Truth be told: Cognitive moderators of selective sharing of fact-checks on social media

# Truth be told: Cognitive moderators of selective sharing of fact-checks on social media
### Tiago Ventura
### University of Maryland, College Park
### Necon - 10/28/2021

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## .red[About me]

### Tiago Ventura

### Researcher Civic Integrity at Twitter

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# Plans for Talk

- **An overview of our research agenda and methods at the iLCSS**

- **Truth be told: Cognitive moderators of selective sharing of fact-checks on social media**

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### iLCSS: Social Science meets Data

**Truth be Told**  is one of the many projects I have been working as a member of the [Interdisciplinary Lab for Computational Social Science](https://ilcss.umd.edu/) at UMD

Before we start, I want to say a few words about how this project fits in our broader mission at iLCSS.

At iLCSS we replicate a logic quite strong on hard-science labs: most of our work is collaborative and involves several co-authors. So I want to thanks my co-authors on this paper:

- Natalia Aruguete (UnQ), Ernesto Calvo (UMD), Ingrid Bachman (Puc-Chile), Sebastian Valenzuela (Puc-Chile)

- And *Chequeado*

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<img src="figs/network.png" width="90%" />
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<img src="figs/manuel.png" width="60%" />
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Under this umbrella, I have worked on a few different projects focusing on substantive issues in the midst of communications, political behavior, and computational social science:

- News Sharing on Social-Media: Papers [Here](https://www.tandfonline.com/doi/abs/10.1080/21670811.2020.1852094) and [Here](https://tiagoventura.rbind.io/files/News_by_popular_demand_II.pdf)

- Framing, Activation and Social Media Echo-Chambers: Papers [Here]() and [Here](https://tiagoventura.rbind.io/files/Event_Adjudication_in_Social_Media.pdf)

- Behavioral Effects of Social Media: [Trust](https://www.econstor.eu/handle/10419/237464) and [Risk Related to Covid-19](https://www.cambridge.org/core/journals/latin-american-politics-and-society/article/abs/will-i-get-covid19-partisanship-social-media-frames-and-perceptions-of-health-risk-in-brazil/496B0EE199D8079AC6B9467BCF0EB08C)

- Strategies to work with Big Social Media Network Data: [The Path-Weighted Regression Model](https://methods.sagepub.com/book/research-methods-in-political-science-and-international-relations/i4823.xml)

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## Truth be told: Cognitive moderators of selective sharing of fact-checks on social media

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## A Glimpse of the Literature

Previous work have discussed several reasons why people share misinformation:

- Partisan-Sharing and Negative Partisanship (Guess, Nagler, & Tucker 2019; Osmundsen et. al. 2021)

- Lack of Cognitive Sophistication (Pennycock and Rand 2019)

- Digital Literacy (Guess, Nagler & Tucker 2019)

- Polarization, Highly Partisan Media Outlets (Faris et al 2017; Eldridge 2017)

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### Fact-Checking

Fact-checkers have become ubiquitous to most media companies.

Several recent scholarly work have shown effects of fact-checking corrections on attitudes:

- Fact-checking overall reduces misperceptions (Walter et al 2020, Wood and Porter, 2021, Nyhan et. al. 2020).

- Little Evidence of back-fire effect (Wood and Porter, 2019).

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While we have enough evidence for the effects of corrections on attitudes,  fewer studies address the motivations for sharing corrections to political misinformation and what make users more willing to propagate corrections.

- Users prefer to share pro-attitudinal corrections (Ekstrom & Lai, 2020, Lewendosky et al., 2012)

- Partisans consult congenial sources to adjudicate between true/false (Peterson and Iyengar, 2020)

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### When do users share fact-checks with their peers?

#### .red[What are the main cognitive moderators of the decision to share corrections?]

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#### .red[Hypothesis]

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- *H1: Attitude-consistent fact-checks are more likely to be shared than counter-attitudinal fact-checks.*

- *H2: Attitude-consistent fact-checks rated ‘true’ are more likely to be shared than attitude-consistent fact-checks rated ‘false.’*
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## Research Design

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### Research Design: Experiment + Social Media Data

Our paper combines two distinct empirical approaches:

- .red[On-line Experiment] ~> Two Stage, Two Arm Experiment, where respondents are assigned to True/False Fact-Checking Corrections.

- .red[Observational data] ~> Social media from Twitter + a regression discontinuity design.

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### Experimental Design: General Flow

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### Experimental Design: With Images

And we do the same with a tweet congruent to Macri voters (Ofelia Case)

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.pull-right[

<img src="figs/false.png" width="50%" />
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### Experimental Design and Hypotheses

.center[**Would you share?**]
<table class="table table-hover" style="font-size: 14px; margin-left: auto; margin-right: auto;">
 <thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1"></th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3"><div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">Audifono</div></th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3"><div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">Ofelia</div></th>
</tr>
  <tr>
   <th style="text-align:left;"> Vote Choice </th>
   <th style="text-align:left;"> Tweet </th>
   <th style="text-align:left;"> True </th>
   <th style="text-align:left;"> False </th>
   <th style="text-align:left;"> Tweet_2 </th>
   <th style="text-align:left;"> True_Tw2 </th>
   <th style="text-align:left;"> False_Tw2 </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;width: 1.5in; font-weight: bold;"> Fernández </td>
   <td style="text-align:left;"> Pro-attitudinal message
H1 (+) </td>
   <td style="text-align:left;"> <span style=" font-weight: bold;    color: darkgreen !important;">Pro-attitudinal confirmation<br>H2 (++)</span> </td>
   <td style="text-align:left;"> Counter-attitudinal refutation </td>
   <td style="text-align:left;"> Counter-attitudinal message </td>
   <td style="text-align:left;"> Counter-attitudinal confirmation </td>
   <td style="text-align:left;"> <span style=" font-weight: bold;    color: #C93312 !important;">Pro-attitudinal refutation<br>H2 (+)</span> </td>
  </tr>
  <tr>
   <td style="text-align:left;width: 1.5in; font-weight: bold;"> Macri </td>
   <td style="text-align:left;"> Counter-attitudinal message </td>
   <td style="text-align:left;"> Counter-attitudinal confirmation </td>
   <td style="text-align:left;"> <span style=" font-weight: bold;    color: #C93312 !important;">Pro-attitudinal refutation H2 (+)</span> </td>
   <td style="text-align:left;"> Pro-attitudinal message
H1 (+) </td>
   <td style="text-align:left;"> <span style=" font-weight: bold;    color: darkgreen !important;">Pro-attitudinal confirmation<br>H2 (++)</span> </td>
   <td style="text-align:left;"> Counter-attitudinal refutation </td>
  </tr>
</tbody>
</table>

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## Results

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### H1: Pro-Attitudinal Effects

.panelset.sideways[

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### H2: Pro-Attitudinal Confirmation

.panelset.sideways[

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### Observational Social Media Data

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### Modeling the activation of Fact-Checkers

<br><br>
Our experiment provided evidence of selective sharing of .red[‘true’] and .red[‘false’] fact-checks.

To assess the robustness of experimental findings, we introduce supporting evidence of **partisan selective sharing** as stated in H1 using real-world social media data.

We focus on the **false rumor** that Macri was using a ear device (*audifono*) during the debate

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### Research Design

Using the Twitter APIs we collected close to .red[3 million] Tweets during the two weeks around the debate.

- Filtered only **retweets**

- Searched for the *audifono* misinformation (simple regex search)

- Employed **Community Detection algorithms** to identify Pro-Macri and Anti-Macri

Using the exact time of the Chequeado Correction (False), we estimated the causal effects (.red[Regression Discontinuity Design]) of the intervention on users' reaction (time-to-retweet)

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### Results: Time-to-Retweet after the Correction

.panelset.sideways[

]
]

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### Conclusion

.panelset.sideways[

- Users exhibit .red[partisan reasoning] when sharing corrections.

- More important,  a pro-attitudinal fact-check labeled .red[true] is more likely to be shared on social media than an equally congenial fact-check labeled .red[false]

- This result suggests that sharing fact-checking messages is regulated by the .red[hot cognition] and confirmatory bias (directional goals driven by automatic responses)

]

#### .red[Fact-checkers should consider presenting their work with a ‘true’ adjudication more often]

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## Thank you!