Jula Lühring
University of Vienna & Complexity Science Hub
Replication of Martel et al., 2020
False/accurate COVID-19 news headlines
\(\rightarrow\) No effects of emotional state on misinformation acceptance
Luehring*, Shetty*, et al., 2024
But more anger and less joy in responses to false news
🗯 “Fake”, “Bullshit”, “Nonsense”
\(\rightarrow\) Function of emotion depends on pre-existing beliefs (partisan-based processing)
\(\rightarrow\) *Emotional response = the better measure?
Moralizing & arousing content grabs attention
Misinformation: conflict, negative, moral outrage
- only 0.3-6% in 5 studies from 2016-2021,
- spread by elite & partisan superspreaders,
- creating a self-reinforcing feedback loop with algorithmic systems.
\(\rightarrow\) Misinformation plays into emotional inter-group dynamics
\(\rightarrow\) What are secondary effects?
Misinformation is measured as clearly true or false instances,
- misrepresenting the broader spectrum of news,
- reflecting distinct properties of extreme types.
Different effects of emotions are overlooked by
- mixing up timings of emotions,
- ignoring the function of emotions,
- measuring positive and negative sentiment only.
\(\rightarrow\) NewsGuard is the most comprehensive list of source ratings
But: it’s proprietary, i.e., not reproducible, and we can’t validate it
\(\rightarrow\) ratings and coverage are stable and complete for DE
Luehring, Metzler et al., 2025
\(\rightarrow\) probability of each tweet containing each emotion (0-1)
Widmann & Wich, 2022
Matching with Nearest Neighbor and Mahalanobis distance
Ho et al., 2007
\(\rightarrow\) robust 6% of untrustworthy news
\(\rightarrow\) matching mitigates carryover effects
Zero-inflated negative binomial models (bootstrapped CIs)
\(\uparrow\) more retweets for tweets with untrustworthy links
\(\downarrow\) but fewer likes & replies
Zeileis et al., 2008
OLS regression models (bootstrapped CIs)
\(\uparrow\) more anger, disgust and fear
\(\downarrow\) and less joy in response to untrustworthy sources
Sources with lower trustworthiness get unique engagement
- but reach is overall limited
Sources with low trustworthiness predict anger & other negative emotions
- but emotions are functional
- and largely reflect user preferences for such content
Data set: 2019-2023 timelines of 29k Austrian Twitter users (~140M tweets) \(\rightarrow\) pre-registered!
Note. Random subset of 1,000 users
RQ1: What are characteristics of users?
RQ2: What is the temporal development of trustworthiness and emotions?
Note. Random subset of 1,000 users
Email: jula.luehring@univie.ac.at
Bluesky: @julaluehring.bsky.social
Github: github.com/julaluehring