MPA 2026 - For Believing in Uncertain Times: Anomie and Rejection in Misinformation Evaluation

Poster

knitr::include_graphics("shin poster.jpg")

Poster Text

INTRODUCTION

The use of social media to share mental health information and to obtain virtual social support (Chen & Wang, 2021) is increasingly widespread. With this comes concerns regarding misinformation, as information online is often inaccurate (misinformation) or deceptive (disinformation; Hudon et al., 2025; Aïmeur et al., 2023). This study examines the roles of expectation of rejection and anomie on the belief in and endorsement of untrue information online (i.e., misinformation acceptance).

Expectation of rejection occurs when individuals experience internalized anticipatory feelings of being treated poorly due to stigma (London et al., 2020; Botha & Frost 2020). This may be associated with vulnerability to misinformation, as it often involves themes of fearfulness and danger (De Los Santos & Nabi, 2019). Differentiating between accurate information and misinformation also requires effortful cognition, rendered more difficult in a state of heightened emotion (Petty & Brinol, 2012).

Anomie is defined as the shared perception of social disintegration and deregulation where norms are atypical and uncertain due to social change (Teymoori et al., 2017; Durkehim, 1952; Abrutyn, 2019). The combination of reliable and unreliable information online (Wardle & Derakhshan, 2017) may increase feelings of anomie and produce a higher chance of misinformation acceptance.

This study tests the hypothesis that expectation of rejection (H1) and anomie (H2) will be associated with higher rates of misinformation acceptance, and the impact of experiences with discrimination on these relationships.

METHOD

In Spring 2025, data was collected from college students and adults with no college experience (n = 184). Participants responded to pre-manipulation items asking about expectation of rejection. Then, they were exposed to either majority accurate/inaccurate social media posts about psychology (condition), and rated perceptions of truthfulness and accuracy (acceptance). Lastly, participants responded to measures of anomie and other factors.

DISCUSSION

We began by hypothesizing that expectation of rejection (H1) and anomie (H2) would predict misinformation acceptance. Individuals with rejection sensitivity may also perceive societal dysfunction, which might stir up emotional distress that drives the inclination to seek out validation online. This might increase the reception of information that is inaccurate.

Findings suggest that our hypotheses were not supported. However, we did find a significant positive relationship between expectation of rejection and anomie. The perception of whether one will be accepted by others seems to be associated with the perception of general societal functioning. This finding suggests that there may be a connection between societal health and individual wellbeing.

We predicted that this perception of societal instability would impact the individual’s ability to rationally evaluate the truthfulness and accuracy of information. Findings were insignificant, there was no relationship between anomie and misinformation acceptance. We also predicted that discrimination would moderate the relationships between expectation of rejection, anomie, and misinformation acceptance, but our findings were non-significant across the board.

Moreover, we found only a weak relationship between expectation of rejection and misinformation acceptance. This relationship is negatively associated, suggesting that when expectation of rejection increases, misinformation acceptance decreases. This demonstrates that expectation of rejection may serve as protective barriers as it elevates feelings of cautiousness and wariness of others. This may serve individuals well in a space that is plagued by disinformation prevalence. This, however, may have adverse psychological effects.

In conclusion, the counterintuitive findings offer insight into the intricate relationship between individuals’ engagement with online information and social perception. Limitations of our current study include our small sample size.

REFERENCES

Abrutyn, S. (2019). Toward a General Theory of Anomie The Social Psychology of Disintegration. European Journal of Sociology / Archives Européennes de Sociologie, 60(1), 109–136. https://doi.org/10.1017/S0003975619000043

Aïmeur, E., Amri, S., & Brassard, G. (2023). Fake news, disinformation and misinformation in social media: A review. Social Network Analysis and Mining, 13(1), 30. https://doi.org/10.1007/s13278-023-01028-5

Botha, M., & Frost, D. M. (2020). Extending the Minority Stress Model to Understand Mental Health Problems Experienced by the Autistic Population. Society and Mental Health, 10(1), 20–34. https://doi.org/10.1177/2156869318804297

Chen, J., & Wang, Y. (2021). Social Media Use for Health Purposes: Systematic Review. Journal of Medical Internet Research, 23(5), e17917. https://doi.org/10.2196/17917

De Los Santos, T. M., & Nabi, R. L. (2019). Emotionally Charged: Exploring the Role of Emotion in Online News Information Seeking and Processing. Journal of Broadcasting & Electronic Media, 63(1), 39–58. https://doi.org/10.1080/08838151.2019.1566861

Durkheim, E. (1952). Suicide, a study in sociology. Routledge & K. Paul London.

Hudon, A., Perry, K., Plate, A.-S., Doucet, A., Ducharme, L., Djona, O., Aguirre, C. T., & Evoy, G. (2025). Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok Videos. Journal of Medical Internet Research, 27(1), e64225. https://doi.org/10.2196/64225

London, B., Macdonald, J., & Inman, E. (2020). Invited Reflection: Rejection Sensitivity as a Social-Cognitive Model of Minority Stress. Archives of Sexual Behavior, 49(7), 2281–2286. https://doi.org/10.1007/s10508-020-01675-1

Petty, R. E., & Briñol, P. (2012). The elaboration likelihood model. In Handbook of theories of social psychology, Vol. 1 (pp. 224–245). Sage Publications Ltd. https://doi.org/10.4135/9781446249215.n12

Teymoori, A., Bastian, B., & Jetten, J. (2017). Towards a psychological analysis of anomie. Political Psychology, 38(6), 1009–1023. (2016-53930-001). https://doi.org/10.1111/pops.12377

Wardle, C., & Derakhshan, H. (2017). Information disorder: Toward an interdisciplinary framework for research and policymaking (Vol. 27). Council of Europe Strasbourg.

Code

Load Libraries

library(sjPlot)
library(ggplot2)
library(haven)
library(psych)

Load Data

df <- read_sav("poster data.sav")
df <- subset(df, outlier != 1)

Hypothesis 1

Expectation of rejection will predict anomie, moderated by everyday discrimination.

df$eds_rc <- NA
df$eds_rc[df$eds > 2.26] <- "1 high"
df$eds_rc[df$eds < 2.27] <- "0 low"
df$eds_di <- NA
df$eds_di[df$eds_rc == "0 low"] <- 0
df$eds_di[df$eds_rc == "1 high"] <- 1
df$eds_di <- as.factor(df$eds_di)

df$ers_new <- (df$Q8_1 + df$Q8_4 + df$Q8_5 + df$Q8_6)/4
df$anomie <- as.numeric(scale(df$anomie, center=T, scale=T))
df$ers_new <- as.numeric(scale(df$ers_new, center=T, scale=T))

reg2 <- lm(anomie ~ ers_new*eds_di + condition, data=df)
summary(reg2)

Call:
lm(formula = anomie ~ ers_new * eds_di + condition, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.38111 -0.48535  0.09074  0.68502  1.95926 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)  
(Intercept)     -0.11306    0.13311  -0.849   0.3968  
ers_new          0.10748    0.13640   0.788   0.4317  
eds_di1          0.33312    0.15828   2.105   0.0367 *
conditionm      -0.12163    0.14641  -0.831   0.4072  
ers_new:eds_di1  0.09262    0.16794   0.552   0.5820  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.9667 on 178 degrees of freedom
Multiple R-squared:  0.08612,   Adjusted R-squared:  0.06558 
F-statistic: 4.193 on 4 and 178 DF,  p-value: 0.002864

p <- plot_model(reg2, type="int", show.data =T)

Data points may overlap. Use the `jitter` argument to add some amount of
  random variation to the location of data points and avoid overplotting.

p <- p +
  # Labels — update these to match your variables
  labs(
    title = "Expectation of Rejection, Discrimination, and Anomie",
    subtitle = "Adj. R² = .07, F(3,180) = 4.19, p = .003",
    x = "Expectation of Rejection",
    y = "Anomie",
    color = "Discrimination"
  ) +
  scale_color_manual(
    values = c("#2E86AB", "#E84855"),
    labels = c("Low", "High")   # <-- change these
  ) +
  # Clean theme
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", margin = margin(b = 8)),
    plot.subtitle   = element_text(color = "grey50", margin = margin(b = 12)),
    axis.title      = element_text(face = "bold", color = "grey30"),
    axis.text       = element_text(color = "grey20"),
    legend.title    = element_text(face = "bold"),
    legend.position = "bottom",
    panel.grid.minor  = element_blank(),
    panel.grid.major  = element_line(color = "grey92"),
    plot.background   = element_rect(fill = "white", color = NA),
    plot.margin       = margin(20, 20, 20, 20),
    legend.key.spacing.y = unit(0.5, "lines"),
    text = element_text(size = 12)
  ) +

  # Color palette for the interacting groups
  scale_fill_manual(values  = c("#2E86AB", "#E84855"))

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

p

# ggsave("interaction_plot1.svg", plot = p, width = 12.18, height = 6.7, bg = "transparent")

Hypothesis 2

Anomie will predict misinformation acceptance, moderated by everyday discrimination.

df$mis_rating <- as.numeric(scale(df$mis_rating, center=T, scale=T))
df$acc_rating <- as.numeric(scale(df$acc_rating, center=T, scale=T))

reg4 <- lm(mis_rating ~ anomie*eds_di + acc_rating + condition, data=df)
summary(reg4)

Call:
lm(formula = mis_rating ~ anomie * eds_di + acc_rating + condition, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.15441 -0.54597  0.01556  0.55991  1.94362 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -0.05545    0.10316  -0.538    0.592    
anomie          0.05305    0.07283   0.728    0.467    
eds_di1        -0.01681    0.12000  -0.140    0.889    
acc_rating      0.64840    0.05833  11.115   <2e-16 ***
conditionm      0.13242    0.11623   1.139    0.256    
anomie:eds_di1 -0.04249    0.12576  -0.338    0.736    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7727 on 176 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.4195,    Adjusted R-squared:  0.403 
F-statistic: 25.43 on 5 and 176 DF,  p-value: < 2.2e-16

p <- plot_model(reg4, type="int", show.data =T)

Data points may overlap. Use the `jitter` argument to add some amount of
  random variation to the location of data points and avoid overplotting.

p +
  # Labels — update these to match your variables
  labs(
    title = "Anomie, Misinformation Acceptance, and Discrimination",
    subtitle = "Adj. R² = .40, F(5,176) = 25.43, p < .001",
    x = "Anomie",
    y = "Misinformation Acceptance",
    color = "Everyday Discrimination"
  ) +
  scale_color_manual(
    values = c("#2E86AB", "#E84855"),
    labels = c("Low", "High")   # <-- change these
  ) +
  # Clean theme
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", size = 15, margin = margin(b = 4)),
    plot.subtitle   = element_text(color = "grey50", size = 11, margin = margin(b = 12)),
    axis.title      = element_text(face = "bold", color = "grey30"),
    axis.text       = element_text(color = "grey20"),
    legend.title    = element_text(face = "bold", size = 11),
    legend.position = "bottom",
    panel.grid.minor  = element_blank(),
    panel.grid.major  = element_line(color = "grey92"),
    plot.background   = element_rect(fill = "white", color = NA),
    plot.margin       = margin(20, 20, 20, 20)
  ) +

  # Color palette for the interacting groups
  scale_fill_manual(values  = c("#2E86AB", "#E84855"))

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Hypothesis 3

Expectation of rejection will predict misinformation acceptance, moderated by everyday discrimination

reg6 <- lm(mis_rating ~ ers*eds_di + acc_rating + condition, data=df)
summary(reg6)

Call:
lm(formula = mis_rating ~ ers * eds_di + acc_rating + condition, 
    data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.99074 -0.52918  0.01402  0.51154  2.02540 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.204706   0.223502   0.916    0.361    
ers         -0.173094   0.126482  -1.369    0.173    
eds_di1     -0.005253   0.297937  -0.018    0.986    
acc_rating   0.658226   0.057667  11.414   <2e-16 ***
conditionm   0.103701   0.116725   0.888    0.376    
ers:eds_di1  0.056087   0.155766   0.360    0.719    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.7662 on 176 degrees of freedom
  (1 observation deleted due to missingness)
Multiple R-squared:  0.4292,    Adjusted R-squared:  0.413 
F-statistic: 26.47 on 5 and 176 DF,  p-value: < 2.2e-16

p <- plot_model(reg6, type="int", show.data =T)

Data points may overlap. Use the `jitter` argument to add some amount of
  random variation to the location of data points and avoid overplotting.

p +
  # Labels — update these to match your variables
  labs(
    title = "Expectation of Rejection, Misinformation Acceptance, \nand Discrimination",
    subtitle = "Adj. R² = .41, F(5,176) = 26.47, p < .001",
    x = "Expectation of Rejection",
    y = "Misinformation Acceptance",
    color = "Everyday Discrimination"
  ) +
  scale_color_manual(
    values = c("#2E86AB", "#E84855"),
    labels = c("Low", "High")   # <-- change these
  ) +
  # Clean theme
  theme_minimal(base_size = 13) +
  theme(
    plot.title      = element_text(face = "bold", size = 15, margin = margin(b = 4)),
    plot.subtitle   = element_text(color = "grey50", size = 11, margin = margin(b = 12)),
    axis.title      = element_text(face = "bold", color = "grey30"),
    axis.text       = element_text(color = "grey20"),
    legend.title    = element_text(face = "bold", size = 11),
    legend.position = "bottom",
    panel.grid.minor  = element_blank(),
    panel.grid.major  = element_line(color = "grey92"),
    plot.background   = element_rect(fill = "white", color = NA),
    plot.margin       = margin(20, 20, 20, 20)
  ) +

  # Color palette for the interacting groups
  scale_fill_manual(values  = c("#2E86AB", "#E84855"))

Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.