VoxPop: An Experimental Social Media Platform for Calibrated (Mis)information Discourse

Sharevski, Filipo; Jachim, Peter; Pieroni, Emma; Jachim, Nate

doi:10.1145/3498891.3498893

Cited by 6 publications

(11 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study we were motivated to bring soft moderation closer to users' everyday experiences while minimizing imposition, which as witnessed, often backfire [71]. We distinguished between a need for context when the hazard comes from the fabrication of facts and when the hazard comes from the interpretation of facts in a rather improbable way.…”

Section: Discussionmentioning

confidence: 99%

“…We opted for a watermark and not a replacement of the exclamation point inline the warning tag to avoid confusion with the red flag emoji frequently used on social media. The watermarking, centered in a ratio over the entire tweet area, follows the paradigm for misinformation flagging proposed in [71] with a midpoint transparency to create a non-negligible design friction for anyone attempting to read the tweet. By this choice, we wanted to stretch the overall text-and-flag warning throughout the suspected misinformation tweet and not only after it.…”

Section: Fabricated Facts (Spam)mentioning

confidence: 99%

“…However, adaptations could be made here too. Participants commented on the size and the transparency, so variations could include testing options where these two variables are determined by the level of confidence of fact checkers or the engagement it attracts over time, as suggested in [71]. The watermark display could vary based on a particular user's content preferences, e.g.…”

Section: User-tailored Soft Moderationmentioning

confidence: 99%

“…The design of the warnings, thus, requires adaptation of the approach to retain their usability in various misinformation scenarios while avoiding a "backfire effect" [71]. To help this effort, we developed enhancements to the misinformation warning tags used by Twitter and evaluated them with a sample of 337 regular users.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter

Sharevski¹,

Devine²,

Pieroni³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Warning users about misinformation on social media is not a simple usability task. Soft moderation has to balance between debunking falsehoods and avoiding moderation bias while preserving the social media consumption flow. Platforms thus employ minimally distinguishable warning tags with generic text under a suspected misinformation content. This approach resulted in an unfavorable outcome where the warnings "backfired" and users believed the misinformation more, not less. In response, we developed enhancements to the misinformation warnings where users are advised on the context of the information hazard and exposed to standard warning iconography. We ran an A/B evaluation with the Twitter's original warning tags in a 337 participant usability study. The majority of the participants preferred the enhancements as a nudge toward recognizing and avoiding misinformation. The enhanced warning tags were most favored by the politically left-leaning and to a lesser degree moderate participants, but they also appealed to roughly a third of the right-leaning participants. The education level was the only demographic factor shaping participants' preferences. We use our findings to propose user-tailored improvements in the soft moderation of misinformation on social media. CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; Usability in security and privacy.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Fabricated Facts (Spam)mentioning

confidence: 99%

Section: User-tailored Soft Moderationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter

Sharevski¹,

Devine²,

Pieroni³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Through a study with 235 social media users in United States, we found out that misinformation is conceptualized in several distinct ways based on how the information on social media relates to facts known to the public. Looking top-down, one would expect that most, if not all, of the participants in our study will possess what we named the inherently fallacious information folk model: misinformation is any information unfaithful to to known facts, regardless of contexts or intentions (see [61] for the discussion on the "faithfulness to known facts" qualification). However, the most prevalent folk model was the political counter(argumentation): misinformation was conceptualized as any information with faithfulness to selective facts relative to political and ideological contexts, created and disseminated with a political agenda-setting or argumentwinning intentionality.…”

Section: Introductionmentioning

confidence: 99%

Folk Models of Misinformation on Social Media

Sharevski¹,

Devine²,

Pieroni³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In this paper we investigate what folk models of misinformation exist through semi-structured interviews with a sample of 235 social media users. Work on social media misinformation does not investigate how ordinary users-the target of misinformation-deal with it; rather, the focus is mostly on the anxiety, tensions, or divisions misinformation creates. Studying the aspects of creation, diffusion and amplification also overlooks how misinformation is internalized by users on social media and thus is quick to prescribe "innoculation" strategies for the presumed lack of immunity to misinformation. How users grapple with social media content to develop "natural immunity" as a precursor to misinformation resilience remains an open question. We have identified at least five folk models that conceptualize misinformation as either: political (counter)argumentation, out-of-context narratives, inherently fallacious information, external propaganda, or simply entertainment. We use the rich conceptualizations embodied in these folk models to uncover how social media users minimize adverse reactions to misinformation encounters in their everyday lives. CCS CONCEPTS• Security and privacy → Social aspects of security and privacy; Usability in security and privacy.

show abstract

Investigating the Mechanisms by which Prevalent Online Community Behaviors Influence Responses to Misinformation: Do Perceived Norms Really Act as a Mediator?

Aghajari,

Baumer,

Lazard

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

VoxPop: An Experimental Social Media Platform for Calibrated (Mis)information Discourse

Cited by 6 publications

References 74 publications

Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter

Meaningful Context, a Red Flag, or Both? Users' Preferences for Enhanced Misinformation Warnings on Twitter

Folk Models of Misinformation on Social Media

Investigating the Mechanisms by which Prevalent Online Community Behaviors Influence Responses to Misinformation: Do Perceived Norms Really Act as a Mediator?

Contact Info

Product

Resources

About