{"id":"1389f8c5-cfee-490b-9c02-58dcfbc3df37","arxiv_id":"2508.04900","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Temporal label noise is systemic in video-level hate annotations: trimming hateful videos to timestamped hate segments raises macro-F1 by 19.34% and 30.45% on HateMM and MultiHateClip-English.","lead":"Videos labeled as hateful often contain long stretches of non-hateful content, and this paper quantifies that temporal label noise in two benchmark datasets. By trimming videos to their annotated hateful segments, the authors show detection models gain 19-30 points of macro-F1, and argue that hate detection should move to timestamp-level labels.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Reported F1 gains compare different test distributions (full videos vs trimmed segments) and training setups, so they are not a clean measure of label noise; a matched test-set comparison is needed.","rationale":"The paper's core evidence for 'temporal label noise' is the F1 gap in Table 3. But that gap is computed across different evaluation protocols: coarse detection uses full videos as both train and test instances; clean segment detection uses short trimmed hate segments as positive instances and full non-hate videos as negative instances. Since segment length and content homogeneity differ systematically, a model trained/tested on segments can trivially achieve higher F1 without any reduction in label noise. The paper's own 'Noisy-to-Clean' row is closer to a controlled comparison but changes hyperparameters and training instance count relative to the clean row, so even that 20+ point gap is not clean. The timestamp-reliability issue (Section 4.4) compounds this: if the timestamps are incomplete or context-dependent, the 'clean' split itself is uncertain. The reader's weakest_assumption focused on timestamp correctness; I agree that is a concern, but the evaluation protocol mismatch is more directly load-bearing for the headline claim, because even under perfect timestamps the comparison would be invalid. A matched test-set rerun would settle it. For these reasons, the verdict should remain conditional (UNCHANGED relative to the reader), pending a controlled comparison.","tokens_in":10494,"tokens_out":6139,"duration_ms":71509,"concrete_test":"Re-run Clean Segment-Level and Noisy-to-Clean with the same test set (trimmed hate segments + non-hate videos), same backbone/feature extraction, same optimizer, epochs, batch size, learning rate, and matched number of training samples (e.g., subsample segments or use full-video bags), across the same 5 folds; report per-fold MF1 and a paired significance test. If clean-trained no longer beats noisy-trained by roughly 20+ points, the headline gains are confounded.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central quantitative claim (Section 5.2, Table 3) is that Clean Segment-Level Detection improves macro F1 by 19.34 (HateMM) and 30.45 (MultiHateClip-English) over Coarse Video-level Detection. However, these two rows differ in both training and test distribution: coarse models see entire hateful videos, clean models see only timestamped hate segments (plus full non-hate videos). The 19-30 point gap therefore conflates the effect of label noise with the effect of changing the unit of classification (short, explicitly hateful snippets vs long, partly neutral videos) and with different training-set sizes/hyperparameters (Section 5.1). The row that actually holds the test distribution fixed—Noisy-to-Clean Generalization, which trains on full videos and tests on trimmed hate segments—uses different epochs/batch/lr, so it also does not isolate label noise. Moreover, the paper's own Section 4.4 shows trimmed non-hate and trimmed hate segments overlap semantically, which is evidence that the timestamp-derived 'clean' labels are themselves uncertain. Until a comparison is run with identical test set, training budget, and instance counts, the headline improvements cannot be attributed to temporal label noise.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper studies temporal label noise in multimodal hateful video classification by exploiting timestamp annotations in HateMM and MultiHateClip-English to create trimmed hate and trimmed non-hate segments. It presents descriptive analyses (lexical, acoustic, visual, and semantic) comparing these segments and then reports four experimental settings: Coarse Video-level Detection, Noisy-to-Clean Generalization, Clean-to-Noisy Generalization, and Clean Segment-Level Detection. The central claim is that clean segment-level detection outperforms coarse video-level detection by 19.34% and 30.45% macro-F1 on HateMM and MultiHateClip-English, respectively, and that temporal label noise systematically degrades model decision boundaries.","tokens_in":10807,"tokens_out":4687,"duration_ms":51065,"significance":"The paper addresses a genuinely underexplored issue—temporal granularity in hateful video labels—and provides a useful descriptive analysis of segment-level properties across two benchmarks. Releasing the trimmed segment sets and code is a concrete contribution. However, the headline quantitative claim is not yet supported by the experimental design, because the compared settings differ in test distribution, training data, and hyperparameters. The work is a valuable exploratory study, but the causal link between label noise and the reported performance gains requires a matched comparison.","major_comments":[{"comment":"The claimed 19.34% and 30.45% macro-F1 improvements are not a clean measure of label noise. Coarse Video-level Detection trains and tests on full videos, while Clean Segment-Level Detection trains and tests on trimmed hate segments plus full non-hate videos; the test distributions differ, so the gap conflates label noise with the easier task of classifying short, explicitly hateful snippets. Additionally, the Noisy-to-Clean Generalization row, which tests on trimmed segments but trains on full videos, uses different hyperparameters (Section 5.1: 40 epochs, batch 128, lr 1e-5 for HateMM; 20 epochs, batch 16, lr 1e-5 for MultiHateClip) than the other settings (20 epochs, batch 16, lr 1e-4). Provide a matched comparison with identical test set, training budget, instance counts, and hyperparameters.","section":"Section 5.2, Table 3"},{"comment":"The trimming protocol assumes the annotated timestamp pairs correctly and completely identify hateful spans, and that content outside those spans is genuinely non-hateful. Yet the paper's own analysis shows that trimmed non-hate segments overlap semantically with trimmed hate segments (Figures 6c, 6g) and are semantically distinct from genuine non-hate videos (Figures 6d, 6h). Section 4.2 also acknowledges the 'inadequacy of current temporal annotations.' Thus the 'clean' labels are themselves uncertain, and the high accuracy in Clean Segment-Level Detection may reflect learning the timestamp-induced boundary rather than true semantic content. Validate the timestamp annotations (e.g., human agreement on boundary placement) and evaluate on independently re-annotated segments.","section":"Section 3 vs Section 4.4"},{"comment":"The 70/10/20 split is not specified as being at the video level or segment level. If segments from the same video appear in both the training and test sets, the Clean Segment-Level Detection result would be inflated by video-level leakage, since the model could memorize video-specific features. Please state the split granularity; if it is segment-level, re-run the experiments with a video-level split and report both results.","section":"Section 5.1"}],"minor_comments":[{"comment":"The caption contains a typo: 'performanc' should be 'performance.'","section":"Figure 1"},{"comment":"Reference [20] lists the author as 'Rui Yang Tan,' but the text (Section 3) writes 'Tan Rui Yang'; please make the name consistent.","section":"References"},{"comment":"The reference title contains 'Confernece'—should be 'Conference.'","section":"Reference [2]"},{"comment":"The text says 'reported the top 15' significant Empath categories, but Figure 3 appears to show fewer categories; please clarify the count.","section":"Section 4.1"},{"comment":"The analysis assumes that if an object appears in any frame of a segment, it is present throughout the segment. This is a strong assumption that may bias the null result; state it as a limitation.","section":"Section 4.3"}],"recommendation":"major_revision","confidential_remarks":"The descriptive analysis and public release of trimmed segment data are valuable. The main obstacle to acceptance is the unmatched experimental comparison underlying the headline claim; a matched test-set and hyperparameter-controlled experiment would make the causal claim credible. I recommend major revision to require this re-analysis."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The one thing to know: this is the first paper I've seen that directly studies temporal label noise in multimodal hateful video classification, and it does a genuinely useful descriptive job. The authors take two existing timestamped datasets, trim hateful videos into hate and non-hate segments, and quantify how much non-hateful material sits inside videos labeled hateful. That analysis holds up. Their own plots and statistics show real contamination: hateful videos have roughly a third of content outside annotated hate spans, and the semantic embedding visualization shows trimmed non-hate from hateful videos overlaps heavily with trimmed hate. The acoustic and visual analyses honestly report little separation, which strengthens the point that hate expression is context-dependent. Code and data are released. That part is a solid contribution.\n\nThe soft spot is the quantitative headline. The 19-30 point macro-F1 gain for \"clean segment-level detection\" over \"coarse video-level detection\" is not a clean measure of label noise because the two settings differ in both training and test distribution. Coarse models are evaluated on full videos; clean models on trimmed hate segments plus non-hate videos. That's a different task, not just cleaner labels. The Noisy-to-Clean Generalization row is closer to a matched comparison, but there the hyperparameters change too (40 epochs, batch 128, lr 1e-5 vs 20/16/1e-4), so it still doesn't isolate label noise. Also, the trimming assumes the annotated timestamps correctly separate hateful from non-hateful content. The paper's own Section 4.4 suggests those boundaries are fuzzy. So the F1 gap likely overstates the benefit of clean labels. A matched experiment—same model, same hyperparameters, same test set, varying only the label/instance granularity—would settle it.\n\nThat caveat doesn't sink the paper. The descriptive findings and the argument for temporally-aware benchmarks are worthwhile on their own, and the authors are transparent about the ambiguity in their data. The paper is for researchers building or evaluating hateful video detectors, and for anyone thinking about label noise in weakly supervised video tasks. It deserves a serious referee. My recommendation: send it to review, but require the authors to add a properly matched test-set comparison and report variance across folds before the F1 numbers are taken at face value.","headline":"First real look at temporal label noise in hateful video detection, but the headline F1 gains conflate label noise with a change in test distribution.","tokens_in":11214,"tokens_out":1921,"would_cite":true,"duration_ms":23471,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Coarse video-level hate labels are systematically noisy; timestamp-trimmed training raises macro F1 by 19.34% and 30.45% on two benchmarks.","keywords":["hateful video detection","temporal label noise","video-level annotations","timestamp trimming","multimodal classification","label noise robustness","HateMM","MultiHateClip"],"falsifier":"Have independent raters annotate the trimmed non-hate segments (and the context around trimmed hate segments) at frame level; if a substantial fraction of trimmed non-hate segments is judged hateful or if removing context flips human hate judgments, then the macro-F1 gap between clean segment-level and coarse video-level detection would shrink or reverse when those segments are relabelled.","tokens_in":10460,"feed_emoji":"🎬","tokens_out":8321,"duration_ms":87644,"temperature":0.7,"pith_summary":"Video-level hate labels are the field's default, but they are temporally coarse: a video flagged hateful can contain long stretches that are not hateful. This paper argues that the resulting label noise is systematic, not random, and that replacing coarse labels with timestamp-trimmed segment labels changes measured performance dramatically. Using annotated hate spans from HateMM and MultiHateClip-English, the authors extract trimmed hate and trimmed non-hate segments, then analyze lexical, acoustic, visual, and semantic overlap between them. In controlled experiments, clean segment-level detection beats coarse video-level detection by 19.34% macro F1 on HateMM and 30.45% on MultiHateClip-English, while cross-generalization between noisy and clean settings fails in both directions. The upshot: current video-level benchmarks partly reward models that memorize noise, and hate expression itself is context-dependent and temporally continuous rather than cleanly bounded.","feed_headline":"Timestamp-clean labels beat video-level hate detection by up to 30%","feed_subtitle":"Coarse video-level labels bury hate in long neutral stretches; segment-level labels expose the gap.","key_machinery":"The central mechanism is the timestamp-trimming protocol: for each hate-labelled video with annotated hate spans $(s_i, e_i)$, the interval $[s_i, e_i]$ becomes a trimmed hate segment and the outside portions become trimmed non-hate segments, while non-hate videos stay whole. This converts coarse video-level labels into pseudo-clean segment labels, making the noise visible and measurable. The argument is carried by comparing four controlled settings—coarse video-level detection, noisy-to-clean generalisation, clean-to-noisy generalisation, and clean segment-level detection—on identical feature pipelines, so performance gaps can be attributed to label quality rather than architecture.","core_discovery":"The paper's central claim is that coarse, video-level annotations in multimodal hate-video datasets carry systematic temporal label noise, and that this noise—not model architecture—explains a large share of apparent difficulty and apparent performance. The evidence: after trimming videos by annotated hate spans, 58.64% of extracted HateMM segments and 35.16% of MultiHateClip-English segments are non-hate content from inside hate-labelled videos. Semantic embedding visualisations show that these trimmed non-hate segments do not cluster with genuinely non-hate videos but sit in an intermediate region overlapping trimmed hate segments, indicating gradual semantic drift rather than discrete bou","pith_inferences":["If the trim labels are trusted, then leaderboard comparisons on HateMM and MultiHateClip-English partly rank models by how well they absorb video-level noise; re-annotation to segment level could reorder published methods without changing their architectures.","A natural testable extension is to train on clean segments while conditioning on the surrounding video context, which may close the clean-to-noisy gap that the paper leaves open.","The semantic overlap between trimmed hate and trimmed non-hate segments suggests treating timestamps as soft evidence or as targets for weakly supervised span localisation, rather than hard cut points.","The platform contrast (BitChute vs YouTube) indicates the noise structure is platform-dependent; annotation guidelines may need platform-specific temporal granularity."],"forward_implications":["Clean segment-level detection raises macro F1 from 79.30% to 98.64% on HateMM and from 64.37% to 97.31% on MultiHateClip-English (Table 3).","Models trained on noisy video labels fail to identify trimmed hateful segments, and models trained on clean segments fail on full videos; the two-way failure shows the noise is structural, not random.","Because semantic embedding structure stays similar across settings while confidence collapses under noisy training, the paper implies label noise mainly corrupts decision boundaries rather than learned features.","Current video-level benchmarks should be re-evaluated with segment-level or temporally-aware labels, since a large part of their difficulty is an artefact of annotation granularity."],"supporting_citations":[{"why":"Introduces the HateMM dataset and its baseline detection pipeline, providing the video-level labels and timestamp annotations used for trimming.","marker":"[4]"},{"why":"Introduces the MultiHateClip-English dataset and baseline, supplying the second set of timestamp-annotated hateful videos.","marker":"[21]"},{"why":"Provides the lexical category lexicon used to compare trimmed hate and trimmed non-hate transcripts.","marker":"[9]"},{"why":"Supplies the text embedding model used to produce semantic features for the embedding-space visualisations.","marker":"[6]"},{"why":"Supplies the dimensionality-reduction projection used to visualise semantic overlap between segment types.","marker":"[16]"},{"why":"Supplies the MFCC audio features used in the multimodal classification pipeline.","marker":"[17]"},{"why":"Supplies the vision transformer feature extractor used for video frames in the classification pipeline.","marker":"[7]"}],"fun_headline_variants":["Coarse video labels hide true hate: 58% of 'hate' clips are neutral","Time-stamped hate labels expose 58% of 'hateful' segments as non-hate","Hate video benchmarks are mislabeled: 58% of 'hate' segments are non-hate","Time-stamped labels reveal 58% of hate-video segments are neutral","58% of 'hateful' segments in hate-video datasets are non-hate"],"cache_read_input_tokens":2816,"weakest_assumption_plain":"The load-bearing premise is that the annotated timestamp pairs mark exactly when hate occurs and that everything outside them in a hate-labelled video is genuinely non-hateful; if the timestamps are wrong or the surrounding context is required to interpret the hate, the 'clean' segment labels are not clean and the reported gains are inflated.","fun_headline_variants_meta":{"raw":{"variants":["Coarse video labels hide true hate: 58% of 'hate' clips are neutral","Time-stamped hate labels expose 58% of 'hateful' segments as non-hate","Hate video benchmarks are mislabeled: 58% of 'hate' segments are non-hate","Time-stamped labels reveal 58% of hate-video segments are neutral","58% of 'hateful' segments in hate-video datasets are non-hate"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001606,"raw_usage":{"total_tokens":6237,"prompt_tokens":750,"completion_tokens":5487,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":494,"completion_tokens_details":{"reasoning_tokens":5368}},"tokens_in":494,"tokens_out":5487,"duration_ms":41715,"temperature":1.0,"reasoning_tokens":5368,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-05T23:40:50.800050+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Have independent raters annotate the trimmed non-hate segments (and the context around trimmed hate segments) at frame level; if a substantial fraction of trimmed non-hate segments is judged hateful or if removing context flips human hate judgments, then the macro-F1 gap between clean segment-level and coarse video-level detection would shrink or reverse when those segments are relabelled.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Introduces the HateMM dataset and its baseline detection pipeline, providing the video-level labels and timestamp annotations used for trimming."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Introduces the MultiHateClip-English dataset and baseline, supplying the second set of timestamp-annotated hateful videos."}],"review_version":1}