Suicide represents a public health issue that requires new preventive strategies. Therefore, this study analyzes differences in language use between a themed posts group (suicide and depression) and a random posts group (non-specific topics) from different social media platforms. In addition, the similarity of the texts of themed posts group with the set of phrases linked to suicide risk factors is analyzed. Texts were processed using the Linguistic Inquiry and Word Count (LIWC) software. A 95% bootstrap confidence interval (CI) was built for the difference in means for the resulting values per word category across groups. Significant differences in the use of language were observed between the themed post group and the random post group, whereas no differences were found between the themed post group and the set of phrases linked to suicide risk factors. These results can contribute to the development of risk-detection tools, which identify users at risk based on the analysis of language use embedded in social media. Such opportune detection would significantly increase the suicide prevention work. However, there is still a latent need for doing more research in this field, especially for the Mexican culture.
AllenN. B.NelsonB. W.BrentD.AuerbachR. P. (2019). Short-term prediction of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough?Journal of Affective Disorders, 250, 163–169. https://doi.org/10.1016/j.jad.2019.03.044
CoppersmithG.LearyR.CrutchleyP.FineA. (2018). Natural language of social media as screening for suicide risk. Biomedical Informatics Insights, 10, 1–11. https://doi.org/10.1177/1178222618792860
4.
CoppersmithG.LearyR.WhyneE.WoodT. (2015). Quantifying suicidal ideation via language usage on social media. In ResnikP.ResnikR.MitchelM. (Eds.), Proceedings of the workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 51–60). Association for Computational Linguistics. https://qntfy.com/static/papers/jsm2015.pdf
5.
CrystalD. (2004). Language and the internet. Cambridge University Press.
6.
EichstaedtJ. C.SmithR. J.MerchantR. M.UngarL. H.CrutchleyP.Preotiuc-PietroD.AschD.SchwartzH. A. (2018). Facebook language predicts depression in medical records. In FiskeS. T. (Ed.), Proceedings of the national academy of sciences (Vol. 115, pp. 11203–11208). Princeton University.
7.
Fernández-CabanaM.Jiménez-FélizJ.Alves-PérezM. A.MateosR.Gómez-ReinoI.García-CaballeroA. (2015). Linguistic analysis of suicide notes in Spain. European Journal of Psychiatry, 29(2), 145–155. https://doi.org/10.4321/S0213-61632015000200006
8.
FodehS.GouletJ.BrandtC.HamadaAl-T. (2017). Leveraging Twitter to better identify suicide risk. In FodehS.Stan RaicuD. (Eds.), Proceedings of the first workshop medical informatics and healthcare held with the 23rd SIGKDD Conference on knowledge discovery and data mining (Vol. 69, pp. 1–7). PMLR. http://proceedings.mlr.press/v69/fodeh17a/fodeh17a.pdf
9.
FranklinJ.RibeiroJ.BentleyK.HuangX.MusacchioM.ChangB.FoxK.KleimanE.JaroszewskiA.NockM. (2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin, 143(2), 187–232. https://doi.org/10.1037/bul0000084
10.
GlennC. R.KleimanE. M.ChaC. B.DemingC. A.FranklinJ. C.NockM. K. (2018). Understanding suicide risk within the Research Domain Criteria (RDoC) Framework: A meta-analytic review. Depression & Anxiety, 35(1), 65–88. https://doi.org/10.1002/da.22686
11.
González-FortezaC.JuárezC. E.MontejoL.OsegueraG.WagnerF. A.JiménezA. (2015). Ideación suicida y su asociación con drogas, depresión e impulsividad en una muestra representativa de estudiantes de secundaria del estado de Campeche, México. Acta Universitaria, 25(2), 29–34. https://doi.org/10.15174/au.2015.862
12.
GrantR. N.KucherD.LeónA. M.GemmellJ. F.RaicuD. S.FodehS. J. (2018). Automatic extraction of informal topics from online suicidal ideation. BMC Bioinformatics, 19, 211. https://doi.org/10.1186/s12859-018-2197-z
13.
HandelmanL. D.LesterD. (2007). The content of suicide notes from attempters to completers. The Journal of Crisis Intervention and Suicide Prevention, 28(2), 102–104. https://doi.org/10.1027/0227-5910.28.2.102
JashinskyJ.BurtonS. H.HansonC. L.WestJ.Giraud-CarrierC.BarnesM. D.ArgyleT. (2013). Tracking suicide risks factors through Twitter in the US. Crisis: The Journal of Crisis Intervention and Suicide Prevention, 35(1), 51–59. https://doi.org/10.1027/0227-5910/a000234
16.
JiS.PanS.Xue LiE. C.LongG.HuangZ. (2021). Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(1), 215–226. https://doi.org/10.1109/TCSS.2020.3021467
17.
JoinerT. E.PettitJ. W.WalkerR. L.VoelzZ. R.CruzJ.RuddM. D.LesterD. (2005). Perceived burdensomeness and suicidality: Two studies on the suicide notes of those attempting and those completing suicide. Journal of Social and Clinical Psychology, 21(5), 531–545. https://doi.org/10.1521/jscp.21.5.531.22624
18.
KimK.ChoiS.LeeJ.SeaJ. (2019). Differences in linguistic and psychological characteristics between suicide notes and diaries. The Journal of General Psychology, 146(4), 1–26. https://doi.org/10.1080/00221309.2019.1590304
19.
KlonskyE. D.QiuT.SafferB. Y. (2017). Recent advances in differentiating attempters from suicide ideators. Current Opinion in Psychiatry, 30(1), 15–20. https://doi.org/10.1097/YCO.0000000000000294
20.
LeisA.RonzanoF.MayerM. A.FurlongL. I.SanzF. (2019). Detecting signs of depression in tweets in Spanish: Behavioral and linguistic analysis. Journal of Medical Internet Research, 21(6), e14199. https://doi.org/10.2196/14199
21.
LesterD.HainesJ.WilliamsC. L. (2010). Content differences in suicide notes by sex, age, and method: A study of Australian suicide notes. Psychological Reports, 106(2), 475–476. https://doi.org/10.2466/pr0.106.2.475-476
22.
LiT. M.ChauM.YipP. S.WongP. (2014). Temporal and computerized psycholinguistic analysis of the blog of a Chinese adolescent suicide. Crisis: The Journal of Crisis Intervention and Suicide Prevention, 35(3), 1–8. https://doi.org/10.1027/0227-5910/a00024
23.
LitvinovaT. A.SeredinP. V.LitvinovaO. A.RomanchenkoO. V. (2017). Identification of suicidal tendencies of individuals based on the quantitative analysis of their internet texts. Computación y Sistemas, 21(2), 243–252. https://doi.org/10.13053/CyS-21-2-2721
24.
MaX.HancockJ.NaamanM. (2016). Anonymity, intimacy and self-disclosure in social media. In KayeJ.DruinA. (Eds.), Proceedings of the 2016 conference on human factors in computing systems (pp. 3857–3869). Association for Computing Machinery.
25.
ManagoA.TaylorT.GreenfieldP. (2012). Me and my 400 friends: The anatomy of college students’ facebook networks, their communication patterns, and wellbeing. Developmental Psychology, 48(2), 369–380. https://doi.org/10.1037/a0026338
26.
NockM. K.HwangI.SampsonN. A.KesslerR. C. (2010). Mental disorders, comorbidity and suicidal behavior: Results from the National Comorbidity Survey Replication. Molecular Psychiatry, 15(8), 868–876. https://doi.org/10.1038/mp.2009.29
27.
O’ConnorR. C. (2011). The integrated motivational-volitional model of suicidal behavior. Journal of Crisis Intervention and Suicide Prevention, 32(6), 295–298. https://doi.org/10.1027/0227-5910/a000120
28.
O’DeaB.WanS.BatterhamP. J.CalearA. I.ParisC.ChristensenH. (2015). Detecting suicidality on Twitter. Internet Interventions, 2(2), 183–188. https://doi.org/10.1016/j.invent.2015.03.005
29.
PandeyA. R.BistaB.DhunganaR. R.AryalK. K.ChaliseB.DhimalM. (2019). Factors associated with suicidal ideation and suicidal attempts among adolescent students in Nepal: Findings from Global School-based Students Health Survey. PLoS One, 14(4), 1–13. https://doi.org/10.1371/journal.Pone.0210383
30.
PennebakerJ. W.BoydR. L.JordanK.BlackburnK. (2015). The development and psychometric properties of LIWC2015. University of Texas at Austin.
31.
Ramírez-EsparzaN.PennebakerJ. W.AndreaF.SuriáR. (2007). La psicología del uso de las palabras: Un programa de computadora que analiza textos en español. Revista Mexicana de Psicología, 24(1), 85–99.
32.
RibeiroJ. D.HuangX.FoxK. R.FranklinJ. C. (2018). Depression and hopelessness as risk factors for suicide ideation, attempts and death: Meta-analysis of longitudinal studies. The British Journal of Psychiatry, 212(5), 279–286. https://doi.org/10.1192/bjp.2018.27
33.
SawhneyR.ManchadaP.MathurP.SinghR.ShahR. R. (2018). Exploring and learning suicidal ideation connotations on social media with deep learning. In BalahurA.MohammadS.HosteV.KlingerR. (Eds.), Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis (pp. 167–175). Association for Computational Linguistics.
34.
SierraG.Bel-EnguixG.Osornio-ArteagaA.Cabrera-MoraA.García-NietoL.BustosA.Romo-AnayaA. M.Silva-CuevasV. (2020). An exploration of personality traits detection in a Spanish Twitter corpus. In KokkinakisD.ForsK. L.ThemistocleousC.AntonssonM.EckerströmM. (Eds.), Proceedings of the LREC 2020 workshop on: Resources and processing of linguistic data from people with various forms of cognitive/psychiatric/developmental impairments (RaPID-3) (pp. 132–141). ELRA. https://lrec2020.lrec-conf.org/media/proceedings/Workshops/Books/RaPID3book.pdf
Suárez-ColoradoY.PalacioJ.Caballero-DomínguezC. C.Pineda-RoaC. A. (2019). Adaptación, validez de constructo y confiabilidad de la escala de riesgo suicida Plutchik en adolescentes colombianos. Revista Latinoamericana de Psicología, 51(3), 145–152. https://doi.org/10.14349/rlp.2019.v51.n3.1
37.
TausczikY. R.PennebakerJ. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177%2F0261927X09351676
Van OrdenK. A.WitteT. K.CukrowiczK. C.BraithwaiteS. R.SelbyE. A.JoinerT. E.Jr. (2010). The interpersonal theory of suicide. Psychological Review, 117(2), 575–600. https://doi.org/10.1037/a0018697
