I-Bias ku-Machine Learning Models for Depression Detection

Umbala we-Left Abstract futhi Introduction Ukucaciswa imiphumela Ukusabela & Ukusabela Ukuhlobisa I-escalating prevalence ye-mental health conditions, ikakhulukazi i-depression, inikeza isixazululo esikhulu se-global ye-health. I-social media platforms ziye ziye ziye ziye ziye zithunyelwe njengezithombe ezigcwele zebhizinisi lapho abantu zibonise izakhiwo zabo nezimo, ibonise ithuba elizodwa yokubonisa izimo ze-mental health ngokusebenzisa izindlela ezihlangene ze-computing. Imodeli ye-machine learning kanye ne-deep learning zithunyelwe ukuhlola le data eningi, engagcinwe ukuze zibonise izakhiwo ezihambayo ze-depression. Le nqakraza okuzenzakalelayo lithathwe ukuthuthukiswa kwe-depression ku-social media, ngokuvumelana ukuhlola Umhlahlandlela we-key findings Ukubuyekezwa kwethu kuboniswe izinzuzo eziningi eziyinhloko kanye nezinzuzo zokusebenza ezivela ku-powerability kanye nokuvamile kwe-machine learning kanye nemodeli ye-deep learning kule ndawo. Izinzuzo ze-sampling zihlanganisa ngenxa ye-dependence ephakeme kumadivayisi ezizodwa ze-social media, ikakhulukazi i-Twitter, okuyinto isetshenziselwa ku-63.8% kwezifundo. Ngaphezu kwalokho, amaningi izifundo zihlanganisa izinto ezihlangeneyo ze-English futhi abasebenzisi ezivela kumazwe ezizodwa, ikakhulukazi e-United States naseYurophu. Lezi zinzuzo zihlanganisa i-representativity yamafutha, njengokuba zihlanganisa ububanzi be-social media abasebenzisi Ukuhlolwa kwama-hyperparameter, i-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyperparameter ye-hyper Izinzuzo nezinzuzo ze-Review Ukubuyekezwa okuzenzakalelayo ngenxa yayo ephelele, ukuhlola i-bias emhlabeni wonke umjikelezo we-ML, kusuka ku-sampling kuya ku-reporting, ku-depression detection ku-social media. Ngaphandle kokunciphisa ukuhlaziywa kwama-aspekti ezizodwa, isibuyekezo inikeza ukubuyekeza okuhlobene kanjani i-bias ingatholakala ku-model validity. Enye amandla kuyinto ukuxhaswa kwe-methodological, ngokuvumelana nezimfuneko ezivamile nge-strateji yokufinyelela okuhlobene nezingcele zokufinyelela. Ukukhuthaza izifundo ezivunywe emva kwe-2010, inikeza imiphumela emisha ku-ML kanye ne-DL izicelo zokwelapha zemvelo. Ukusetshenziswa kwezindlela ezivamile ze-bias assessment, ikakhulukazi i-PROBAST, inikeza ukucindezeleka ngokuvamile ngokuvumelana ne-bias e-domes methodological eziyinhloko. Ngaphezu kwalokho, inqubo esifundeni se-data extraction ye-revision ivumela ukucindezeleka okuzenzakalelayo, okuvumela ukucindezeleka kwezimpendulo ezisebenzayo, njenge-diversifying data sources kanye nokuphucula ukucindezeleka. Nangona kunjalo, ukucubungula kunezinto. Ukucubungula kwebhizinisi olufanelekayo kanye nokunciphisa kuphela e-English kungabangela iziphumo ezinhle ezivela ku-research engaphandle kwe-English, okukhusela ukucubungula kweziphumo. Ukucubungula kwezifundo ezidlulile (kuye emva-2010) kungabangela ukuhlangabezana nezinsizakalo ezidlulayo ezidlulile, kanti ukuhlangabezana kwezifundo zezifundo zihlanganisa ukucubungula ngqo futhi zihlanganisa i-meta-analysis ephakeme. Ngaphezu kwalokho, ukucubungula ukucubungula kungabangela iziphumo ezinhle, futhi ukucubungula izifundo ze-grey kungabangela ukuthi izindlela ezidlulayo akufanele Lezi zincazelo zihlanganisa izindawo zokuphuculwa kwezifundo zayo zokuqala, njenge-enlarging database kanye nokuphuculwa kwezilimi, kuhlanganise i-grey literature, kanye nokuhlola i-meta-analysis lapho kungenziwa. Ngokuhambisana nezindawo zayo, izifundo zokusebenza zokusebenza zokusebenza zokusiza ukwandisa ukunambitheka kwezimodeli ze-ML yokusebenza kwe-mental health kanye nokunikezela ukuxhumana okuhlobene, okuhlobene, futhi kuhlobene emhlabeni jikelele. Imiphumela ye-Future Research Ukuze ukwandisa i-generalizability kanye ne-application of machine learning and deep learning models in depression detection on social media, ukuxhumana ne-biases eyenziwe ngempumelelo. Okokuqala, ukwandisa imithombo yebhizinisi eziningana ne-social media platforms futhi ukuhlanganisa izilimi ezingaphandle kwe-English kanye nezindawo ezingenalutho ngeke ukwandise i-representativity kanye ne-generalizability.. Ukwandisa izindlela ze-sampling kubalulekile. Ukuhlanganisa i-sampling esisekelwe ku-keyword ne-sampling ezisebenzayo kungasiza ukunciphisa i-bias ye-selection kanye nokufaka abasebenzisi abakwazi ukunciphisa izici ze-depression kodwa akuyona izimo ezithakazelisayo. Kwi-data preprocessing Ukuhlobisa i-hyperparameter ephelele futhi ephelele kuzo zonke amamodeli kubalulekile ukuqinisekisa ukuhlanganiswa okuhambisanayo nokuphucula ukusebenza kwama-model. Izimfuneko ezinhle ze-data partitioning, kuhlanganise ukusetshenziswa kwama-validation ne-test sets, kufanele isetshenziswe ukuze zikhuthaze i-overfitting kanye nokuthuthukiswa kwe-model generalizability. Lapho ukulawula amamodeli, abacwaningi kufanele zihlanganisa ama-metrics ezihambisana ne-class imbalance, njenge-precision, recall, i-F1 score, ne-AUROC. Lezi ama-metrics zihlanganisa ukusebenza kwama-model futhi zihlanganisa kakhulu ku-depression, lapho isigaba se-minority iyatholakala kakhulu. Imibuzo yokuqala Ukubuyekezwa okuzenzakalelayo kubaluleke izinzuzo eziphambili zokusebenza kwezifundo ezivamile ezivela ekubuyekezwa kwe-depression ngokusebenzisa i-analysis ye-social media ngokusebenzisa amamodeli ye-machine learning ne-deep learning. Ukuphendula lezi zinzuzo kubaluleke ekubuyekezwa ama-models eziningana, ezigcwele futhi ezahlukile ezokuthuthukiswa okwenziwe ngempumelelo ama-individual e-depression. Ucwaningo olandelayo kufanele lihlanganisa ukuthuthukiswa kwedatha, ukuphucula izindlela ze-sampling, ukuphucula izindlela ze-data preprocessing ne-model development, kanye nokusetshenziswa ama-evaluation metric ezisebenzayo ukuze kuqinisekiswe ukuhlolwa okuphilayo nokufanelekayo. Ngokusebenzisa lezi zindlela, abacwaningi angakwazi ukwandisa ukuthuthukiswa kwezindlela zokufunda ukwelashwa kwesifo se-mental ezingenalutho futhi ezisebenzayo phakathi kwezinhlayiyana kanye nezinhlangano ezahlukahlukene. Izinzuzo ezivamile zihlanganisa izindlela zokuphendula okusheshayo, ekugcineni ukwandisa imiphumela ye-mental health e-global. I-Appendix 1. I-Review Study on Machine Learning Models for Depression Detection on Social Media Ukungena ngemvume: Agarwal, A. K., Mittal, J., Tran, A., Umthengisi, R., & Guntuku, S. C. (2023). Ukuhlolwa kwe-Social Media Ukubuyekeza Imithi ye-Emergency Medicine' Emotional Well-Being I-COVID19. 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Ukuhlolwa kwe-depression ngokuvamile nge-multi-aspect features usebenzisa imodeli ye-hybrid yokufunda okuphumula ku-social media. I-World Wide Web, 25(1), 281-304. https://doi.org/10.1007/s11280-021-00992-2 Umbhali: (1) Yuchen Cao, Khoury College of Computer Science, North Eastern University; (2) Jianglai Dai, I-Department of EECS, I-University of California, Berkeley; (3) Zhongyan Wang, Center for Data Science, I-New York University; (4) Yeyubei Zhang, School of Engineering and Applied Science, University of Pennsylvania; (5) Xiaorui Shen, Khoury College of Computer Science, North Eastern University; (6) Yunchong Liu, School of Engineering and Applied Science, University of Pennsylvania; (7) Yexin Tian, Georgia Institute of Technology, College of Computing. Authors: (1) Yuchen Cao, Khoury College of Computer Science, North Eastern University; (2) Jianglai Dai, I-Department of EECS, I-University of California, Berkeley; (3) Zhongyan Wang, Center for Data Science, I-New York University; (4) Yeyubei Zhang, School of Engineering and Applied Science, University of Pennsylvania; (5) Xiaorui Shen, Khoury College of Computer Science, North Eastern University; (6) Yunchong Liu, School of Engineering and Applied Science, University of Pennsylvania; (7) Yexin Tian, Georgia Institute of Technology, College of Computing. Okuzenzakalelayo iyatholakala ku-archiv ngaphansi kwe-license CC BY 4.0. Okuzenzakalelayo iyatholakala ku-archiv ngaphansi kwe-license CC BY 4.0.