{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84079"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84079","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Analyzing the Effect of Community Norms on Gender Bias","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Raut, Niharika"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Joseph, Kenneth","Computer Science and Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:42Z","date_published":"2022-06-21T15:47:42Z","updated_at":"2026-07-27T19:05:30Z","subjects":["computer science"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/84079","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Joseph, Kenneth","Computer Science and Engineering"]},{"key":"dc:creator","label":"Author","values":["Raut, Niharika"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:42Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/84079"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","The literature on bias in NLP has chiefly focused on the extent to which an algorithm produces outputs that can be differentiated along demographic lines. This is universally framed as undesirable and generally assumed to manifest in similar ways across different models/datasets. NLP models are claimed to play an essential role in shaping this societal bias, but it is equally important to understand how societal norms shape this bias as bias in NLP models originates from either the training corpus or the word embeddings or the algorithm. This thesis aims to find if the manifestation of bias, specifically gender bias, is different across different communities with different norms. The hypothesis is that the exhibition of gender bias is in sync with community norms, and changes along with it. We study gender bias in three different online communities - r/RoastMe, r/ToastMe, and r/RateMe. r/RoastMe roasts people who upload an image whereas in r/ToastMe, we find comments complimenting or appreciating the users. r/RateMe tries to rate a person \"objectively\" based on the uploaded image. Given such contrasting norms, We use NLP models to check if the data from these subreddits indeed has bias by trying to predict the gender of the person for whom a comment is made. We then see if the biases reflected in the models are in sync with the norms of the three communities by comparing the coefficients obtained from the models. The biases are also compared and contrasted across gender and communities by ranking the words associated with each gender. The evaluations show that gender bias exhibited in different communities is indeed different and depends on the context and the norms of the community. On comparing the biases in r/RoastMe and word embeddings, we also see that some words are universally gendered whereas others are context-specific.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Analyzing the Effect of Community Norms on Gender Bias"]}]}],"canonical_facts":{"dc:contributor":["Joseph, Kenneth","Computer Science and Engineering"],"dc:creator":["Raut, Niharika"],"dc:date":["2022-06-21T15:47:42Z","2020"],"dc:description":["M.S.","The literature on bias in NLP has chiefly focused on the extent to which an algorithm produces outputs that can be differentiated along demographic lines. This is universally framed as undesirable and generally assumed to manifest in similar ways across different models/datasets. NLP models are claimed to play an essential role in shaping this societal bias, but it is equally important to understand how societal norms shape this bias as bias in NLP models originates from either the training corpus or the word embeddings or the algorithm. This thesis aims to find if the manifestation of bias, specifically gender bias, is different across different communities with different norms. The hypothesis is that the exhibition of gender bias is in sync with community norms, and changes along with it. We study gender bias in three different online communities - r/RoastMe, r/ToastMe, and r/RateMe. r/RoastMe roasts people who upload an image whereas in r/ToastMe, we find comments complimenting or appreciating the users. r/RateMe tries to rate a person \"objectively\" based on the uploaded image. Given such contrasting norms, We use NLP models to check if the data from these subreddits indeed has bias by trying to predict the gender of the person for whom a comment is made. We then see if the biases reflected in the models are in sync with the norms of the three communities by comparing the coefficients obtained from the models. The biases are also compared and contrasted across gender and communities by ranking the words associated with each gender. The evaluations show that gender bias exhibited in different communities is indeed different and depends on the context and the norms of the community. On comparing the biases in r/RoastMe and word embeddings, we also see that some words are universally gendered whereas others are context-specific.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84079"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["computer science"],"dc:title":["Analyzing the Effect of Community Norms on Gender Bias"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:30Z"}