
 
 
 
 
 
 
 
 
 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. Umbala we-Left 
 
 
 
 
 Abstract futhi Introduction Ukucaciswa imiphumela Ukusabela & Ukusabela abstract Ukubuyekezwa kwehlabathi ye-depression ngokufanelekayo, idatha kufuneka izindlela ezintsha zokubuyekeza idatha yokubuyekeza ngokushesha. I-social media inikeza ithuba elizodwa yokubuyekeza i-depression ngokusebenzisa amaphepha asebenzayo. Lolu hlola okuhlolwa okuzenzakalelayo (ML) amamodeli yokubuyekezwa kwe-depression ku-social media, ngokuvumelana nezimo zokubuyekeza kanye nezimo zokubuyekeza zokusebenza emhlabeni wonke. A search ye-PubMed, i-IEEE Xplore, ne-Google Scholar inikeza izifundo ezithakazelisayo zithunyelwe emva kwe-2010. Lezi zifundo zokubuyekeza i-Risk Of Bias ASsessment Tool (PROBAST Ukuhlolwa I-Depression kanye nezinye izimo zempilo zempilo ziye ziye ziye ziye ziye zihlanganisa izimo zempilo zokusebenza emhlabeni wonke, zihlanganisa ezingaphezu kuka-264 million abantu emhlabeni wonke, ngokuvumelana World Health Organization (WHO, 2020). Ukukhula okuphuza izimo zempilo zempilo zihlanganisa ukweseka okusheshayo kwezinhlelo zokucwaninga ezisebenzayo kanye nezinhlangano zokusebenza. Ukuhlaziywa okusheshayo kwe-depression kungabangela ukwelashwa ngokushesha kanye neziphumela ezingcono, ekugcineni ukunciphisa umthwalo kwama-patients, ama-care providers, ne-healthcare systems (Kessler et al., 2017). Kwesikhathi se-digital, i-social media platforms njenge-Twitter, i-Facebook, ne-Reddit zihlanganisa isigaba esisodwa sokusebenza kwezinsuku ezingu-millions. Lezi zindlela akuvumela kuphela ukuxhumana, kodwa zihlanganisa njenge-public outlets lapho abantu zihlanganisa ngokuvumelana nezimonyo zabo, izimo, kanye nezimo zempilo (Choudhury et al., 2013). I-content eyenziwe ngamakhasimende kulezi zihlanganisa amathuba elizodwa yokufundisa isifo se-mental, okuvumela ukuhlaziywa kwe-time ye-linguistic and behavioral trends, futhi inikeza iziphakamiso okuyinto engaphansi (Guntuku et al., 2017). Izinzuzo ze-machine learning kanye ne-deep learning zithuthukisa kakhulu ikhono lokucubungula nokuhlola ama-datasets amakhulu. Lezi zenzakalo zihlanganisa kahle kakhulu ekusebenziseni idatha emangalisayo futhi emangalisayo ebonakalayo ezivela ku-social media, njengokuba zihlanganisa imizila futhi zihlanganisa ngokusekelwe ama-textual ne-behavioral clues. Lezi zokusebenza zenza izixhobo ezinhle zokucubungula ukwelashwa kwezilwanyana, okuvumela abacwaningi ukuthuthukisa amamodeli abakwazi ukuhlola abantu abacwaningi ngokuvumelana nezimo zabo ze-social media (Shatte et al., 2019). Ngokusebenzisa ama-algorithms abakwazi ukufundisa kusuka ku-textual ne-beha Umhlahlandlela we-Historical Studies on Machine Learning Approaches for Mental Health Detection in Social Media Uhlolo oluthile lwezenzululwazi lwezinto zokucwaninga zokucwaninga zokucwaninga yokucwaninga ngokusebenzisa ukucwaninga kwe-social media. Ama-algorithms ahlukahlukene - kusuka kuma-technology zokucwaninga zokucwaninga zokucwaninga zokuzonwabisa njenge-logistic regression ne-support vector machines kuya kwama-deep-learning amamodeli ezihlangene ne-assembly methods - ziye asetshenziswe ukuhlangabezana ama-user posts kanye nokuqondisa izimo ze-mental health ngokusekelwe izici ze-lingvistic ne-semantic (De Choudhury et al., 2013; Yazdavar et al., 2020). I-platforms afana ne-Twitter, i-Facebook, ne-Reddit asetshenziswe Enye yezindlela ezivamile ngaphakathi le ucwaningo kubandakanya ukuhlaziywa kwezinto, okuyinto kusiza ukucacisa umdlavuza emzimbeni eyenziwe kubasebenzisi. Ngokuvamile izibalo ezinhle, ezingenalutho, noma ezingenalutho ezibonakalayo emaphaketheni, abacwaningi zihlanganisa imibala yebhizinisi nomdlavuza (Kumar et al., 2020). Ngokwesibonelo, ukusetshenziswa okwandiswa kwe-first-person i-singular pronouns kanye nemibala ye-emoto emibi emibi kubandakanya imiphumela ye-depressive (Rude et al., 2004). Ngaphandle kweziphumo zayo zangaphambili, izinzuzo eziningi zihlala. Okokuqala, izifundo eziningi zihlala ukusabalalisa okunciphisa ngenxa yama-samples amancane noma amangqamuzana okungenani engabonakali. I-Data bias kuyinto ingxaki esikhulu, ebonakalayo ekubunjweni okungenani amaqembu amabili noma ama-communities amabhizinisi nangokungabonakali nabanye (Olteanu et al., 2019). Ngaphezu kwalokho, ukunciphisa izindlela ezinzima ze-sampling kanye ne-protokolls ezivamile zihlanganisa ukuzinza iziphumo. Thina, ukusetshenziswa okungenani kwe-machine learning amamodeli, kanye nokusetshenziswa okungenani kwe-nuances amabhizinisi asebenzayo, njenge-sarcasm noma Izinzuzo nezifundo zokufundisa kanye nezidingo zokufundisa Nangona izifundo ezivamile zithunyelwe izicelo zokufundisa umshini zokufundisa zokufundisa ukwelashwa kwesifo se-mental, kunezinto ezingenalutho ezingenalutho zokufundisa ukusebenza kwezimodeli zokufundisa umshini phakathi kwezifundo ezihlukahlukene. Njengoba zithunyelwe, izifundo ezivamile zihlukile ekuphenduleni izinzuzo eziyinhloko zokufundisa emhlabeni wonke zokufundisa umshini kanye nezicelo zokufundisa ezijwayelekile, kuhlanganise isampula, ukucutshungulwa kwedatha, ukwakhiwa kwama-model, nokuthuthukiswa (Johnson et al., 2019). Nakuba ama-biases kanye ne-limitations zithunyelwe ngaphakathi kwezifundo ezithile ezivamile kwama-auteurs, imiphumela yayo esikhulu kuzo Ukuze ukwelashwa lezi zincazelo, le nophando kusetshenziswe ukubuyekeza okuhlobene okuhlobisa kanye nokuhlola amamodeli asebenza ku-machine-learning ezisebenzayo yokubonisa isifo se-depression ku-social media. Izici ezizodwa zihlanganisa: I-Examining the effectiveness of machine learning and deep learning models by focusing on bias e-sampling, data preprocessing, model construction, fine-tuning, evaluation, and comparison, kanye nezimo ezihlangene ne-model generalizability phakathi kwezinhlobonhlobo ze-social media. 2. Ukuhlola izibuyekezo yezindlela, kuhlanganise izibuyekezo ezizodwa zokusebenza kwezilwanyana zezilwanyana, ezifana nokuphathwa kwezibuyekezo zezilwanyana lapho izibuyekezo zihlanganisa zihlanganisa izibuyekezo. Ngaphezu kwalokho, izibuyekezo ezininzi ezivamile ze-machine learning, ezifana nokuphuculwa kwezilwanyana zokuhlola amamodeli kanye nokuphuculwa kwezibuyekezo zebhizinisi ezinxulumene nezinhlangano ezisodwa zebhizinisi, zihlanganisa futhi. Kubalulekile ukholelwa ukuthi iningi lezi zibuyekezo zihlanganisa ngempumelelo, noma ngenxa yokufakelwa kwezibuyekezo ezisebenzayo zokusebenza kwe-machine learning ku-mental health detection 3. Ukunikezela imibuzo yokufundisa izilwanyana ukuze ukwandise ukucubungula kanye nokusetshenziswa kwe-machine learning amamodeli ekuthuthukiseni isifo se-mental. Lezi zibonakalo zihlanganisa izindlela zokuphucula izinzuzo zokuphendula ngokushesha futhi zihlanganisa ukuthuthukiswa kwezicelo ze-machine learning ezinzima, ezahlukile, ezahlukile futhi ezingenalutho. Ngokwenza lokhu, isibuyekezo kusiza ukunikezela isixazululo esithile esihlalweni esilandelayo, lapho ukunciphisa izixhumanisi ezivamile kunesiphumela ekuphenduleni kwe-biases engapheliyo. Ngokuhambisana nezidingo ezithakazelisayo, le nqakraza kusiza ukunikezela ukucaciswa okuqhubekayo kwezimfuneko ezivamile nezimfuneko ezivela ku-field. Iziphumo zihlanganisa ukuqondisa izinzuzo zokusebenza zokusungula izicelo ze-machine-learning ezinzima, ezahlukile nezimo zokusebenza zokusebenza kwezidakamizwa ze-mental health ngokusebenzisa idatha ze-social media. Kwi-section ezilandelayo, siya kuqala ukubuyekeza izindlela kanye nemodeli ezisetshenziselwa ngezifundo ezivela ku-analysis of common biases and limitations. We will conclude with a discussion on best practices and recommendations for advancing the field. 
 
 Okuzenzakalelayo iyatholakala ku-archiv ngaphansi kwe-license CC BY 4.0. Okuzenzakalelayo iyatholakala ku-archiv ngaphansi kwe-license CC BY 4.0.

Part of HackerNoon's growing list of open-source research papers, promoting free access to academic material.

MediaBias.TECH

We publish deeply researched (and often vastly underread) academic papers about our collective omnipresent media bias.

Ngaba ungenza lapho uneminyaka emangalisayo?

About Author

IMIBONO

HANG TAGS

LESI SIHLOKO SETHULWE NGAPHAKATHI

Related Stories

The Billionaires F*cked Up

Zulu Network: Moving The Bitcoin Economy Forward With a Two-Tiered Bitcoin Layer 2 Architecture

The Billionaires F*cked Up

Zulu Network: Moving The Bitcoin Economy Forward With a Two-Tiered Bitcoin Layer 2 Architecture

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps