{"id":"https://openalex.org/W2892352525","doi":"https://doi.org/10.18653/v1/d18-1303","title":"SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories","display_name":"SafeCity: Understanding Diverse Forms of Sexual Harassment Personal Stories","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2892352525","doi":"https://doi.org/10.18653/v1/d18-1303","mag":"2892352525"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d18-1303","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1303","pdf_url":"https://www.aclweb.org/anthology/D18-1303.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D18-1303.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083128256","display_name":"Sweta Karlekar","orcid":null},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]},{"id":"https://openalex.org/I1333535994","display_name":"University of North Carolina Health Care","ror":"https://ror.org/00qz24g20","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1333535994"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sweta Karlekar","raw_affiliation_strings":["UNC Chapel Hill"],"affiliations":[{"raw_affiliation_string":"UNC Chapel Hill","institution_ids":["https://openalex.org/I1333535994","https://openalex.org/I114027177"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001987532","display_name":"Mohit Bansal","orcid":"https://orcid.org/0000-0001-5522-1351"},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]},{"id":"https://openalex.org/I1333535994","display_name":"University of North Carolina Health Care","ror":"https://ror.org/00qz24g20","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1333535994"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohit Bansal","raw_affiliation_strings":["UNC Chapel Hill"],"affiliations":[{"raw_affiliation_string":"UNC Chapel Hill","institution_ids":["https://openalex.org/I1333535994","https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083128256"],"corresponding_institution_ids":["https://openalex.org/I114027177","https://openalex.org/I1333535994"],"apc_list":null,"apc_paid":null,"fwci":3.5436,"has_fulltext":true,"cited_by_count":38,"citation_normalized_percentile":{"value":0.94262789,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2805","last_page":"2811"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9944999814033508,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.9944999814033508,"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/T13157","display_name":"Cancer-related gene regulation","score":0.9801999926567078,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9569000005722046,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/harassment","display_name":"Harassment","score":0.9298779964447021},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5936675071716309},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5871862173080444},{"id":"https://openalex.org/keywords/beijing","display_name":"Beijing","score":0.5820270776748657},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.45853739976882935},{"id":"https://openalex.org/keywords/sexual-abuse","display_name":"Sexual abuse","score":0.44451671838760376},{"id":"https://openalex.org/keywords/hamming-code","display_name":"Hamming code","score":0.4267064929008484},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36283251643180847},{"id":"https://openalex.org/keywords/internet-privacy","display_name":"Internet privacy","score":0.34956419467926025},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.3058165907859802},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.22496023774147034},{"id":"https://openalex.org/keywords/history","display_name":"History","score":0.11634024977684021},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10976424813270569},{"id":"https://openalex.org/keywords/poison-control","display_name":"Poison control","score":0.0909801721572876},{"id":"https://openalex.org/keywords/human-factors-and-ergonomics","display_name":"Human factors and ergonomics","score":0.0884053111076355}],"concepts":[{"id":"https://openalex.org/C2778976716","wikidata":"https://www.wikidata.org/wiki/Q3240539","display_name":"Harassment","level":2,"score":0.9298779964447021},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5936675071716309},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5871862173080444},{"id":"https://openalex.org/C2778304055","wikidata":"https://www.wikidata.org/wiki/Q657474","display_name":"Beijing","level":3,"score":0.5820270776748657},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.45853739976882935},{"id":"https://openalex.org/C2992354236","wikidata":"https://www.wikidata.org/wiki/Q43414","display_name":"Sexual abuse","level":4,"score":0.44451671838760376},{"id":"https://openalex.org/C73150493","wikidata":"https://www.wikidata.org/wiki/Q853922","display_name":"Hamming code","level":4,"score":0.4267064929008484},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36283251643180847},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.34956419467926025},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3058165907859802},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.22496023774147034},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.11634024977684021},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10976424813270569},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.0909801721572876},{"id":"https://openalex.org/C166735990","wikidata":"https://www.wikidata.org/wiki/Q1750812","display_name":"Human factors and ergonomics","level":3,"score":0.0884053111076355},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.0},{"id":"https://openalex.org/C157125643","wikidata":"https://www.wikidata.org/wiki/Q884707","display_name":"Block code","level":3,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C191935318","wikidata":"https://www.wikidata.org/wiki/Q148","display_name":"China","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d18-1303","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1303","pdf_url":"https://www.aclweb.org/anthology/D18-1303.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d18-1303","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d18-1303","pdf_url":"https://www.aclweb.org/anthology/D18-1303.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/5","display_name":"Gender equality","score":0.7400000095367432}],"awards":[],"funders":[{"id":"https://openalex.org/F4320309480","display_name":"Nvidia","ror":"https://ror.org/03jdj4y14"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2892352525.pdf","grobid_xml":"https://content.openalex.org/works/W2892352525.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W1141891500","https://openalex.org/W1522301498","https://openalex.org/W1601924930","https://openalex.org/W1810943226","https://openalex.org/W1815076433","https://openalex.org/W2053479646","https://openalex.org/W2095705004","https://openalex.org/W2102605133","https://openalex.org/W2146241755","https://openalex.org/W2156935079","https://openalex.org/W2158899491","https://openalex.org/W2160685721","https://openalex.org/W2187089797","https://openalex.org/W2250595267","https://openalex.org/W2259779547","https://openalex.org/W2282821441","https://openalex.org/W2284289336","https://openalex.org/W2296476705","https://openalex.org/W2516809705","https://openalex.org/W2623779865","https://openalex.org/W2766165088","https://openalex.org/W2784010253","https://openalex.org/W2786315637","https://openalex.org/W2804610389","https://openalex.org/W2952230511","https://openalex.org/W2962762902","https://openalex.org/W2962797668","https://openalex.org/W2962851944","https://openalex.org/W2963240572","https://openalex.org/W2963793818","https://openalex.org/W2964121744","https://openalex.org/W2964159778","https://openalex.org/W2964335273","https://openalex.org/W3140910462","https://openalex.org/W4231555810"],"related_works":["https://openalex.org/W2015747722","https://openalex.org/W2530773950","https://openalex.org/W2362050182","https://openalex.org/W2382418233","https://openalex.org/W2134970749","https://openalex.org/W2369897927","https://openalex.org/W4250048047","https://openalex.org/W2042870779","https://openalex.org/W2059786516","https://openalex.org/W2502903008"],"abstract_inverted_index":{"With":[0],"the":[1,27,36,53,58,126],"recent":[2],"rise":[3],"of":[4,9,38,45,60,72,82],"#MeToo,":[5],"an":[6,70],"increasing":[7],"number":[8],"personal":[10],"stories":[11,50],"about":[12],"sexual":[13,16,46],"harassment":[14,31],"and":[15,32,41,63,74,95,103,124],"abuse":[17],"have":[18],"been":[19],"shared":[20,51],"online.":[21],"In":[22],"order":[23],"to":[24,98],"push":[25],"forward":[26],"fight":[28],"against":[29],"such":[30],"abuse,":[33],"we":[34,85],"present":[35,86],"task":[37],"automatically":[39,113],"categorizing":[40],"analyzing":[42],"various":[43],"forms":[44],"harassment,":[47],"based":[48],"on":[49,52],"online":[54],"forum":[55],"SafeCity.":[56],"For":[57],"labels":[59],"groping,":[61],"ogling,":[62],"commenting,":[64],"our":[65,75],"single-label":[66],"CNN-RNN":[67],"model":[68,77,101],"achieves":[69,78],"accuracy":[71],"86.5%,":[73],"multi-label":[76],"a":[79],"Hamming":[80],"score":[81],"82.5%.":[83],"Furthermore,":[84],"analysis":[87],"using":[88],"LIME,":[89],"first-derivative":[90],"saliency":[91],"heatmaps,":[92],"activation":[93],"clustering,":[94],"embedding":[96],"visualization":[97],"interpret":[99],"neural":[100],"predictions":[102],"demonstrate":[104],"how":[105],"this":[106],"helps":[107],"extract":[108],"features":[109],"that":[110],"can":[111],"help":[112],"fill":[114],"out":[115],"incident":[116],"reports,":[117],"identify":[118],"unsafe":[119,122],"areas,":[120],"avoid":[121],"practices,":[123],"'pin":[125],"creeps'.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":7},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":8}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2025-10-10T00:00:00"}