{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T03:28:44Z","timestamp":1782876524909,"version":"3.54.5"},"reference-count":91,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Softw. Eng. Methodol."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>In an industry dominated by straight men, many developers representing other gender identities and sexual orientations often encounter hateful or discriminatory messages. Such communications pose barriers to participation for women and LGBTQ+ persons. Due to sheer volume, manual inspection of all communications for discriminatory communication is infeasible for a large-scale Free Open Source Software (FLOSS) community. To address this challenge, this study proposes an automated mechanism to identify Sexual Orientation and Gender Identity Discriminatory (SGID) texts in software developers\u2019 communications. On this goal, we trained and evaluated SGID4SE (Sexual orientation and Gender Identity Discriminatory text identification for (4) Software Engineering texts), a supervised learning-based tool. SGID4SE incorporates six preprocessing steps and 10 state-of-the-art algorithms. SGID4SE employs six distinct strategies to enhance the performance of the minority class. We empirically evaluated each strategy and identified an optimum configuration for each algorithm. In our ten-fold cross-validation-based evaluations, a BERT-based model achieves the best performance with 85.9% precision, 80.0% recall, and 82.9% F1-score for the SGID class. This model achieves 95.7% accuracy and a Matthews Correlation Coefficient of 80.4%. Our dataset and tool establish a foundation for further research in this direction.<\/jats:p>","DOI":"10.1145\/3757739","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T15:18:43Z","timestamp":1754061523000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Identification of Sexual Orientation and Gender Identity Discriminatory Texts from Issue Comments"],"prefix":"10.1145","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8316-2560","authenticated-orcid":false,"given":"Sayma","family":"Sultana","sequence":"first","affiliation":[{"name":"Department of Computer Science, Wayne State University, Detroit, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6440-7596","authenticated-orcid":false,"given":"Jaydeb","family":"Sarker","sequence":"additional","affiliation":[{"name":"University of Nebraska, Omaha, Nebraska, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6898-3979","authenticated-orcid":false,"given":"Farzana","family":"Israt","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wayne State University, Detroit, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7293-1558","authenticated-orcid":false,"given":"Rajshakhar","family":"Paul","sequence":"additional","affiliation":[{"name":"Idaho State University, Pocatello, Idaho, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3178-6232","authenticated-orcid":false,"given":"Amiangshu","family":"Bosu","sequence":"additional","affiliation":[{"name":"Wayne State University, Detroit, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Hatebase dataset. 2022. Retrieved December 23 2022 from https:\/\/hatebase.org\/"},{"key":"e_1_3_2_3_2","unstructured":"LGBT slang. 2022. Retrieved December 23 2022 from https:\/\/en.wikipedia.org\/wiki\/LGBT_slang"},{"key":"e_1_3_2_4_2","unstructured":"Pejorative terms for women. 2022. Retrieved December 23 2022 from https:\/\/en.wikipedia.org\/wiki\/Category:Pejorative_terms_for_women"},{"key":"e_1_3_2_5_2","unstructured":"Search: Github docs. 2022. Retrieved December 23 2022 from https:\/\/docs.github.com\/en\/rest\/search"},{"key":"e_1_3_2_6_2","unstructured":"Content Consumer. 2008. The great Ubuntu-girlfriend experiment. Retrieved June 1 2014 from http:\/\/contentconsumer.wordpress.com\/2008\/04\/27\/is-ubuntu-useableenough-for-my-girlfriend\/"},{"key":"e_1_3_2_7_2","first-page":"242","article-title":"Detecting misogynous tweets","author":"Ahluwalia Resham","year":"2018","unstructured":"Resham Ahluwalia, Evgeniia Shcherbinina, Edward Callow, Anderson C. A. Nascimento, and Martine De Cock. 2018. Detecting misogynous tweets. In Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval@ SEPLN), 242\u2013248.","journal-title":"Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval@ SEPLN)"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2017.8115623"},{"key":"e_1_3_2_9_2","unstructured":"Conversation AI. 2025. What if technology could help improve conversations online? Retrieved June 30 2025 from https:\/\/www.perspectiveapi.com\/"},{"key":"e_1_3_2_10_2","unstructured":"Conversation AI. 2018. Annotation instructions for toxicity with sub-attributes. Retrieved from https:\/\/github.com\/conversationai\/conversationai.github.io\/blob\/master \/crowdsourcing_annotation_schemes\/toxicity_with_subattributes.md"},{"key":"e_1_3_2_11_2","unstructured":"Anonymous. 2014. Leaving toxic open source communities. Retrieved from https:\/\/modelviewculture.com\/pieces\/leaving-toxic-open-source-communities"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91947-8_6"},{"key":"e_1_3_2_13_2","volume-title":"Women in Tech: The Facts","author":"Ashcraft Catherine","year":"2016","unstructured":"Catherine Ashcraft, Brad McLain, and Elizabeth Eger. 2016. Women in Tech: The Facts. National Center for Women & Technology (NCWIT), Colorado, CO, USA."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.176"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.4000\/books.aaccademia.6747"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.17723\/0360-9081-81.1.65"},{"key":"e_1_3_2_17_2","unstructured":"Shiladitya Bhattacharya Siddharth Singh Ritesh Kumar Akanksha Bansal Akash Bhagat Yogesh Dawer Bornini Lahiri and Atul Kr. Ojha. 2020. Developing a Multilingual Annotated Corpus of Misogyny and Aggression. In Proceedings of the Second Workshop on Trolling Aggression and Cyberbullying. European Language Resources Association (ELRA) Marseille France 158\u2013168."},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_3_2_19_2","first-page":"872","volume-title":"International Conference on Machine Learning","author":"Byrd Jonathon","year":"2019","unstructured":"Jonathon Byrd and Zachary Lipton. 2019. What is the effect of importance weighting in deep learning? In International Conference on Machine Learning. PMLR, 872\u2013881."},{"key":"e_1_3_2_20_2","first-page":"229","article-title":"Misogyny identification through SVM at IberEval","author":"Can\u00f3s Jose Sebasti\u00e1n","year":"2018","unstructured":"Jose Sebasti\u00e1n Can\u00f3s. 2018. Misogyny identification through SVM at IberEval. In Ibereval@ Sepln, 229\u2013233.","journal-title":"Ibereval@ Sepln"},{"issue":"2","key":"e_1_3_2_21_2","first-page":"76","article-title":"Sample size estimation using Yamane and Cochran and Krejcie and Morgan and green formulas and Cohen statistical power analysis by G* power and comparisons","volume":"10","author":"Chaokromthong Kajohnsak","year":"2021","unstructured":"Kajohnsak Chaokromthong and Nittaya Sintao. 2021. Sample size estimation using Yamane and Cochran and Krejcie and Morgan and green formulas and Cohen statistical power analysis by G* power and comparisons. Apheit International Journal 10, 2 (2021), 76\u201386.","journal-title":"Apheit International Journal"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.242"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.4324\/9780203195598"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1177\/001316446002000104"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies Vol. 1 4171\u20134186.","DOI":"10.18653\/v1\/N19-1423"},{"key":"e_1_3_2_27_2","unstructured":"DiDio Laura. 1997. Look out for techno-hazing. Computerworld 31 39 (1997) 72\u201373."},{"key":"e_1_3_2_28_2","unstructured":"Melanie Ehrenkranz. 2017. Women engineers get real about the worst sexism they\u2019ve experienced at work. Retrieved from https:\/\/www.mic.com\/articles\/181968\/women-engineers-get-real-about-the-worst-sexism-theyve-experienced-at-work"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"Nelly Elsayed Anthony S. Maida and Magdy Bayoumi. 2019. Deep gated recurrent and convolutional network hybrid model for univariate time series classification. International Journal of Advanced Computer Science and Applications 10 5 (2019).","DOI":"10.14569\/IJACSA.2019.0100582"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3292522.3326045"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11199-019-01095-z"},{"key":"e_1_3_2_32_2","first-page":"1","volume-title":"ACM on Human-Computer Interaction","volume":"5","author":"Ferreira Isabella","year":"2021","unstructured":"Isabella Ferreira, Jinghui Cheng, and Bram Adams. 2021. The \u201cshut the f** k up\u201d phenomenon: Characterizing incivility in open source code review discussions. In ACM on Human-Computer Interaction 5, CSCW2 (2021), 1\u201335."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2023.111935"},{"key":"e_1_3_2_34_2","first-page":"214","article-title":"Overview of the task on automatic misogyny identification at IberEval 2018","volume":"2150","author":"Fersini Elisabetta","year":"2018","unstructured":"Elisabetta Fersini, Paolo Rosso, and Maria Anzovino. 2018. Overview of the task on automatic misogyny identification at IberEval 2018. In Workshop on Evaluation of Human Language Technologies for Iberian Languages 2150 (2018), 214\u2013228.","journal-title":"Workshop on Evaluation of Human Language Technologies for Iberian Languages"},{"key":"e_1_3_2_35_2","first-page":"260","volume-title":"3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (Ibereval \u201918)","volume":"2150","author":"Frenda Simona","year":"2018","unstructured":"Simona Frenda and Ghanem Bilal. 2018. Exploration of misogyny in Spanish and English tweets. In 3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (Ibereval \u201918), Vol. 2150. Ceur Workshop Proceedings, 260\u2013267."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2019.00272"},{"key":"e_1_3_2_37_2","first-page":"249","article-title":"Automatic misogyny identification using neural networks","author":"Goenaga Iakes","year":"2018","unstructured":"Iakes Goenaga, Aitziber Atutxa, Koldo Gojenola, Arantza Casillas, Arantza D\u00edaz de Ilarraza, Nerea Ezeiza, Maite Oronoz, Alicia P\u00e9rez, and Olatz Perez-de Vi\u00f1aspre. 2018. Automatic misogyny identification using neural networks. In Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval@ SEPLN), 249\u2013254.","journal-title":"Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval@ SEPLN)"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.5555\/2487085.2487132"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/2568225.2568260"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2005.06.042"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.eacl-main.114"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.12.035"},{"key":"e_1_3_2_43_2","unstructured":"Paul B. Hanton. 2015. The Lack of Women in Technology: The Role Culture and Sexism Play. Master\u2019s thesis American Military University."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/2908131.2908183"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1080\/10304312.2014.924479"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-2902"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1052"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-016-9493-x"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.4324\/9780203800942"},{"key":"e_1_3_2_50_2","first-page":"45","article-title":"Scikit-learn","author":"Kramer Oliver","year":"2016","unstructured":"Oliver Kramer and Oliver Kramer. 2016. Scikit-learn. In Machine Learning for Evolution Strategies. Springer International Publishing, 45\u201353.","journal-title":"Machine Learning for Evolution Strategies."},{"key":"e_1_3_2_51_2","unstructured":"Klaus Krippendorff. 2011. Computing Krippendorff\u2019s alpha-reliability. Computing 1 25 (2011)."},{"key":"e_1_3_2_52_2","first-page":"1","volume-title":"1st Workshop on Trolling, Aggression and Cyberbullying (TRACn \u201918)","author":"Kumar Ritesh","year":"2018","unstructured":"Ritesh Kumar, Atul Kr Ojha, Shervin Malmasi, and Marcos Zampieri. 2018. Benchmarking aggression identification in social media. In 1st Workshop on Trolling, Aggression and Cyberbullying (TRACn \u201918). Association for Computational Linguistics, 1\u201311."},{"key":"e_1_3_2_53_2","unstructured":"Keita Kurita Anna Belova and Antonios Anastasopoulos. 2020. Towards robust toxic content classification. arXiv: 1912.06872. Retrieved from https:\/\/arxiv.org\/abs\/1912.06872"},{"key":"e_1_3_2_54_2","unstructured":"Zhenzhong Lan Mingda Chen Sebastian Goodman Kevin Gimpel Piyush Sharma and Radu Soricut. 2019. Albert: A lite bert for self-supervised learning of language representations. arXiv:1909.11942. Retrieved from https:\/\/arxiv.org\/abs\/1909.11942"},{"key":"e_1_3_2_55_2","first-page":"268","article-title":"Identification and classification of misogynous tweets using multi-classifier fusion","author":"Liu Han","year":"2018","unstructured":"Han Liu, Fatima Chiroma, and Mihaela Cocea. 2018. Identification and classification of misogynous tweets using multi-classifier fusion. In Workshop on Evaluation of Human Language Technologies for Iberian Languages (Ibereval@ Sepln), 268\u2013273.","journal-title":"Workshop on Evaluation of Human Language Technologies for Iberian Languages (Ibereval@ Sepln)"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1177\/1461444815608807"},{"key":"e_1_3_2_57_2","first-page":"710","volume-title":"44th International Conference on Software Engineering","author":"Miller Courtney","year":"2022","unstructured":"Courtney Miller, Sophie Cohen, Daniel Klug, Bogdan Vasilescu, and Christian KaUstner. 2022. \u201cDid you miss my comment or what?\u201d Understanding toxicity in open source discussions. In 44th International Conference on Software Engineering, 710\u2013722."},{"key":"e_1_3_2_58_2","unstructured":"MSang. 2022. HatEval dataset. Retrieved April 23 2022 from https:\/\/github.com\/msang\/hateval\/blob\/master\/keyword_set.md"},{"key":"e_1_3_2_59_2","unstructured":"Hala Mulki and Bilal Ghanem. 2021. Let-Mi: An Arabic levantine twitter dataset for misogynistic language. In Proceedings of the Sixth Arabic Natural Language Processing Workshop 154\u2013163."},{"key":"e_1_3_2_60_2","first-page":"29","volume-title":"EVALITA Evaluation of NLP and Speech Tools for Italian","author":"Muti Arianna","year":"2020","unstructured":"Arianna Muti and Alberto Barron-Cede. 2020. UniBO@ AMI: A multi-class approach to misogyny and aggressiveness identification on twitter posts using AlBERTo. In EVALITA Evaluation of NLP and Speech Tools for Italian, 29."},{"key":"e_1_3_2_61_2","first-page":"274","article-title":"AMI at IberEval2018 automatic misogyny identification in Spanish and English tweets","author":"Nina-Alcocer Victor","year":"2018","unstructured":"Victor Nina-Alcocer. 2018. AMI at IberEval2018 automatic misogyny identification in Spanish and English tweets. In Workshop on Evaluation of Human Language Technologies for Iberian Languages (Ibereval@ Sepln), 274\u2013279.","journal-title":"Workshop on Evaluation of Human Language Technologies for Iberian Languages (Ibereval@ Sepln)"},{"key":"e_1_3_2_62_2","unstructured":"Stack Overflow. 2023. Developer Survey. Retrieved from https:\/\/survey.stackoverflow.co\/2022\/"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2020.102360"},{"key":"e_1_3_2_64_2","first-page":"234","volume-title":"3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval \u201918)","volume":"2150","author":"Pamungkas Endang Wahyu","year":"2018","unstructured":"Endang Wahyu Pamungkas, Alessandra Teresa Cignarella, Valerio Basile, and Viviana Patti. 2018. 14-ExLab@ UniTo for AMI at IberEval2018: Exploiting lexical knowledge for detecting misogyny in English and Spanish tweets. In 3rd Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval \u201918), Vol. 2150, CEURWS, 234\u2013241."},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/SANER.2019.8667987"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_67_2","first-page":"1","volume-title":"6th Italian Conference on Computational Linguistics (CLiC-It \u201919)","volume":"2481","author":"Polignano Marco","year":"2019","unstructured":"Marco Polignano, Pierpaolo Basile, Marco De Gemmis, Giovanni Semeraro, and Valerio Basile. 2019. Alberto: Italian BERT language understanding model for NLP challenging tasks based on tweets. In 6th Italian Conference on Computational Linguistics (CLiC-It \u201919), Vol. 2481, CEUR, 1\u20136."},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3377816.3381732"},{"key":"e_1_3_2_69_2","doi-asserted-by":"crossref","unstructured":"Nils Reimers and Iryna Gurevych. 2019. Sentence-bert: Sentence embeddings using Siamese bert-networks. arXiv:1908.10084. Retrieved from https:\/\/arxiv.org\/abs\/1908.10084","DOI":"10.18653\/v1\/D19-1410"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3042604"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/ESEM56168.2023.10304855"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/APSEC51365.2020.00030"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583562"},{"key":"e_1_3_2_74_2","volume-title":"Model Assisted Survey Sampling","author":"S\u00e4rndal Carl-Erik","year":"2003","unstructured":"Carl-Erik S\u00e4rndal, Bengt Swensson, and Jan Wretman. 2003. Model Assisted Survey Sampling. Springer Science & Business Media."},{"key":"e_1_3_2_75_2","volume-title":"Sociology: A Brief Introduction","author":"Schaefer Richard T.","year":"2011","unstructured":"Richard T. Schaefer and Bonnie Haaland. 2011. Sociology: A Brief Introduction. McGraw-Hill New York."},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1057\/9780230625099"},{"key":"e_1_3_2_77_2","first-page":"255","article-title":"Classifying misogynistic tweets using a blended model: The AMI shared task in IBEREVAL 2018","author":"Shushkevich Elena","year":"2018","unstructured":"Elena Shushkevich and John Cardiff. 2018. Classifying misogynistic tweets using a blended model: The AMI shared task in IBEREVAL 2018. In Workshop on Evaluation of Human Language Technologies for Iberian Languages (Ibereval@ Sepln), 255\u2013259.","journal-title":"Workshop on Evaluation of Human Language Technologies for Iberian Languages (Ibereval@ Sepln)"},{"issue":"3","key":"e_1_3_2_78_2","first-page":"98","article-title":"Motivated and capable but no space for error. The International Journal of Information","volume":"5","author":"Singh Vandana","year":"2021","unstructured":"Vandana Singh and Brice Bongiovanni. 2021. Motivated and capable but no space for error. The International Journal of Information Diversity, & Inclusion 5, 3 (2021), 98\u2013126.","journal-title":"Diversity, & Inclusion"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/HICSS.2015.623"},{"key":"e_1_3_2_80_2","doi-asserted-by":"publisher","DOI":"10.4103\/ipj.ipj_32_18"},{"key":"e_1_3_2_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3559515"},{"key":"e_1_3_2_82_2","unstructured":"Sayma Sultana London Ariel Cavaletto and Amiangshu Bosu. 2021. Identifying the prevalence of gender biases among the computing organizations. arXiv:2107.00212. Retrieved from https:\/\/arxiv.org\/abs\/2107.00212"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3475716.3484189"},{"key":"e_1_3_2_84_2","doi-asserted-by":"publisher","DOI":"10.1037\/0022-3514.68.2.199"},{"key":"e_1_3_2_85_2","unstructured":"7 Steps to Learn English. 2022. Gender of Nouns: Useful Masculine and Feminine List. Retrieved from https:\/\/7esl.com\/gender-of-nouns\/"},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","unstructured":"Bianca Trinkenreich Ricardo Britto Marco Aurelio Gerosa and Igor Steinmacher. 2022. An empirical investigation on the challenges faced by women in the software industry: A case study. In Proceedings of the 2022 ACM\/IEEE 44th International Conference on Software Engineering: Software Engineering in Society 24\u201335.","DOI":"10.1109\/ICSE-SEIS55304.2022.9793931"},{"key":"e_1_3_2_87_2","unstructured":"Trae Vassallo Ellen Levy Michele Madansky Hillary Mickell Bennett Porter and Monica Leas. 2017. Elephant in the Valley. Retrieved from https:\/\/www.elephantinthevalley.com\/"},{"key":"e_1_3_2_88_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W17-3012"},{"issue":"2","key":"e_1_3_2_89_2","first-page":"1","article-title":"Understanding games and the industry that produces them: A review of the edited volume the video game industry","volume":"2","author":"Young Chris J.","year":"2015","unstructured":"Chris J. Young. 2015. Understanding games and the industry that produces them: A review of the edited volume the video game industry. Journal of Games Criticism 2, 2 (2015), 1\u201316.","journal-title":"Journal of Games Criticism"},{"key":"e_1_3_2_90_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.acl-long.247"},{"key":"e_1_3_2_91_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME46990.2020.00017"},{"issue":"5","key":"e_1_3_2_92_2","first-page":"1017","article-title":"Oversampling method for imbalanced classification","volume":"34","author":"Zheng Zhuoyuan","year":"2015","unstructured":"Zhuoyuan Zheng, Yunpeng Cai, and Ye Li. 2015. Oversampling method for imbalanced classification. Computing and Informatics 34, 5 (2015), 1017\u20131037.","journal-title":"Computing and Informatics"}],"container-title":["ACM Transactions on Software Engineering and Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3757739","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T06:33:16Z","timestamp":1777098796000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3757739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":91,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5,31]]}},"alternative-id":["10.1145\/3757739"],"URL":"https:\/\/doi.org\/10.1145\/3757739","relation":{},"ISSN":["1049-331X","1557-7392"],"issn-type":[{"value":"1049-331X","type":"print"},{"value":"1557-7392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"2023-11-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}