This document summarizes a pilot study on using news recommender systems to reduce polarization on climate change. The study involved 180 US participants and analyzed how their attitudes correlated with liking or engaging with different news articles. The results showed that participants' concern for the environment correlated with liking news articles. However, a news article's sentiment was not correlated with other factors like environmental concern or liking. The document discusses using algorithmic and interface methods to expose users to more diverse opinions and examining their long-term effects on attitudes. The goal is to evaluate if news recommender systems can shift views on less ambiguous topics than climate change.

1. Research Centre for Responsible
Media Technology and Innovation
Project number 309339
Towards Attitudinal Change in News Recommender Systems:
A Pilot Study on Climate Change
Alain Starke
University of Amsterdam (NL) & University of Bergen (NOR)

2. Work with
• Jia-hua Jeng  his first PhD paper!
• Christoph Trattner

3. Aim of Jeng’s project: Reducing Polarization
in Personalized Environments
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4. Using algorithmic and interface
persuasion to steer user attitudes
• Particularly important to convince ‘strong believers’
• Supporting exposure to and interaction with opinion-challenging
content
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5. News Recommender Systems
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6. Features used in recommendation
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7. Sentiment Analysis:
Detecting one’s opinion towards a topic
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8. - Filter bubbles
- Not as big of an issue as thought of previously, but echo chambers may still arise
- Selective exposure
- Traditional news recommender systems are taste-based
- No optimization on normative diversity, demographic values, etc.
8
Problems
Polarization

9. Can we change
climate attitudes?

11. Procedure
N = 180,
US-based

12. Methods
Dataset
• Climate Change News
Measurements
• NEP scale for Environmental Concern
• User Preference Propositions to evaluate each news article
• Reading it
• Trust it
• Agreeing with it
• Recommending it to others

13. Results
13

14. Result 1:
Trust, Agreeing with, and
Recommending an article to
others collapsed into a latent
aspect labelled ‘Like’
14
Correlational Analysis

15. Result 1:
Results of the
exploratory factor
analysis
15
Correlational Analysis

16. Result 2:
Significant correlation
between ‘Liking’ and
environmental concern:
r = 0.42, p < 0.001.
16
Correlational Analysis

17. Result 3:
A news article’s sentiment was not
correlated to other characteristics.
Not with environmental concern,
nor with Liking (both: p > 0.05).
Moreover, the correlation between
title and body text was only found to
be weak (r = 0.3, p < 0.001).
17
Correlational Analysis

18. 18
Regression Analyses
Model 1: What determines Liking a news article?
Model 2: What determined self-reported Reading?

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20. What’s next?
• Examining both algorithmic and interface persuasion to
engage with more diverse news.
• Examining short-term and longer-term effects on user
attitudes
• Taking a ‘simpler’ polarized topic
• Climate change is too ambiguous for simple sentiment analysis
• Stance detection methods based on AI are still in development
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21. Thank you
for your attention
Contact information:
Research Centre for Responsible
Media Technology and Innovation
Project number 309339
Jia-Hua.Jeng@uib.no
alain.starke@uva.nl
christoph.trattner@uib.no