{"paper":{"title":"Scale over Preference: The Impact of AI-Generated Content on Online Content Ecology","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"AI-generated content creators achieve aggregate engagement comparable to human creators through high-volume production despite user preference for human content.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chenyi Lei, Fengbin Zhu, Fuli Feng, Han Li, Tianhao Shi, Tian Yang, Wenwu Ou, Xiaoyan Zhao, Yang Song, Yang Zhang, Yongdong Zhang","submitted_at":"2026-04-02T06:46:43Z","abstract_excerpt":"The rapid proliferation of Artificial Intelligence-Generated Content (AIGC) is fundamentally restructuring online content ecologies, necessitating a rigorous examination of its behavioral and distributional implications. Leveraging a comprehensive longitudinal dataset comprising tens of millions of users from a leading Chinese video-sharing platform, this study elucidated the distinct creation and consumption behaviors characterizing AIGC versus Human-Generated Content (HGC). We identified a prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"A prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the observed behaviors and preferences are generalizable beyond the specific platform and dataset, and that the algorithmic moderation is accurately identified without confounding factors.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AIGC creators match HGC engagement via high-volume production despite consumer preference for HGC, with algorithms moderating the effect.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"AI-generated content creators achieve aggregate engagement comparable to human creators through high-volume production despite user preference for human content.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f2e45fba30072f73620cfbf7b0a95652301636d90c3519ac46c603e51015896d"},"source":{"id":"2604.01690","kind":"arxiv","version":2},"verdict":{"id":"969bce86-3fd5-4b28-a943-ec0014693cde","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T22:10:07.526600Z","strongest_claim":"A prevalent scale-over-preference dynamic, wherein AIGC creators achieve aggregate engagement comparable to HGC creators through high-volume production, despite a marked consumer preference for HGC.","one_line_summary":"AIGC creators match HGC engagement via high-volume production despite consumer preference for HGC, with algorithms moderating the effect.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the observed behaviors and preferences are generalizable beyond the specific platform and dataset, and that the algorithmic moderation is accurately identified without confounding factors.","pith_extraction_headline":"AI-generated content creators achieve aggregate engagement comparable to human creators through high-volume production despite user preference for human content."},"references":{"count":34,"sample":[{"doi":"","year":2024,"title":"Scientific Reports14(1), 10413 (2024)","work_id":"55ac03de-64c9-410a-9b71-6bcdf64f98f9","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"PNAS nexus4(6), 170 (2025)","work_id":"0d0b26f1-bf53-45f1-bf23-65d68b6a35d8","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Scientific Reports (2026)","work_id":"9ee74c2f-787f-44de-82b3-d1095456565a","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Advances in neural information processing systems35, 27730–27744 (2022)","work_id":"a5ef1ebf-e214-4ba0-8555-04f15135a60c","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Nature (2026)","work_id":"ff5858dc-4402-460f-8b6d-68313579cb10","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":34,"snapshot_sha256":"8c1a4faae9e4ac9cf75aca652c04d86c65f0a1fd55332b1cf431dc8298d87408","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"3315a2c4fb1abda34f799d4136faa38fc056efef1dd3f4547ba56a14473ca5f5"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}