{"id":"https://openalex.org/W4410636680","doi":"https://doi.org/10.1145/3701716.3717551","title":"Entity-Aware Optimal Transport and Residual Attention for Multimodal Content Moderation","display_name":"Entity-Aware Optimal Transport and Residual Attention for Multimodal Content Moderation","publication_year":2025,"publication_date":"2025-05-08","ids":{"openalex":"https://openalex.org/W4410636680","doi":"https://doi.org/10.1145/3701716.3717551"},"language":"en","primary_location":{"id":"doi:10.1145/3701716.3717551","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3717551","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3717551","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3717551","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028892629","display_name":"Siddhant Bikram Shah","orcid":"https://orcid.org/0009-0000-9403-8728"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Siddhant Bikram Shah","raw_affiliation_strings":["Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092596279","display_name":"Shuvam Shiwakoti","orcid":"https://orcid.org/0009-0004-4716-2696"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuvam Shiwakoti","raw_affiliation_strings":["Virginia Tech, Blacksburg, VA, USA"],"affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049525793","display_name":"Touhid Bhuiyan","orcid":"https://orcid.org/0000-0002-6747-0846"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Touhid Bhuiyan","raw_affiliation_strings":["Washington University of Science and Technology, Alexandria, VA, USA"],"affiliations":[{"raw_affiliation_string":"Washington University of Science and Technology, Alexandria, VA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034828056","display_name":"Mohammad Ali Moni","orcid":"https://orcid.org/0000-0003-0756-1006"},"institutions":[{"id":"https://openalex.org/I153230381","display_name":"Charles Sturt University","ror":"https://ror.org/00wfvh315","country_code":"AU","type":"education","lineage":["https://openalex.org/I153230381"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Mohammad Ali Moni","raw_affiliation_strings":["Charles Sturt University, Bathurst, Australia and Washington University of Science and Technology, Alexandria, VA, USA"],"affiliations":[{"raw_affiliation_string":"Charles Sturt University, Bathurst, Australia and Washington University of Science and Technology, Alexandria, VA, USA","institution_ids":["https://openalex.org/I153230381"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020807509","display_name":"Surendrabikram Thapa","orcid":"https://orcid.org/0000-0003-4119-8239"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Surendrabikram Thapa","raw_affiliation_strings":["Virginia Tech, Blacksburg, VA, USA"],"affiliations":[{"raw_affiliation_string":"Virginia Tech, Blacksburg, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077006200","display_name":"Usman Naseem","orcid":"https://orcid.org/0000-0003-0191-7171"},"institutions":[{"id":"https://openalex.org/I99043593","display_name":"Macquarie University","ror":"https://ror.org/01sf06y89","country_code":"AU","type":"education","lineage":["https://openalex.org/I99043593"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Usman Naseem","raw_affiliation_strings":["Macquarie University, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"Macquarie University, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I99043593"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5028892629"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.05809102,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2306","last_page":"2313"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9990000128746033,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9943000078201294,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/moderation","display_name":"Moderation","score":0.7593386769294739},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.6448886394500732},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6162192821502686},{"id":"https://openalex.org/keywords/content","display_name":"Content (measure theory)","score":0.5153407454490662},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33722123503685},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3309013843536377},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1932935118675232},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1575404405593872},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13357657194137573}],"concepts":[{"id":"https://openalex.org/C93225998","wikidata":"https://www.wikidata.org/wiki/Q1941972","display_name":"Moderation","level":2,"score":0.7593386769294739},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.6448886394500732},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6162192821502686},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.5153407454490662},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33722123503685},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3309013843536377},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1932935118675232},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1575404405593872},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13357657194137573},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3701716.3717551","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3717551","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3717551","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},{"id":"pmh:oai:vtechworks.lib.vt.edu:10919/137457","is_oa":true,"landing_page_url":"https://hdl.handle.net/10919/137457","pdf_url":null,"source":{"id":"https://openalex.org/S4306400248","display_name":"VTechWorks (Virginia Tech)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I859038795","host_organization_name":"Virginia Tech","host_organization_lineage":["https://openalex.org/I859038795"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":{"id":"doi:10.1145/3701716.3717551","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3701716.3717551","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3701716.3717551","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM on Web Conference 2025","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4410636680.pdf","grobid_xml":"https://content.openalex.org/works/W4410636680.grobid-xml"},"referenced_works_count":11,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2282821441","https://openalex.org/W2789285779","https://openalex.org/W3093080381","https://openalex.org/W3195130895","https://openalex.org/W4321480037","https://openalex.org/W4367047467","https://openalex.org/W4385805046","https://openalex.org/W4390190020","https://openalex.org/W4396844013","https://openalex.org/W4404782199"],"related_works":["https://openalex.org/W2740268725","https://openalex.org/W3093710306","https://openalex.org/W750217911","https://openalex.org/W4206949499","https://openalex.org/W2296969234","https://openalex.org/W4226254121","https://openalex.org/W4388229525","https://openalex.org/W131533274","https://openalex.org/W4313163826","https://openalex.org/W3204019825"],"abstract_inverted_index":{"The":[0],"increasing":[1],"prevalence":[2],"of":[3,17,121,164],"memes":[4,24,122,202],"on":[5,131,217],"social":[6,218],"media":[7],"platforms":[8],"has":[9],"amplified":[10],"both":[11,76,149],"the":[12,162,179,208],"positive":[13],"and":[14,28,78,88,91,104,113,139,151,170,213],"negative":[15],"impact":[16],"these":[18],"highly":[19],"shareable,":[20],"multimodal":[21],"artifacts.":[22],"While":[23],"can":[25,31,198],"be":[26],"humorous":[27],"engaging,":[29],"they":[30],"also":[32],"serve":[33],"as":[34],"vehicles":[35],"for":[36,58,85,117,210],"hateful":[37],"or":[38,46,126],"harmful":[39,59,201],"content":[40,215],"that":[41,62,143,186,189],"targets":[42],"specific":[43],"social,":[44],"ethnic,":[45],"political":[47],"groups.":[48],"In":[49],"this":[50],"paper,":[51],"we":[52,141],"propose":[53],"ImTOTMeme,":[54],"a":[55,97],"novel":[56],"framework":[57],"meme":[60],"detection":[61],"combines":[63],"an":[64,157,187],"optimal":[65],"transport-based":[66],"alignment":[67,193],"mechanism":[68],"with":[69,194],"global":[70],"residual":[71],"interactions":[72],"to":[73,95,160,174],"better":[74],"capture":[75],"local":[77,191],"contextual":[79,127,196],"cues.":[80],"We":[81,108,154],"leverage":[82],"CLIP":[83],"embeddings":[84,112],"initial":[86],"image":[87],"text":[89,106],"representations":[90],"employ":[92],"Sinkhorn":[93],"iteration":[94],"learn":[96],"minimal-cost":[98],"matching":[99],"between":[100],"fine-grained":[101],"visual":[102],"tokens":[103],"OCR-extracted":[105],"tokens.":[107],"further":[109,155],"incorporate":[110],"facial":[111],"entity":[114],"information,":[115],"allowing":[116],"more":[118,211],"nuanced":[119],"analysis":[120],"involving":[123],"human":[124],"subjects":[125],"references.":[128],"Through":[129],"experiments":[130],"four":[132],"publicly":[133],"available":[134],"datasets:":[135],"Harm-C,":[136],"Harm-P,":[137],"FHM,":[138],"MultiOFF,":[140],"demonstrate":[142],"ImTOTMeme":[144],"achieves":[145],"competitive":[146],"accuracy":[147],"in":[148,167],"binary":[150],"multi-class":[152],"settings.":[153],"conduct":[156],"ablation":[158],"study":[159],"verify":[161],"significance":[163],"each":[165],"component":[166],"our":[168],"framework,":[169],"use":[171],"LIME-based":[172],"visualizations":[173],"provide":[175],"deeper":[176],"interpretability":[177],"into":[178],"model's":[180],"classification":[181],"decisions.":[182],"Our":[183],"findings":[184],"highlight":[185],"approach":[188],"balances":[190],"token-level":[192],"broader":[195],"modeling":[197],"effectively":[199],"detect":[200],"across":[203],"diverse":[204],"topical":[205],"domains,":[206],"paving":[207],"way":[209],"robust":[212],"transparent":[214],"moderation":[216],"media.":[219]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
