary axes of division.

Supplemental Material

sj-docx-1-sms-10.1177_20563051261417516 – Supplemental material for The Conjuncture of Intentionality, Facticity, and Identity: Exposure to Disinformation and Malinformation on Social Media and Their Association With Ethnic Polarization

Supplemental material, sj-docx-1-sms-10.1177_20563051261417516 for The Conjuncture of Intentionality, Facticity, and Identity: Exposure to Disinformation and Malinformation on Social Media and Their Association With Ethnic Polarization by Qurban Hussain Pamirzad and Qiang Chen in Social Media + Society

Footnotes

ORCID iDs

Qurban Hussain Pamirzad

Qiang Chen

Ethical Considerations

This study adhered to internationally recognized ethical principles for research with human participants. Informed consent was obtained prior to voluntary participation, and anonymity was ensured by not collecting personally identifiable information (e.g. names, phone numbers, email addresses, or places of residence), thereby protecting privacy and minimizing any potential risks.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This article has been supported by the National Social Science Foundation of China (Grant No. 23BXW029).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

Author biographies

Qurban Hussain Pamirzad is a PhD candidate at the School of Journalism and New Media, Xi’an Jiaotong University in China. His research interests are social media polarization, public diplomacy, and public opinion.

Qiang Chen is a Professor at the School of Journalism and New Media at Xi’an Jiaotong University in China. His research interests are government social media and health communication.

References

Aïmeur

Amri

Brassard

(2023). Fake news, disinformation, and misinformation in social media: A review. Social Network Analysis and Mining, 13(1), Article 30.

Allcott

Gentzkow

(2017). Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2), 211–236.

Arora

S. D.

Singh

G. P.

Chakraborty

Maity

(2022). Polarization and social media: A systematic review and research agenda. Technological Forecasting and Social Change, 183, Article 121942.

Asimovic

Nagler

Bonneau

Tucker

J. A.

(2021). Testing the effects of Facebook usage in an ethnically polarized setting. Proceedings of the National Academy of Sciences of the United States of America, 118(25), Article e2022819118.

Asimovic

Nagler

Tucker

J. A.

(2023). Replicating the effects of Facebook deactivation in an ethnically polarized setting. Research & Politics, 10(4), Article 205157.

C. H.

K. K.

Chiu

D. K.

(2022). The role of online misinformation and fake news in ideological polarization: Barriers, catalysts, and implications. Information Systems Frontiers, 1, Article 24.

Borthakur

Kotokey

(2020). Ethnicity or religion? The genesis of the Taliban movement in Afghanistan. Asian Affairs, 51(4), 817–837.

Bradley

Chauchard

(2022). The ethnic origins of affective polarization: Statistical evidence from cross-national data. Frontiers in Political Science, 4, Article 920615.

Bradshaw

Howard

P. N.

(2018). The global organization of social media disinformation campaigns. Journal of International Affairs, 71(15), 23–32.

10.

Brady

W. J.

Wills

J. A.

Jost

J. T.

Tucker

J. A.

Van Bavel

J. J.

(2017). Emotion shapes the diffusion of moralized content in social networks. Proceedings of the National Academy of Sciences of the United States of America, 114(28), 7313–7318.

11.

Brewer

M. B.

(1999). The psychology of prejudice: Ingroup love and outgroup hate? Journal of Social Issues, 55(3), 429–444.

12.

Broda

Strömbäck

(2024). Misinformation, disinformation, and fake news: Lessons from an interdisciplinary, systematic literature review. Annals of the International Communication Association, 48(2), 139–166.

13.

Bruns

dos Santos Choucair

Esau

Svegaard

S. F.

Vilkins

(2024). Polarization in online spaces: Distinguishing forms of polarized politics. In Lilleker

Jackson

Kalsnes

Mellado

Trevisan

Veneti

(Eds.), The Routledge handbook of political campaigning (pp. 45–57). Routledge.

14.

Cohen

West

S. G.

Aiken

L. S.

(2013). Applied multiple regression/correlation analysis for the behavioral sciences. Routledge.

15.

Cooke

N. A.

(2021). Media and information literacies as a response to misinformation and populism. In Tumber

Waisbord

(Eds.), The Routledge companion to media disinformation and populism (pp. 489–497). Routledge.

16.

Deinla

I. B.

Mendoza

G. A. S.

Ballar

K. J.

Yap

J. K.

(2022). The link between fake news susceptibility and political polarization among youth in the Philippines. Asian Journal of Political Science, 30(2), 160–181.

17.

Dinakhel

M. A.

(2018). An analysis of the conflict between the Pashto and Dari languages of Afghanistan. Central Asia, 83(Winter), 79–99.

18.

Etikan

(2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5, 1–4.

19.

Evans

Need

(2002). Explaining ethnic polarization over attitudes toward minority rights in eastern Europe: A multilevel analysis. Social Science Research, 31(4), 653–680.

20.

Flynn

D. J.

Nyhan

Reifler

(2017). The nature and origins of misperceptions: Understanding false and unsupported beliefs about politics. Political Psychology, 38, 127–150.

21.

Freelon

Bossetta

Wells

Lukito

Xia

Adams

(2022). Black trolls matter: Racial and ideological asymmetries in social media disinformation. Social Science Computer Review, 40(3), 560–578.

22.

French

Storey

V. C.

Wallace

(2024). A typology of disinformation intentionality and impact. Information Systems Journal, 34(4), 1324–1354.

23.

Gelovani

Theocharis

Koc-Michalska

Bimber

(2025). Intergroup ethnocentrism and social media: Evidence from three Western democracies. Information, Communication & Society, 28(4), 597–615.

24.

Gonçalves-Segundo

P. R.

(2022). Fake news, moral panic, and polarization in Brazil: A critical discursive approach. Linguistic Frontiers, 5(2), 51–60.

25.

Goyanes

Lee

Salgado

Gil

Zúñiga

(2025). Enhancing self-perceived disinformation identification in democracy: The impact of fact-checking integration into daily news consumption practices. Journal of Broadcasting & Electronic Media, 69, 331–352.

26.

Grambo

(2018). Fake news and racial, ethnic, and religious minorities: A precarious quest for truth. University of Pennsylvania Journal of Constitutional Law, 21, Article 1299.

27.

Hameleers

(2022). Populist disinformation: Are citizens with populist attitudes affected most by radical right-wing disinformation?. Media and Communication, 10(4), 129–140.

28.

Hameleers

Van der Meer

Vliegenthart

(2022). Civilized truths, hateful lies? Incivility and hate speech in false information–evidence from fact-checked statements in the US. Information, Communication & Society, 25(11), 1596–1613.

29.

Hayes

A. F.

(2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76(4), 408–420.

30.

Hayes

A. F.

Montoya

A. K.

Rockwood

N. J.

(2017). The analysis of mechanisms and their contingencies: PROCESS versus structural equation modeling. Australasian Marketing Journal, 25(1), 76–81.

31.

Hopp

Ferrucci

Vargo

C. J.

(2020). Why do people share ideologically extreme, false, and misleading content on social media? A self-report and trace data–based analysis of countermedia content dissemination on Facebook and Twitter. Human Communication Research, 46(4), 357–384.

32.

Horne

Adams

Gidron

(2025). The economy and affective polarization. In Torcal

Harteveld

(Eds.), Handbook of affective polarization (p. 346). Edward Elgar.

33.

Human Rights Watch. (2005). Blood-stained hands past atrocities in Kabul and Afghanistan’s legacy of impunity. https://www.hrw.org/reports/2005/afghanistan0605/afghanistan0605.pdf

34.

Ibrahimi

(2023). A violent nexus: ethnonationalism, religious fundamentalism, and the Taliban. The Review of Faith & International Affairs, 21(3), 22–37.

35.

Iyengar

Lelkes

Levendusky

Malhotra

Westwood

S. J.

(2019). The origins and consequences of affective polarization in the United States. Annual Review of Political Science, 22(1), 129–146.

36.

Iyengar

Sood

Lelkes

(2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431.

37.

Jenke

(2024). Affective polarization and misinformation belief. Political Behavior, 46(2), 825–884.

38.

Jones-Jang

S. M.

Kim

D. H.

Kenski

(2021). Perceptions of mis- or disinformation exposure predict political cynicism: Evidence from a two-wave survey during the 2018 US midterm elections. New Media & Society, 23(10), 3105–3125.

39.

Kaiser

Vaccari

Chadwick

(2022). Partisan blocking: Biased responses to shared misinformation contribute to network polarization on social media. Journal of Communication, 72(2), 214–240.

40.

Kubin

Von Sikorski

(2021). The role of (social) media in political polarization: A systematic review. Annals of the International Communication Association, 45(3), 188–206.

41.

Leake

(2022). States, nations, and self-determination: Afghanistan and decolonization at the United Nations. Journal of Global History, 17(2), 272–291.

42.

Lee

J. K.

Choi

Kim

(2014). Social media, network heterogeneity, and opinion polarization. Journal of Communication, 64(4), 702–722.

43.

Lewandowsky

Ecker

U. K. H.

Cook

(2017). Beyond misinformation: Understanding and coping with the “post-truth” era. Journal of Applied Research in Memory and Cognition, 6(4), 353–369.

44.

Lim

W. M.

(2023). Fact or fake? The search for truth in an infodemic of disinformation, misinformation, and malinformation with deepfakes and fake news. Journal of Strategic Marketing, 1–37. https://doi.org/10.1080/0965254X.2023.2253805

45.

Mahboob

Qatey

Hanif

(2024). The position of common languages in Afghanistan in the last two centuries. Science and Education, 5(3), 632–643.

46.

Marino

Iannelli

(2023). Seven years of studying the associations between political polarization and problematic information: A literature review. Frontiers in Sociology, 8, Article 1174161.

47.

Mason

(2018). Ideologues without issues: The polarizing consequences of ideological identities. Public Opinion Quarterly, 82(S1), 866–887.

48.

Pamirzad

Q. H.

(2025). A qualitative exploration of hate speech manifestation in Afghanistan’s Twitter-sphere. Jurnal Komunikasi: Malaysian Journal of Communication, 41(3), 1–20.

49.

Pamirzad

Q.H.

Chen

(2026). Exploring the Association of Social Media Connectivity and Interactivity with Ethnic Polarization: An Interdependent Hierarchical Affordance Perspective. Computers in Human Behavior, 178, Article 108919.

50.

Pamirzad

Q. H.

Susyall

G. A.

Hakimi

A. B.

(2025). A moderated mediation analysis of the impact of Facebook network heterogeneity on affective polarization among ethnic groups in Afghanistan. Journal of Information, Communication and Ethics in Society, 23(3), 415–432.

51.

Pérez-Escolar

Noguera-Vivo

J. M.

(2021). Introduction to the dilemmas and struggles of a participatory society. In Pérez-Escolar

Noguera-Vivo

J. M.

(Eds.), Hate speech and polarization in participatory society (pp. 1–11). Routledge.

52.

Rashidi

Göksel

G. U.

(2019). Nation building process in Afghanistan. Saudi Journal of Humanities and Social Sciences, 4(4), 279–289.

53.

Recuero

Soares

F. B.

Zago

(2021). Polarization, hyperpartisanship, and echo chambers: How the disinformation about COVID-19 circulates on Twitter. Contracampo-Brazilian Journal of Communication, 4(1), 1–16.

54.

Riley

J. K.

(2022). Angry enough to riot: An analysis of in-group membership, misinformation, and violent rhetoric on TheDonald.win between election day and inauguration. Social Media + Society, 8(2), Article 109189.

55.

Saadati

Chehreh

Ansari

(2024). The role of social media platforms in spreading misinformation targeting specific racial and ethnic groups: A brief review [Conference session]. 36th Conference of the Open Innovations Association FRUCT, Helsinki, Finland.

56.

Santos-D’Amorim

de Oliveira Miranda

M. K. F.

(2021). Misinformation, disinformation, and malinformation: clarifying the definitions and examples in disinfodemic times. Encontros Bibli: Revista Eletrônica de Biblioteconomia e Ciência da Informação, 26, 1–23. https://doi.org/10.5007/1518-2924.2021.e76900

57.

Schudson

Zelizer

Derakhshan

Wardle

Fletcher

Nielsen

R. K.

Nießner

(2017). Understanding and addressing the disinformation ecosystem. Annenberg School for Communication, https://firstdraftnews.org/wp-content/uploads/2018/03/The-Disinformation-Ecosystem-20180207-v4.pdf

58.

Singer

P. W.

Brooking

E. T.

(2018). LikeWar: The weaponization of social media. Eamon Dolan Books.

59.

Stephan

W. G.

Boniecki

K. A.

Ybarra

Bettencourt

Ervin

K. S.

Jackson

L. A.

Renfro

C. L.

(2002). The role of threats in the racial attitudes of Blacks and Whites. Personality and Social Psychology Bulletin, 28(9), 1242–1254.

60.

Tajfel

Turner

J. C.

Austin

W. G.

(1982). Social psychology of intergroup relations. Annual Review of Psychology, 33(1), 1–39.

61.

Teruel-Rodríguez

(2023). Increasing political polarization with disinformation: A comparative analysis of the European quality press. Profesional de la Información, 32(6), Article e320612.

62.

Tong

Gill

Valenzuela

Rojas

(2020). “Fake news is anything they say!”: Conceptualization and weaponization of fake news among the American public. Mass Communication and Society, 23(5), 755–778.

63.

Tucker

J. A.

Guess

Barberá

Vaccari

Siegel

Sanovich

Stukal

Nyhan

(2018). Social media, political polarization, and political disinformation: A review of the scientific literature. Hewlett Foundation. https://shorturl.at/XCvch

64.

Turcilo

Obrenovic

(2020). Misinformation, disinformation, malinformation: Causes, trends, and their influence on democracy. Heinrich Böll Foundation.

65.

Turner

J. C.

Brown

R. J.

Tajfel

(1979). Social comparison and group interest in ingroup favoritism. European Journal of Social Psychology, 9(2), 187–204.

66.

Vasist

P. N.

Chatterjee

Krishnan

(2024). The polarizing impact of political disinformation and hate speech: A cross-country configural narrative. Information Systems Frontiers, 26(2), 663–688.

67.

Vicario

M. D.

Quattrociocchi

Scala

Zollo

(2019). Polarization and fake news: Early warning of potential misinformation targets. ACM Transactions on the Web (TWEB), 13(2), 1–22.

68.

Wafayezada

M. Q.

(2024). Unpacking affective polarization in Afghanistan: Ethnic politics, elite competition, and online divisive content. Toda Peace Institute. https://toda.org/assets/files/resources/policy-briefs/t-pb-193_affective-polarization-in-afghanistan_wafayezada.pdf

69.

Wafayezada

M. Q.

Shafiq

(2025). The demographic dynamics of conflict and violence: ethnicity, youth bulge, and population mobility in Afghanistan. Intechopen. https://doi.org/10.5772/intechopen.1006878

70.

Wani

Z. A.

(2024). Women, extremism and repression under Taliban 2.0 in Afghanistan: Beyond the good. Small Wars & Insurgencies, 35(7), 1212–1232.

71.

Wardle

Derakhshan

(2017). Information disorder: Toward an interdisciplinary framework for research and policymaking (pp. 1–107). Council of Europe.

72.

Weismueller

Gruner

R. L.

Harrigan

Coussement

Wang

(2024). Information sharing and political polarization on social media: The role of falsehood and partisanship. Information Systems Journal, 34(3), 854–889.

Supplementary Material

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The Conjuncture of Intentionality,Facticity,and Identity: Exposure to Disinformation and Malinformation on Social Media and Their Association With Ethnic Polarization

Abstract

Keywords

Introduction

Literature Review

Weaponized Information and Ethnic Polarization

The Mediating Effects of Ingroup Positive and Outgroup Negative Perceptions

Methodology

Measurement of Variables

Disinformation Exposure

Malinformation Exposure

Ingroup Positive Perception

Outgroup Negative Perception

Ethnic Polarization

Control Variables

Analytical Approach

Results

Discussion and Conclusion

Implications and Future Research Directions

Supplemental Material

sj-docx-1-sms-10.1177_20563051261417516 – Supplemental material for The Conjuncture of Intentionality, Facticity, and Identity: Exposure to Disinformation and Malinformation on Social Media and Their Association With Ethnic Polarization

Footnotes

ORCID iDs

Ethical Considerations

Funding

Declaration of conflicting interests

Supplemental material

Author biographies

References

Supplementary Material