Anger and Fear Predict Individual Misinformation Sharing but Not Collective Patterns on Twitter

Jula Luehring, David Garcia, Josephine Lukito, Apeksha Shetty, and Annie Waldherr

The temporal nature of emotions and misinformation

Emotions¹ and misinformation² are widely assumed to fuel each other
- Instances of heightened emotions are linked to misinformation acceptance (in experiments: Martel et al., 2020; Greenstein & Franklin, 2020) and conspiracy belief (longitudinal surveys: Albath et al., 2024)
- In turn, misinformation is followed by emotional experiences when it contradicts or confirms beliefs (Ecker et al., 2022; Lühring et al., 2023)

\(\rightarrow\) The relationship exists at the individual level

\(\rightarrow\) The relationship seems bi-directional, hence, it may be important to examine the temporal dynamics

Understanding misinformation spread as operating at multiple levels

At the collective level, misinformation represents only a fraction of news (see Budak et al., 2024 for a discussion)
But individual-level dynamics can ripple through the broader system generating macro-level consequences, including affective polarization (Jenke, 2024) and loss of trust in institutions (Ognyanova et al., 2020)

\(\rightarrow\) Aggregate patterns may mask or contradict individual-level mechanisms (ecological fallacy³)

This relationship between misinformation and emotions operates across multiple levels of analysis, which has consequences for how we study it.

At the collective level, misinformation represents only a small fraction of overall news consumption on platforms.

Yet individual-level dynamics can aggregate and ripple through the broader system, generating macro-level consequences such as hateful emotional dynamics on social media and erosion of trust in institutions.

The methodological implication is that aggregate patterns may mask, or in some cases contradict, the individual-level mechanisms that produce them, which is called the ecological fallacy: a relationship observed at the population level need not hold or reverse for any individual within it.

Studying emotions and misinformation therefore requires examining both levels simultaneously, rather than treating findings from one as evidence about the other.

[NEXT SLIDE]

Looking at different levels of analysis

[Collective level] Synchronized spikes around events but is that mutual reinforcement or just a shared response to the same shock?

[Individual level] Trait vs. state: does emotional baseline matter, or the moment right before sharing?

Research questions

RQ1 [Collective]: Do misinformation and emotional expression reinforce each other at the aggregate level, or do they respond independently to external events?

RQ2 [Individual]: Do static emotional traits, dynamics (baseline, variability, instability, inertia), or momentary states predict whether a user engages with untrustworthy news?

Data and methods

Data

~29,000 Austrian Twitter users, located via Brandwatch⁴
Jan 2019 → Mar 2023 covers pre-pandemic, pandemic, and post-pandemic
~138 million tweets after filtering (spam, media accounts, inactive users)
Collected via academic API v2 just before the X transition

Measurement

Variable	How we measure it
News sharing	URL → NewsGuard domain match
Untrustworthy news	NewsGuard score < 60
Partisan news	NewsGuard left/right label
Anger / fear	`pol_emo_emDeBERTa` (Widmann & Wich, 2022) — German tweets only Figure 1. Area Under the Curve for emotion expressions (1,000 tweets).

Four key variables, all operationalized at the tweet level.

News sharing is identified by matching expanded URLs against the NewsGuard database, which rates roughly 11,000 news domains on a 0–100 trustworthiness scale.

Untrustworthy news is operationalized as content from sources scoring below 60, the threshold NewsGuard itself uses to flag domains as failing basic journalistic criteria.

Partisan news is identified through NewsGuard’s left and right bias labels.

Emotional expression in tweets is classified using pol_emo_emDeBERTa, a transformer model fine-tuned by Widmann and Wich on German-language political text.

We focus on anger and fear because both are high-arousal, negative-valence emotions, and because both feature prominently in the experimental and survey literature linking emotions to misinformation acceptance.

The ROC curves shown here indicate that classifier performance for both emotions compares well against our manual annotations.

[NEXT]

Two levels, two analytic strategies

Collective (RQ1)

Daily proportions (1,551 days)
ARIMA + structural-break detection
VAR(9) with misinformation, anger, fear as endogenous
News sharing & two structural breaks as exogenous controls
Impulse-response + variance decomposition

Individual (RQ2)

Per-user emotional features (Houben et al., 2015):
- baseline, variability, instability, inertia
Adjusted for irregular posting (weekly windows ≥ 10 tweets; hourly decay for inertia)
Logistic regressions predicting any / partisan / untrustworthy news sharing

At the collective level, we work with daily proportions over 1,551 days.

ARIMA models identify structural breaks, which we control for as dummies in a vector autoregression with nine lags.

Misinformation, anger, and fear are endogenous; overall news sharing and the structural breaks are exogenous. Impulse-response and variance decomposition then quantify cross-variable dynamics.

At the individual level, we extract three emotional features per user following Houben and colleagues to go beyond just a baseline and look into how emotions fluctuate: variability, instability, and inertia.

Because users post irregularly, we estimate these within weekly windows and use exponential decay regression for the inertia parameter.

The features then enter logistic regressions predicting whether a user ever shared any news, partisan news, or untrustworthy news.

Results at the collective level

Tweet activity tracks major events

Figure 2. Daily tweet volume and trustworthiness of shared news (here per week for cleaner visualization).

VAR(9): emotions and misinformation move independently

Figure 3. Impulse-response functions based on 10% shock.

Cross-variable effects all < 5% at every forecast horizon
Fear → anger is the only non-trivial cross-effect (~37% at 14 days)
But misinformation ↔︎ emotions shows essentially zero cross-variable effects

Results at the individual level

And only a motivated minority engages with untrustworthy news

Sharing	Users	Share
Any news	24,367	83.5%
Partisan news	10,589	36.3%
Untrustworthy news	4,104	14.1%

Figure 5. Cumulative distribution function of users sharing trustworthy and untrustworthy news. Dashed horizontal line marks 80% cut-off.

Figure 6. Density distributions of anger dynamics for users sharing trustworthy (10,663 tweets) vs. untrustworthy (146 tweets) news sources. Dashed vertical lines = group means.

Conclusion

Selection, not contagion

Aggregate level

Events drive both emotions and misinformation, but they don’t mutually reinforce each other.

\(\rightarrow\) Misinformation spreads through exposure opportunities.

Individual level

Stable emotional traits, and especially volatile anger, predict who engages.

\(\rightarrow\) Misinformation is shared through selection of vulnerable users.

Emotional traits predict who shares. External events predict when it spreads.

Platform-level: rapid response and pre-bunking during predictable crises (the when)
User-level: target users with high-anger, high-volatility profiles (the who)

Putting both levels of analysis together, a consistent picture emerges.

At the aggregate level, both emotions and misinformation respond to external events, but they do not amplify or reinforce each other. The collective spread of misinformation is best understood as driven by these exposure events: when major events occur, more users encounter untrustworthy sources, and a subset of them share.

At the individual level, the picture is different. Stable emotional traits and in particular volatile, high-arousal anger predict which users are the ones that engage.

So misinformation spreads not through a general contagion at the population level, but primarily through selection at the individual level.

[CLICK]

So taken together, emotional traits predict who shares misinformation, and external events predict when it spreads.

This translates into interventions focusing on both rapid response and pre-bunking during predictable crises and targeting users with high-risk profiles.

[NEXT]

Limitations

Proportions, not raw counts, so compositional effects possible
We measure emotional expression, not felt experience
URL-based misinformation misses text/meme/screenshot false claims
Austrian Twitter does not generalize to other platforms/contexts

Thank you!

For questions, email jula.luehring@gesis.org :-)

Pre-registration: osf.io/xejdw

Backup slides

Backup: structural breaks in untrustworthy news

Two unexplained step-changes (July 1 and Aug 28, 2022) in the untrustworthy proportion. Cumulative shift: +0.11 percentage points. Modelled as dummies in the VAR.

Figure 6. Two structural breaks in the trustworthiness time series on July 1, and August 28, 2022.

Backup: emotional dynamics from irregular data

The Houben et al. (2015) metrics assume equally-spaced measurements but tweets aren’t.

Variability / instability: weekly windows with ≥ 10 tweets, then averaged across weeks
Inertia: exponential decay regression → hourly autocorrelation \(\phi_{hour}\)

Backup: why VAR(9)?

Lags	AIC	BIC	Portmanteau
7	5191.0	5591.7	<.001
8	5182.2	5630.9	<.001
9	5151.1	5647.8	.002

Chose 9: lowest AIC, best residual properties, stability max-eigenvalue 0.972.

Backup: variance decomposition

At 14-day horizon:

Misinformation: 95.3% self-explained
Fear: 97.2% self-explained
Anger: ~63% self-explained (37% from fear shocks)
Misinformation ↔︎ emotions cross-effects: all < 5%

References

Albath, E. A., Greifeneder, R., Douglas, K. M., Cichocka, A., Marques, M. D., Wilson, M. S., Kerr, J. R., Sibley, C. G., & Osborne, D. (2024). Does Lower Psychological Need Satisfaction Foster Conspiracy Belief? Longitudinal Effects Over 3 Years in New Zealand. Personality and Social Psychology Bulletin, 01461672241292841. https://doi.org/10.1177/01461672241292841

Budak, C., Nyhan, B., Rothschild, D. M., Thorson, E., & Watts, D. J. (2024). Misunderstanding the harms of online misinformation. Nature, 630(8015), 45–53. https://doi.org/10.1038/s41586-024-07417-w

Ecker, U. K. H., Lewandowsky, S., Cook, J., Schmid, P., Fazio, L. K., Brashier, N., Kendeou, P., Vraga, E. K., & Amazeen, M. A. (2022). The psychological drivers of misinformation belief and its resistance to correction. Nature Reviews Psychology, 1(1), 13–29. https://doi.org/10.1038/s44159-021-00006-y

Frischlich, L., Olsson, H., Roy, A., Schulze, H., Rhodes, S., & Mansheim, A. (2025). The complexity of misinformation extends beyond virus and warfare analogies. Npj Complexity, 2(1), 29. https://doi.org/10.1038/s44260-025-00053-z

Greenstein, M., & Franklin, N. (2020). Anger Increases Susceptibility to Misinformation. Experimental Psychology, 67(3), 202–209. https://doi.org/10.1027/1618-3169/a000489

Houben, M., Van Den Noortgate, W., & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological well-being: A meta-analysis. Psychological Bulletin, 141(4), 901–930. https://doi.org/10.1037/a0038822

Jenke, L. (2024). Affective Polarization and Misinformation Belief. Political Behavior, 46(2), 825–884. https://doi.org/10.1007/s11109-022-09851-w

Lindquist, K. A., & Barrett, L. F. (2012). A functional architecture of the human brain: Emerging insights from the science of emotion. Trends in Cognitive Sciences, 16(11), 533–540. https://doi.org/10.1016/j.tics.2012.09.005

Lühring, J., Shetty, A., Koschmieder, C., Garcia, D., Waldherr, A., & Metzler, H. (2023). Emotions in misinformation studies: Distinguishing affective state from emotional response and misinformation recognition from acceptance. OSF. https://doi.org/10.31234/osf.io/udqms

Martel, C., Pennycook, G., & Rand, D. G. (2020). Reliance on emotion promotes belief in fake news. Cognitive Research: Principles and Implications, 5. https://doi.org/10.1186/s41235-020-00252-3

Ognyanova, K., Lazer, D., Robertson, R. E., & Wilson, C. (2020). Misinformation in action: Fake news exposure is linked to lower trust in media, higher trust in government when your side is in power. Harvard Kennedy School Misinformation Review. https://doi.org/10.37016/mr-2020-024

Russell, J. A. (1980). A circumplex model of affect. Journal of Personality and Social Psychology, 39(6), 1161–1178. https://doi.org/10.1037/h0077714

Wardle, C., & Derakhshan, H. (2017). Information Disorder: Toward an Interdisciplinary Framework for Research and Policymaking. Council of Europe Report. https://doi.org/http://tverezo.info/wp-content/uploads/2017/11/PREMS-162317-GBR-2018-Report-desinformation-A4-BAT.pdf

Widmann, T., & Wich, M. (2022). Creating and Comparing Dictionary, Word Embedding, and Transformer-Based Models to Measure Discrete Emotions in German Political Text. Political Analysis, 1–16. https://doi.org/10.1017/pan.2022.15