Misinformation and Emotions Online

SDS Colloquium 2025


Jula Lühring


University of Vienna & Complexity Science Hub

Team work <3



On emotions and identity

What do we know?

  1. Arousing emotions1 reinforce partisan-based information processing

Russel, 1980
  1. Misinformation2 thrives in partisan information environments

Mosleh & Rand, 2022

Do emotions make people fall for misinformation?

Emotional state

  • Replication of Martel et al., 2020

  • False/accurate COVID-19 news headlines


\(\rightarrow\) No effects of emotional state on misinformation acceptance

Emotional response*

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?

Misinformation on social media

Collective dynamics

  • 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?

Nikolov et al., 2021

What are effects of misinformation on online interactions?

There are problems

  1. Misinformation is measured as clearly true or false instances,

    -   misrepresenting the broader spectrum of news, 
    
    -   reflecting distinct properties of extreme types. 
  1. Different effects of emotions are overlooked by

    -   mixing up timings of emotions,
    
    -   ignoring the function of emotions,
    
    -   measuring positive and negative sentiment only.  

Our objectives

  1. Collecting a systematic and large-scale data set for the German-speaking context

Continuous trustworthiness ratings by NewsGuard (#1)

  1. Approximating causal inference to test the effects of misinformation on different emotions

Non-parametric matching strategy (#2)

Data collection steps


Big question: How do we measure misinformation?

\(\rightarrow\) 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

Emotion detection in tweets

pol_emo_mDeBERTa2


\(\rightarrow\) probability of each tweet containing each emotion (0-1)

Non-parametric matching

Matching evaluation

Matching with Nearest Neighbor and Mahalanobis distance

Results

Descriptives

\(\rightarrow\) robust 6% of untrustworthy news

\(\rightarrow\) matching mitigates carryover effects

Effects on engagement

Zero-inflated negative binomial models (bootstrapped CIs)

\(\uparrow\) more retweets for tweets with untrustworthy links

\(\downarrow\) but fewer likes & replies

Effects on emotions

OLS regression models (bootstrapped CIs)

\(\uparrow\) more anger, disgust and fear

\(\downarrow\) and less joy in response to untrustworthy sources

Okay, so emotions and misinformation?

  • 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

Now: Analyzing a Twitter panel of Austrian users

Data set: 2019-2023 timelines of 29k Austrian Twitter users (~140M tweets) \(\rightarrow\) pre-registered!

Note. Random subset of 1,000 users

Emotional dynamics

RQ1: What are characteristics of users?

RQ2: What is the temporal development of trustworthiness and emotions?

Note. Random subset of 1,000 users

Thank you!

Email: jula.luehring@univie.ac.at

Bluesky: @julaluehring.bsky.social

Github: github.com/julaluehring