Misinformation and Emotions Online

SDS Colloquium 2025

Jula Lühring

University of Vienna & Complexity Science Hub

Team work <3

On emotions and identity

• People who believe in misinformation are often seen as irrational and stupid

What do we know?

1. Arousing emotions¹ reinforce partisan-based information processing

Russel, 1980

2. Misinformation² thrives in partisan information environments

Mosleh & Rand, 2022

Do emotions make people fall for misinformation?

So when we talk about misinformation, we usually refer to inaccurate information, regardless of the intention.

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?

But we also looked at the emotional response as a more immediate measure of emotions and we found more anger and less joy in response to the false items

However, we also included some open-ended items and we found that people reacted with disbelief, they said things like bullshit or nonse

So here in this plot you can see a non-linear relationship between discernment and anger, and we can see that people who were really good at discerning true from false and the ones who were really bad at it got angry

Therefore, the function of emotions depends on prior beliefs and it mediates the effect of misinformation

This is in line with research showing that one of the strongest predictors is partisanship

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

• On social media, arousing emotions attract attention and higher engagement, reflecting a function of emotions, namely grabbing attention

• Content that is highly arousing and negative gets higher engagement, typically, this is moralizing and conflictual content

• Therefore, misinformation is now understood as a symptom of a clustered information ecosystem where few people are exposed to misinformation, while most people have partisan information diets, including decontextualized but not completely inaccurate information

• So we would also expect misinformation to be embedded in such emotional dynamics, anger, and intergroup hate

• Where a major concern is not just how many people believe in it but secondary effects, such as loss in trust, growing affective polarization, and so on.

What are effects of misinformation on online interactions?

• Therefore, we were wondering what the effects of misinformation are on online discussions, do they trigger conflict and anger, and does this then lead to higher engagement?

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. 

2. Different effects of emotions are overlooked by

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

We identified two problems that we wanted to tackle in this study

• First, misinformation is often measured as clearly false, fact-checked instances, which ignores less extreme and more influential types of misinformation, for instance biased news, and it makes it hard to isolate the effects from real news

  • So the question is, are the effects that we are observing unique to misinformation?

• Second, emotions are functional; and the function of emotions hinges on the interaction of prior beliefs and content. Therefore, measuring only positive and negative sentiment, or mixing up emotional state with emotional reactions overlooks the contextual effects of different emotions.

Our objectives

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

Continuous trustworthiness ratings by NewsGuard (#1)

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

Non-parametric matching strategy (#2)

So, we derived 2 major objectives:

• First, we wanted to collect a systematic, large-scale and long-term dataset that relies on continuous trustworthiness ratings for sources, including biased but relatively trustworthy sources so that it reflects the whole spectrum of news trustworthiness

• Second, we tried a matching approach to approximate causal inference so that we could isolate the effects of untrustworthy sources

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)

• For each tweet, we then classified 8 distinct emotions from text
• This plot shows the covered time frame from October 2020 to March 2022 and we can already see that anger is overall much higher

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

• most users replying to untrustworthy news posts also reply to trustworthy news but not vice versa. While 9.6% of users wrote 80% of the replies to trustworthy news, only 2.1% wrote 80% of the replies to untrustworthy news.

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