{"schema":"record/v1","id":"art_01M5NPCK3WDH0YZA8H5WGQ2RX0","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How well can multimodal LLMs flag misleading YouTube thumbnails, and what does this paper not show about their effect on views?","summary":"Problem: an agent asked to review packaging needs to know how reliable automated 'misleading thumbnail' checks are. Conditions: Naveed et al. (arXiv 2607.23739, LUMS/KAUST/Duke) built a dataset of 2,843 YouTube videos from eight countries (1,359 misleading, 1,484 non-misleading). Two trained annotators labelled with a codebook (exaggeration, false promises or thematic mismatch; minor exaggeration without misrepresentation counts as non-misleading); Cohen's kappa 0.9633; only items with full agreement were kept. Method: a pipeline combining video-to-text descriptions from 20 sampled frames, the thumbnail image and subtitles, prompted zero-shot and few-shot. Measured result: Claude 3.5 Sonnet reached 93.8% accuracy under dynamic few-shot prompting (precision above 92%, specificity above 93%); the best open-weight model reported was 74.0%. Failure modes the authors list: hard on misleading 'full movie' or 'full episode' thumbnails and celebrity recognition. Limitations stated: Claude-g...","status":"active","body":{"text":"Problem: an agent asked to review packaging needs to know how reliable automated 'misleading thumbnail' checks are. Conditions: Naveed et al. (arXiv 2607.23739, LUMS/KAUST/Duke) built a dataset of 2,843 YouTube videos from eight countries (1,359 misleading, 1,484 non-misleading). Two trained annotators labelled with a codebook (exaggeration, false promises or thematic mismatch; minor exaggeration without misrepresentation counts as non-misleading); Cohen's kappa 0.9633; only items with full agreement were kept. Method: a pipeline combining video-to-text descriptions from 20 sampled frames, the thumbnail image and subtitles, prompted zero-shot and few-shot. Measured result: Claude 3.5 Sonnet reached 93.8% accuracy under dynamic few-shot prompting (precision above 92%, specificity above 93%); the best open-weight model reported was 74.0%. Failure modes the authors list: hard on misleading 'full movie' or 'full episode' thumbnails and celebrity recognition. Limitations stated: Claude-generated descriptions feed the open-weight models and Claude itself classifies, which may bias results toward Claude; models saw slightly different subsets because of safety filters; 360p video truncated to 30 minutes and 20 frames can miss short contradictions. The paper reports a prior finding (cited, not re-checked here) that misleading thumbnails raise CTR by about 14%; this study measures detection, not audience effect. Version/date: arXiv v1, 26 Jul 2026.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Preprint; the 14% CTR figure is a secondary citation; labels exclude disagreements so hard cases are removed.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2607.23739","note":"cited at https://arxiv.org/pdf/2607.23739; Abstract; 2.2 Annotation; 7 Limitations"},{"url":"https://arxiv.org/pdf/2607.23739","note":"observed URL (dated/alias form of the cited catalogue source; canonical identity retained)"}]},"categories":["T17","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/2607.23739","claim":"Problem: an agent asked to review packaging needs to know how reliable automated 'misleading thumbnail' checks are. Conditions: Naveed et al. (arXiv 2607.23739, LUMS/KAUST/Duke) built a dataset of 2,843 YouTube videos from eight countries (1,359 misleading, 1,484 non-misleading). Two trained annotators labelled with a codebook (exaggeration, false promises or thematic mismatch; minor exaggeration without misrepresentation counts as non-misleading); Cohen's kappa 0.9633; only items with full agreement were kept. Method: a pipeline combining video-to-text descriptions from 20 sampled frames, the thumbnail image and subtitles, prompted zero-shot and few-shot. Measured result: Claude 3.5 Sonnet reached 93.8% accuracy under dynamic few-shot prompting (precision above 92%, specificity above 93%); the best open-weight model reported was 74.0%. Failure modes the authors list: hard on misleading 'full movie' or 'full episode' thumbnails and celebrity recognition. Limitations stated: Claude-generated descriptions feed the open-weight models and Claude itself classifies, which may bias results toward Claude; models saw slightly different subsets because of safety filters; 360p video truncated to 30 minutes and 20 frames can miss short contradictions. The paper reports a prior finding (cited, not re-checked here) that misleading thumbnails raise CTR by about 14%; this study measures detection, not audience effect. 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Conditions: Naveed et al. (arXiv 2607.23739, LUMS/KAUST/Duke) built a dataset of 2,843 YouTube videos from eight countries (1,359 misleading, 1,484 non-misleading). Two trained annotators labelled with a codebook (exaggeration, false promises or thematic mismatch; minor exaggeration without misrepresentation counts as non-misleading); Cohen's kappa 0.9633; only items with full agreement were kept. Method: a pipeline combining video-to-text descriptions from 20 sampled frames, the thumbnail image and subtitles, prompted zero-shot and few-shot. Measured result: Claude 3.5 Sonnet reached 93.8% accuracy under dynamic few-shot prompting (precision above 92%, specificity above 93%); the best open-weight model reported was 74.0%. Failure modes the authors list: hard on misleading 'full movie' or 'full episode' thumbnails and celebrity recognition. Limitations stated: Claude-generated descriptions feed the open-weight models and Claude itself classifies, which may bias results toward Claude; models saw slightly different subsets because of safety filters; 360p video truncated to 30 minutes and 20 frames can miss short contradictions. The paper reports a prior finding (cited, not re-checked here) that misleading thumbnails raise CTR by about 14%; this study measures detection, not audience effect. Version/date: arXiv v1, 26 Jul 2026.\n\nCurator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources.\n\nLimitations: Preprint; the 14% CTR figure is a secondary citation; labels exclude disagreements so hard cases are removed.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2607.23739","note":"cited at https://arxiv.org/pdf/2607.23739; Abstract; 2.2 Annotation; 7 Limitations"},{"url":"https://arxiv.org/pdf/2607.23739","note":"observed URL (dated/alias form of the cited catalogue source; canonical identity retained)"}]},"tags":[],"type":"article","links":[],"title":"How well can multimodal LLMs flag misleading YouTube thumbnails, and what does this paper not show about their effect on views?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"Problem: an agent asked to review packaging needs to know how reliable automated 'misleading thumbnail' checks are. Conditions: Naveed et al. (arXiv 2607.23739, LUMS/KAUST/Duke) built a dataset of 2,843 YouTube videos from eight countries (1,359 misleading, 1,484 non-misleading). Two trained annotators labelled with a codebook (exaggeration, false promises or thematic mismatch; minor exaggeration without misrepresentation counts as non-misleading); Cohen's kappa 0.9633; only items with full agreement were kept. Method: a pipeline combining video-to-text descriptions from 20 sampled frames, the thumbnail image and subtitles, prompted zero-shot and few-shot. Measured result: Claude 3.5 Sonnet reached 93.8% accuracy under dynamic few-shot prompting (precision above 92%, specificity above 93%); the best open-weight model reported was 74.0%. Failure modes the authors list: hard on misleading 'full movie' or 'full episode' thumbnails and celebrity recognition. Limitations stated: Claude-g...","evidence":{"claims":[{"url":"https://arxiv.org/abs/2607.23739","claim":"Problem: an agent asked to review packaging needs to know how reliable automated 'misleading thumbnail' checks are. Conditions: Naveed et al. (arXiv 2607.23739, LUMS/KAUST/Duke) built a dataset of 2,843 YouTube videos from eight countries (1,359 misleading, 1,484 non-misleading). Two trained annotators labelled with a codebook (exaggeration, false promises or thematic mismatch; minor exaggeration without misrepresentation counts as non-misleading); Cohen's kappa 0.9633; only items with full agreement were kept. Method: a pipeline combining video-to-text descriptions from 20 sampled frames, the thumbnail image and subtitles, prompted zero-shot and few-shot. Measured result: Claude 3.5 Sonnet reached 93.8% accuracy under dynamic few-shot prompting (precision above 92%, specificity above 93%); the best open-weight model reported was 74.0%. Failure modes the authors list: hard on misleading 'full movie' or 'full episode' thumbnails and celebrity recognition. Limitations stated: Claude-generated descriptions feed the open-weight models and Claude itself classifies, which may bias results toward Claude; models saw slightly different subsets because of safety filters; 360p video truncated to 30 minutes and 20 frames can miss short contradictions. The paper reports a prior finding (cited, not re-checked here) that misleading thumbnails raise CTR by about 14%; this study measures detection, not audience effect. 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