The document explores how moral emotions influence the spread of moralized content in online social networks, particularly focusing on political discourse. It details findings from studies conducted on Twitter that illustrate the role of moral-emotional language in promoting content diffusion related to topics such as gun control, same-sex marriage, and climate change, emphasizing the effects of political identity. The results suggest that conservatives may experience a greater moral contagion effect than liberals, with various factors such as ideology, gender, age, and race impacting the dissemination of moralized content.

1. An ideological asymmetry in moral
contagion among political leaders
Ü @jayvanbavel | New York University
psyarxiv.com/43n5e/

2. What factors influence the diffusion of
moralized content in social networks?

3. Stage Theory Social-Intuitionist
Model
Dyadic Morality
Model
Moral Conviction
Model
Dual-Process
Model

4. Moral Contagion Hypothesis: moral emotion increases diffusion of
moralized content in online social networks.
Brady et al. (2017), PNAS

5. Moral emotions: emotions that reliably signal, either to others or to the
self, that something morally relevant has occurred (e.g., Keltner &
Haidt, 1999)
Moral contagion: the spread of moralized content as a result of people
incorporating others’ moral-emotional expressions as informational
input into their own evaluation of a situation that can inform their own
emotional state and also guide their decisions to share the content.

6. Moral contagion: the spread of moralized content as a result of:
• People incorporating others’ moral-emotional expressions as
information that informs their evaluation of a situation (e.g,.
Manstead & Fischer, 2001)
• This can inform their own emotional state and/or guide their
decisions to share the content.
.

7. Method
• Large social media sample from Twitter (N = 563,217)
• 3 studies, collecting Twitter messages on 3 different moral topics
• Gun control
• Same-sex marriage
• Climate change
Brady et al. (2017), PNAS

8. Measuring morality and emotion
• Morality and Emotion defined by their expression through language
in a message (Tausczik & Pennebaker, 2010)
Brady et al. (2017), PNAS

9. • Morality and Emotion defined by their expression through language
in a message (Tausczik & Pennebaker, 2010)
• Moral dictionary: Moral foundations lexicon (Graham, Haidt,
& Nosek, 2009) + piloted moral word list (Gantman & Van Bavel,
2014)
• Emotion dictionary: emotion words from Linguistic Inquiry and
Word Count software (LIWC) (Tausczik & Pennebaker, 2010)
Method
Brady et al. (2017), PNAS
Distinctly Moral Distinctly EmotionalMoral-Emotional

10. • Morality and Emotion defined by their expression through language
in a message (Tausczik & Pennebaker, 2010)
• Moral dictionary: Moral foundations lexicon (Graham, Haidt,
& Nosek, 2009) + piloted moral word list (Gantman & Van Bavel,
2014)
• Emotion dictionary: emotion words from Linguistic Inquiry and
Word Count software (LIWC) (Tausczik & Pennebaker, 2010)
Method
Brady et al. (2017), PNAS
Distinctly Moral Distinctly EmotionalMoral-Emotional

11. • Morality and Emotion defined by their expression through language
in a message (Tausczik & Pennebaker, 2010)
• Moral dictionary: Moral foundations lexicon (Graham, Haidt,
& Nosek, 2009) + piloted moral word list (Gantman & Van Bavel,
2014)
• Emotion dictionary: emotion words from Linguistic Inquiry and
Word Count software (LIWC) (Tausczik & Pennebaker, 2010)
Tweets with moral-emotional language
Brady et al. (2017), PNAS
Moral Word Emotion WordMoral-Emotional Word

12. Method
• Diffusion / Contagion defined by retweet count
• People typically retweet content because
1. they endorse the content
2. they trust the source (Metaxas et al., 2015)
Brady et al. (2017), PNAS

13. Moral-emotional language associated with significant
increases in diffusion
• Model: RT Count = distinctly moral language + distinctly emotional language +
moral-emotional language + covariates
Brady et al. (2017), PNAS
Gun Control Same-Sex Marriage Climate Change

14. Moral emotional language associated with significant
increases in diffusion
• Model: RT Count = distinctly moral language + distinctly emotional language +
moral-emotional language
Mean IRR = 1.20; 20% increase in retweets per ME word
Brady et al. (2017), PNAS
Gun Control Same-Sex Marriage Climate Change

15. Moral emotional language associated with significant
increases in diffusion
• Model: RT Count = distinctly moral language + distinctly emotional language +
moral-emotional language + covariates
Mean IRR = 1.20; 20% increase in retweets per ME word
Brady et al. (2017), PNAS
Gun Control Same-Sex Marriage Climate Change

16. Exploring boundary conditions
• Measured user ideology based on well-validated algorithm that
tracks political accounts each user follows (Barbera et al., 2015)
Moral-emotional
content
Information
Diffusion
Political Identity:
Receiver
Brady et al. (2017)

17. Moral emotional language associated with increases in
“echo-chamber” discourse
Liberals Conservatives
Node = user
Edge = retweet
Brady et al. (2017), PNAS

18. Results: Group Identity
Gun control
Same-sex
marriage
Climate change
Mean interaction IRR = 1.21 Brady et al. (2017), PNAS

19. Summary
• Moral emotion expression plays a key role in the diffusion of
moralized content
• Political identity of the receiver may be an important moderator
of this effect

20. Twitter study 2:
Moral contagion in the 2016 election and
partisanship

21. Moral-emotional
content
Information
Diffusion
Election Study: 2016 U.S. Presidential Election
Partisan
Source
• How do source cues interact with properties of a
message to affect diffusion?
Brady et al., (2018), JEP:G

22. Clinton vs. Trump
• Scraped twitter messages from both candidates posted in the
last 1 year leading up to the 2016 U.S. Presidential Election
• N = 9,505
Brady et al., (2018), JEP:G

23. Results: Clinton vs. Trump
Logretweetcount
# Moral-emotional words
Clinton Trump
Brady et al., (2018), JEP:G

24. Results: Clinton vs. Trump
Predictedretweetcount
# Moral-emotional words
Clinton Trump
IRR = 1.27, p <.001
Brady et al., (2018), JEP:G

25. Results: Clinton vs. Trump
Moral-emotional words associated with most viral messages
Brady et al., (2018), JEP:G

26. Why the asymmetry in moral contagion?
1. Ideology effects
Brady et al., (2018), JEP:G

27. Why the asymmetry in moral contagion?
1. Ideology effects
• Conservatism associated with moral dogmatism and
moralization of action (Jost et al., 2003)
• Conservatives more reactive to high arousal emotions
(e.g., anger) when moral views are threatened
(Tomkins, 1995; Jost et al., 2003)
• Prediction: Conservatives exhibit a relatively greater
moral contagion effect than Liberals
Brady et al., (2018), JEP:G

28. Why the asymmetry in moral contagion?
1. Other source cues?
• Gender, race and age (Brewer, 1988; Fiske & Neuberg,
1990)
Brady et al., (2018), JEP:G

29. U.S. Congress
• Scraped tweets from all U.S. Senators + House one year leading up
the 2016 U.S. Presidential Election
• Test for political ideology and other source effects
• N = 276,750
Brady et al., (2018), JEP:G

30. Results: U.S. Congress
# Moral-Emotional Words
LogRetweetCount
1 – Replicating moral contagion effect in political elites
Brady et al., (2018), JEP:G

31. Results: U.S. Congress
ConservativesLiberals
2 – Political ideology analysis
Cory Booker
Jeff Sessions
Brady et al., (2018), JEP:G

32. Results: U.S. Congress
ConservativesLiberals
2 – Political ideology analysis
Cory Booker
Jeff Sessions
Brady et al., (2018), JEP:G

33. Results: U.S. Congress
3 – Political ideology analysis: follow-up analyses
1. Gender effects
• No significant gender effects on any language category
2. Age effects
• Younger elites exhibited significantly greater moral contagion effect than
older elites, IRR = 0.96, p <.001, 95% CI = [0.95, 0.98]
3. Race effects
• Non-white elites exhibited significantly greater moral contagion effect
than white elites, IRR = 0.93, p = .019, 95% CI = [0.87, 0.99]
Brady et al., (2018), JEP:G

34. Ideology associated with the spread of
moralized content
Brady et al., (2018), JEP:G
Moral-emotional
content
Information
Diffusion
Partisan Source

35. GRATITUDE
Billy Brady Julian Wills John Jost
Research Assistants:
Miaohan Wang Jino Kwon Stephanie Leung
Dominic Burkart
https://psyarxiv.com/43n5e/