تحقيق في الأفكار الانتحارية من وسائل التواصل الاجتماعي باستخدام طريقة التعلم الآلي

Heckler WF, Carvalho JV, Barbosa JLV. Machine learning for suicidal ideation identification: A systematic literature review. Comput Hum Behav. 2022 Mar; 128: 107095. https://doi.org/10.1016/j.chb.2021.107095.

Chakraborty K, Bhatia S, Bhattacharyya S, Platos J, Bag R, Hassanien AE. Sentiment analysis of COVID-19 tweets by deep learning classifiers—A study to show how popularity is affecting accuracy in social media. Appl Soft Comput. 2020 Dec; 97: 106754. https://doi.org/10.1016/j.asoc.2020.106754.

Bhat M, Qadri M, Beg N, Kundroo M, Ahanger N, Agarwal B. Sentiment analysis of social media response on the Covid19 outbreak. Brain Behav. 2020 Jul; 87: 136-7. https://doi.org/10.1016/j.bbi.2020.05.006.

Castillo-Sánchez G, Marques G, Dorronzoro E, Rivera-Romero O, Franco-Martín M, De la Torre-Díez I. Suicide risk assessment using machine learning and social networks: a scoping review. J Med Syst. 2020 Dec; 44(12). https://doi.org/10.1007/s10916-020-01669-5

Roy A, Nikolitch K, McGinn R, Jinah S, Klement W, Kaminsky ZA. A machine learning approach predicts future risk to suicidal ideation from social media data. npj Digit Med. 2020 May 26; 3(1). https://doi.org/10.1038/s41746-020-0287-6

National Crime Records Bureau 2021. Accidental deaths and suicides in India 2021. Chapter 2: Suicides. https://ncrb.gov.in/sites/default/files/ADSI-2021/adsi2021_Chapter-2-Suicides.pdf

Singh O. Startling suicide statistics in India: Time for urgent action. Indian J Psychiatry. 2022; 64(5): 431. https://doi.org/10.4103/indianjpsychiatry.indianjpsychiatry_665_22.

Rabani ST, Khan QR, Khanday AMUD. Detection of suicidal ideation on twitter using machine learning & ensemble approaches. Baghdad Sci J. 2020 Dec 1; 17(4): 1328. https://doi.org/10.21123/bsj.2020.17.4.1328.

Rabani ST, Khan QR, Khanday AMUD. Quantifying suicidal ideation on social media using machine learning: A critical review. Iraqi J Sci. 2021 Nov 30; 62(11): 4092-100. https://doi.org/10.24996/ijs.2021.62.11.29

AL-Jumaili AS. A hybrid method of linguistic and statistical features for arabic sentiment analysis. Baghdad Sci J. 2020 Mar 18; 17(1(Suppl.)): 0385. https://doi.org/10.21123/bsj.2020.17.1(Suppl.).0385.

Lumontod RZI. Seeing the invisible: Extracting signs of depression and suicidal ideation from college students’ writing using LIWC a computerized text analysis. Int J Res Stud Educ International Journal of Research Studies in Education, 2020 Jun 29; 9(4). https://doi.org/10.5861/ijrse.2020.5007.

Aldhyani THH, Alsubari SN, Alshebami AS, Alkahtani H, Ahmed ZAT. Detecting and analyzing suicidal ideation on social media using deep learning and machine learning models. IJERPH. 2022 Oct 3;19(19):12635; https://doi.org/10.3390/ijerph191912635.

Brown RC, Bendig E, Fischer T, Goldwich AD, Baumeister H, Plener PL. Can acute suicidality be predicted by Instagram data? Results from qualitative and quantitative language analyses. PLoS ONE. 2019 Sep 10;14(9):e0220623. https://doi.org/10.1371/journal.pone.0220623.

Pourmand A, Roberson J, Caggiula A, Monsalve N, Rahimi M, Torres-Llenza V. Social media and suicide: A review of technology based epidemiology and risk assessment. TELEMED E-HEALTH. 2019 Oct 1;25(10):880-8. https://doi.org/10.1089/tmj.2018.0203.

Sueki H. The association of suicide-related Twitter use with suicidal behavior: A cross-sectional study of young internet users in Japan. J Affect. 2015 Jan; 170: 155-60. https://doi.org/10.1016/j.jad.2014.08.047.

Hswen Y, Naslund JA, Brownstein JS, Hawkins JB. Monitoring online discussions about suicide among twitter users with schizophrenia: exploratory study. JMIR Ment Health. 2018 Dec 13; 5(4): e11483. https://mental.jmir.org/2018/4/e11483/

Lee S, Kwon Y. Twitter as a place where people meet to make suicide pacts. Public Health. 2018 Jun; 159: 21-6. https://doi.org/10.1016/j.puhe.2018.03.001.

Alvarez-Mon MA, Asunsolo del Barco A, Lahera G, Quintero J, Ferre F, Pereira-Sanchez V, et al. Increasing interest of mass communication media and the general public in the distribution of tweets about mental disorders: observational study. J Med Internet Res. 2018 May 28; 20(5): e205. https://doi.org/10.2196/jmir.9582.

Calear AL, Batterham PJ. Suicidal ideation disclosure: patterns, correlates and outcome. Psychiatry Res. 2019 Aug; 278: 1-6. https://doi.org/10.1016/j.psychres.2019.05.024.

Franco-Martín MA, Muñoz-Sánchez JL, Sainz-de-Abajo B, Castillo-Sánchez G, Hamrioui S, Torre-Díez I. A systematic literature review of technologies for suicidal behavior prevention. J Med Syst. 2018 Apr; 42(4). https://doi.org/10.1007/s10916-018-0926-5

Braithwaite SR, Giraud-Carrier C, West J, Barnes MD, Hanson CL. Validating machine learning algorithms for twitter data against established measures of suicidality. JMIR Mental Health. 2016 May 16; 3(2): e21. https://doi.org/10.2196/mental.4822.

Lekkas D, Klein RJ, Jacobson NC. Predicting acute suicidal ideation on Instagram using ensemble machine learning models. Internet Interv. 2021 Sep; 25: 100424. https://doi.org/10.1016/j.invent.2021.100424.

Price MN, Green AE. Association of gender identity acceptance with fewer suicide attempts among transgender and nonbinary youth. Transgender Health. 2023 Feb 1; 8(1): 56-63. https://doi.org/10.1089/trgh.2021.0079.

Mars B, Heron J, Biddle L, Donovan JL, Holley R, Piper M, et al. Exposure to, and searching for, information about suicide and self-harm on the Internet: Prevalence and predictors in a population based cohort of young adults. J Affect. 2015 Oct; 185: 239-45. https://doi.org/10.1016/j.jad.2015.06.001.

Alkahtani H, Aldhyani THH. Artificial intelligence algorithms for malware detection in android operated mobile devices. Sensors. 2022 Mar 15; 22(6): 2268. https://doi.org/10.3390/s22062268

Aladağ AE, Muderrisoglu S, Akbas NB, Zahmacioglu O, Bingol HO. Detecting suicidal ideation on forums: proof-of-concept study. J Med Internet Res. 2018 Jun 21; 20(6): e215. https://doi.org/10.2196/jmir.9840.