Hate in Context

Week 5: Threatening Events and Hate Crime · LENS preview

Violeta Haas

Department of Political Science

School of Social Sciences and Philosophy

Trinity College Dublin

This Week

  1. Theoretical backdrop
  2. Álvarez-Benjumea & Winter (2020)
  3. Riaz, Bischof & Wagner (2024)
  4. Dipoppa, Grossman & Zonszein (2023)
  5. Outlook

Theoretical backdrop

Discussion: expert groups

Split into Expert Group A and Expert Group B (≈5 min).

Group A — Social norms

  1. Define social norms.
  2. How do attitudes, social norms, and behavior differ? (provide an example for each)
  3. Can behavior change without attitudes changing?

Group B — Vicarious retribution

  1. Sketch a schematic figure of the logic of vicarious retribution.
  2. How do triggering events translate into hate crime?

Then: exchange between experts — Group A presents their definitions and examples; Group B presents their schematic figure.

1. What is a social norm?

[discussed in class]

2. Attitudes, behavior, and norms

[discussed in class]

If the player fails, use: youtube.com/watch?v=o8BkzvP19v4

[discussed in class]

People often look for environmental cues — what others are doing — to figure out how to act (descriptive norms).

Meme: when you have no idea what you are doing but pretend like you do

Book recommendation

Book cover: Bicchieri, Norms in the Wild

Bicchieri, C. (2017). Norms in the wild: How to diagnose, measure, and change social norms. Oxford University Press.

Triggering event

[discussed in class]

Shared grievance and collective blame / liability

[discussed in class]

Vicarious retribution

[discussed in class]

Prolonged intergroup conflict

[discussed in class]

From trigger to hate crime: cognition and affect

[discussed in class]

Today’s papers ask how discrete shocks (triggering events) shift hostility — offline and online — including by changing what seems acceptable (social norms) and by redirecting blame onto uninvolved out-group members (vicarious retribution).

Álvarez-Benjumea & Winter (2020)

Overview

  • Research question: Do terrorist attacks increase online hate speech — and do descriptive norms moderate that effect?
  • Y: hate score of forum comments (1–9 hostility ratings)
  • X: terrorist attacks during fieldwork (Würzburg & Ansbach, July 2016)
  • Moderator: descriptive norms (no / weak / strong)
  • Theory: attacks can weaken antiracist norms → latent xenophobic attitudes become behavior (hate speech)
  • Design: unexpected event during survey/experiment (UEDS) + lab-in-the-field norm manipulation

Unexpected Event During Survey (UEDS) design

Idea: attacks fall between two waves of the same experiment. Compare comments from the before wave vs the after wave.

Comparison around the attacks

Different participants in the same online experiment

UEDS design: Wave 1 on 10 July; Würzburg and Ansbach attacks 18–24 July; Wave 2 from 3 August

Experimental forum

Álvarez-Benjumea and Winter 2020 Figure 1: Screenshot of the no-norm experimental forum condition

  1. Participants join an online forum, see a prompt photo and seed comments that manipulate the descriptive anti-hate norm:
    • No norm: mix of hostile, neutral, and positive comments (norm ambiguous)
    • Weak norm: only neutral and positive comments (anti-hate norm signaled)
    • Strong norm: only very positive comments (anti-hate norm strongly signaled)
  2. Participants then leave their own comment.
    • Comments are rated for hostility on a hate score from 1–9.

Findings: mean change after attacks

Álvarez-Benjumea and Winter 2020: Post-attack mean change in hate score by norm condition

After the attacks, hate toward refugees rises when no norm is shown — but not under weak or strong norms. No parallel shift for unrelated group (i.e., gender rights).

Findings: extreme comments

Álvarez-Benjumea and Winter 2020: Quantile treatment effects of terrorist attacks by norm condition

Descriptive norms especially dampen the most extreme comments: effects concentrate in the upper tail of the hate-score distribution.

Group discussion (≈5 min)

Two main responses to online hate (Siegel 2020): content moderation vs counter-speech.

In groups of 3–4:

  1. For each approach, collect 2 points in favor and 2 drawbacks.
  2. What do Álvarez-Benjumea & Winter’s results speak for?
  3. What do you think is the best way to deal with hate speech?

Approach A — Content moderation

Ban accounts/communities; enforce platform rules (and, where relevant, legal standards).

Approach B — Counter-speech

Challenge or dilute hate with opposing messages (users, campaigns, or designed interventions).

Pros and cons

[discussed in class]

What do Álvarez-Benjumea & Winter speak for?

What is the best way to deal with hate speech?

Riaz, Bischof & Wagner (2024)

Overview

  • Research question: Do local immigrant-attributed crimes raise hate crimes against refugees?
  • Y: daily probability / count of hate crimes against refugees
  • X: immigrant-attributed crime event
  • Theory: vicarious retribution, i.e., natives retaliate against uninvolved out-group members after a triggering crime
  • Design: regression discontinuity in time (RDiT) around local crime events (Germany, 2015–2019)

Wait… RDiT? That sounds familiar!

Regression Discontinuity in Time (RDiT) design

Idea: a sudden local shock is a cutoff in time. Compare hate crimes from the before period vs the after period.

Comparison around local crime events

Same county, days close to the event

RDiT design: compare hate crimes before vs after immigrant-attributed crime, excluding day 0

Scope conditions

Scope condition: the subset of cases / conditions under which a theory applies.

Vicarious retribution applies when…

  • An antecedent event leaves one group with a grievance against another
  • A definable out-group is blamed for the triggering event
  • The event receives considerable publicity (King & Sutton, 2013)
  • An antecedent event leaves one group with a grievance against another
  • A definable out-group is blamed for the triggering event
  • The event receives considerable publicity (King & Sutton, 2013)

→ Riaz et al. (2024): considerable publicity is not necessary — smaller local triggering events can also have an effect.

How do natives—including potential hate crime perpetrators—hear about immigrant-attributed crime in their locality?

[discussed in class]

[discussed in class]

Findings: daily hate-crime rate around events

Riaz et al. 2024 Figure 2: Daily rate of hate crimes in the two weeks before and after an immigrant crime event

Hate crimes surge locally right after immigrant-attributed crimes — but the spike fades within ~4 days, consistent with an emotional trigger (affect) rather than lasting attitude change.

Findings: where the backlash is stronger

Riaz et al. 2024 Figure 3: Heterogeneous LATE estimates by AfD strength and foreign-population context

Backlash is stronger where far-right mobilization potential is high and where the foreign population has recently increased — not simply where many foreigners already live.

Anything else that didn’t convince you?

Outlook

Next week: political elites

Week 6 (Mon 19/Wed 21 Oct): political elites and hate crime.

Required readings

  • Romarri, A. (2020). Do far-right mayors increase the probability of hate crime? Evidence from Italy. Working paper.
  • Dancygier, R. (2023). Hate crime supporters are found across age, gender, and income groups and are susceptible to violent political appeals. Proceedings of the National Academy of Sciences, 120(7), Article e2212757120.
  • Zonszein, S., & Grossman, G. (2025). Voted in, standing out: Public response to immigrants’ political accession. American Journal of Political Science, 69(2), 718–733.

Weekly comments due Sunday 18 Oct, 11:59 pm.

Questions?

Goodbye!

See you next week.