Socialmedia 'trust' or 'distrust' buttons could reduce spreadofmisinformation uclnews elife
Senior Editor; Brown University, United StatesIn the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.
The authors compliment these insights with mechanistic explanations by modelling the 'sharing of posts' data using a drift diffusion model and comparing parameters across environments where trust/distrust vs like/dislike were differentially available. They find that across two studies drift rates are larger in the trust/distrust conditions, suggesting users accumulate more information before making a decision.
The study involves 3 different experiments. One strength of the study is that the main fundings are replicated in 3 independent experiments. In the first experiment, the authors demonstrate that participants discern true from false information when deciding to trust or distrust a new post to a higher degree than when deciding to like or dislike a post. This claim seems to be supported by the data.
1) It is unclear why the ANOVA in experiment 1 did not include an interaction between type of response and valence. The data seems to show a pattern towards this direction whereby the main behavioral effect is driven by a difference between dislike and distrust whereas the difference between like and trust seems much smaller or non-significant. The omission of the interaction might have affected the reported results.
Include the interaction between type of response and valence in study 1. Report the results with and without political orientation to show that the results are independent of the inclusion of this factor. – The authors' idea of offering buttons that are better linked to headline veracity is a practical solution that could be easily implemented.– The conceptualization could be better articulated with wider coverage of the misinformation literature.– Fake and true headlines may be different from one another on other factors that affect people's trust judgments.
3. Why is the participant the evaluator of the news headline in experiment 1 and then the sharer of the headline in experiments 2 and 3? 6. In experiments 2 and 3, the authors provide participants with 100 headlines. Participants make a skip or repost decision. Then, participants receive one of the types of feedback depending on their condition. One problem with this design and what authors suggest in Figure 1 is that participants receive one type of feedback in their respective condition. For instance, in the distrust condition, participants receive distrust feedback no matter what the veracity of the headline is.
9. Trust judgments also bear on other factors. One such factor is emotions . The reader wonders what the valence of the emotions is between the true and false headlines. A pretest that shows differences in emotions between false and true headlines would be helpful to the reader. We thank the reviewers for this positive evaluation of our study. We have repeated all the main analyses including valence and type of reaction/feedback interactions, as well as not including political orientation as a factor. The results show once again that discernment is greater in the ‘Trust’ condition than the other conditions .
We now follow the reviewer’s recommendation and include the starting point as a free parameter . The findings remain the same. In particular we find that the drift rate in the ‘Trust’ environments was meaningfully higher than the drift rate in both the ‘Like’ and Baseline environments . The authors note that the complexities of real world social media environments may work differently to the tightly controlled laboratory analogues described here and this would be an important next step. Other papers have investigated methods to promote the sharing of true information online, but they are typically active training conditions that would be difficult to roll out and achieve engagement.
We thank the reviewer for prompting us to provide the exact instructions. These are now available in full in Figure 2 – —figure supplement 1, Figure 4 – —figure supplement 1 and 2. 1) It is unclear why the ANOVA in experiment 1 did not include an interaction between type of response and valence. The data seems to show a pattern towards this direction whereby the main behavioral effect is driven by a difference between dislike and distrust whereas the difference between like and trust seems much smaller or non-significant. The omission of the interaction might have affected the reported results.
We now also clarify our motivation for including political orientation in the main text. In particular, we included political orientation in our analysis because previous studies suggest a political asymmetry in both the sharing of misinformation online as well as in the subsequent efficacy of interventions aimed at reducing misinformation . For an intervention to be effective it is helpful that it would work across both sides of the political spectrum.
We thank the reviewer for pointing this out. We now include a definition of discernment in the abstract . Following the reviewer’s suggestion, we have repeated all the analyses without political orientation and include an interaction term for valence x type where valence is a factor. Once again, we find that discernment is greater in the ‘Trust’ conditions than the other conditions .
As recommended by the reviewer we now changed the labels to “veracity promoting” and “veracity obstructing” . – Fake and true headlines may be different from one another on other factors that affect people's trust judgments. Yes, changing the incentive structure could lead users to develop new sharing habits. We now note this possibility in the discussion .
Following the reviewer’s comment, it became apparent that the design and rationale behind Experiment 2 was unclear. The feedback participants receive is not disconnected from the actual veracity of the information. Let’s take the ‘distrust’ condition as an example. For any given post, true or false, some users will distrust the post. However, true posts receive fewer ‘distrusts’ than false posts. It is the number of ‘distrusts’ per post that matters.
a. The same for Supplemental Appendix Tables 5 and 7. Authors can test their theory by including an interaction term for type of reaction and valence of that reaction. a. This may explain why the authors do not find a differential effect between trust and distrust conditions. Trust feedback that is disconnected from the actual veracity of the headline would not enhance people's discernment differentially when this feedback is negative or positive. It seems like participants were just operating in an environment knowing that the feedback will be about trust but no matter what they share the valence of the trust would not change.
8. One concern is that the authors stripped the headlines from any factor that could contribute people's truth and trust judgments. For instance, source credibility or consensus are factors that aid people's judgments on social platforms such as Facebook and Twitter.
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