{"url":"/dataset/mob","name":"MoB","full_name":"Malicious or Benign Cartoon Videos","description_markdown":"A dataset of cartoon video clips. For each video clip, the presence or absence of each feature was marked by the annotators. \r\n\r\nMalicious: Video  content  which may  not  be  suitable  for  viewing  by  toddlers  and  pre-schoolers includes a set of clearly defined, trivial and intu-itive features as well as some complex and subtle audio andvideo features. The former includes forms of obscenity and violence on a higher level e.g. nudity, gore, etc. while the latter includes elements such as fast repetitive motion, loud music, disgusting and scary characters, smashing people or things, forms of aggression, loud music, screaming or shout-ing, gunshots and explosions. \r\n\r\nBenign: Educational videos and videos of nursery rhymes are usually considered to be appropriate for the toddlers and pre-schoolers; in fact some experts recommend letting kids watch  them  for  a  limited  number  of  hours.Benign videos are characterized by a slower tempo, softer music or sound effects, moderate-paced motions, and soft-toned  conversations.  Most  importantly,  benign  video  content should not contain any indicators of malicious content as discussed earlier.","description_withheld":null,"homepage":"https://github.com/syedhammadahmed/mob","introduced_date":"2023-05-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/malicious-or-benign-towards-effective-content","title":"Malicious or Benign? Towards Effective Content Moderation for Children's Videos","first_author":"Syed Hammad Ahmed","url":null},"license":null,"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Video Classification","url":"/task/video-classification","datasets_with_task":"/datasets/task/video-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MoB"],"data_loaders":[{"repo":"https://github.com/syedhammadahmed/mob","url":"https://github.com/syedhammadahmed/mob","frameworks":["pytorch"]}],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-classification-on-mob","task":"Video Classification","dataset_variant":"MoB","rows":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"VTN","paper":"/paper/malicious-or-benign-towards-effective-content","metrics":{"Accuracy":"77.85"},"code_links":[{"title":"syedhammadahmed/mob","url":"https://github.com/syedhammadahmed/mob"},{"title":"andreascas/oran_gai","url":"https://github.com/andreascas/oran_gai"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/malicious-or-benign-towards-effective-content","title":"Malicious or Benign? Towards Effective Content Moderation for Children's Videos","date":"2023-05-24","rows_on_this_dataset":3,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}