The arts play a critical role in shaping public opinion and policy discourse around mental health. This study examined global public interest in mental health topics within the Arts & Entertainment (A&E) category of Google searches, focusing on temporal trends, inter-topic associations, and country-level clustering.
Methods:
Using a descriptive infodemiological design, monthly Relative Search Volumes (RSVs) for seven topics (2004–2025) were analyzed with time-series decomposition, ARIMA forecasting, correlation networks, and Latent Profile Analysis.
Results:
Results showed generally stable A&E search trends with COVID-19 spikes; searches for ADHD and anxiety increased, while schizophrenia declined. Significant seasonalities were found for ADHD, BD, MDD, schizophrenia, and mental health. Two A&E topic clusters emerged—(1) everyday mental health and (2) crisis-related concerns—with negative inter-cluster correlations. Country-level analyses revealed two A&E search clusters.
Conclusion:
A&E-focused infodemiology provides novel insights into cultural, temporal, and geographic patterns of arts-related mental health public interest and information seeking in the digital space.
AalK. (2024). Iran. In Influence of social media in a changing landscape of crisis: Insights into the digital dynamics of conflict and activism in the middle Eastern and North African Region (pp. 165–196). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-45517-0_9
2.
AhadA. A.Sanchez-GonzalezM.JunqueraP. (2023). Understanding and addressing mental health stigma across cultures for improving psychiatric care: A narrative review. Cureus, 15(5), e39549. https://doi.org/10.7759/cureus.39549
3.
AlibudbudR. (2022). Googling “mental health” after mental health legislation and during the COVID-19 pandemic: An infodemiological study of public interest in mental health in the Philippines. Journal of Mental Health, 31(4), 568–575. https://doi.org/10.1080/09638237.2022.2091757
4.
AlibudbudR. (2023a). An infodemiological study of worldwide google search volumes for major depressive disorder and persistent depressive disorder from 2004 to 2021. Clinical Epidemiology and Global Health, 19, Article 101211. https://doi.org/10.1016/j.cegh.2022.101211
5.
AlibudbudR. (2023b). Google Trends for health research: Its advantages, application, methodological considerations, and limitations in psychiatric and mental health infodemiology. Frontiers in Big Data, 6, Article 1132764. https://doi.org/10.3389/fdata.2023.1132764
6.
AlibudbudR.CleofasJ. V. (2022). Global utilization of online information for substance use disorder: An infodemiological study of Google and Wikipedia from 2004 to 2022. Journal of Nursing Scholarship, 55(3), 665–680. https://doi.org/10.1111/jnu.12844
7.
Anglada-TortM.LeeH.KrauseA. E.NorthA. C. (2023). Here comes the sun: Music features of popular songs reflect prevailing weather conditions. Royal Society Open Science, 10(5), Article 221443. https://doi.org/10.1098/rsos.221443
8.
AyersJ. W.AlthouseB. M.AllemJ.-P.RosenquistJ. N.FordD. E. (2013). Seasonality in seeking mental health information on google. American Journal of Preventive Medicine, 44(5), 520–525. https://doi.org/10.1016/j.amepre.2013.01.012
ChenC.ZhouW.CuiY.CaoK.ChenM.QuR.MiaoJ.QiJ.WuX.ChenJ.ZhangH.DaiA.FengQ.YangY.ZhouJ.DongN.YangS. (2025). Global, regional, and national characteristics of the main causes of increased disease burden due to the covid-19 pandemic: Time-series modelling analysis of global burden of disease study 2021. BMJ, 390, e083868. https://doi.org/10.1136/bmj-2024-083868
12.
DragiotiE.LiH.TsitsasG.LeeK. H.ChoiJ.KimJ.ChoiY. J.TsamakisK.EstradéA.AgorastosA.VancampfortD.TsiptsiosD.ThompsonT.MosinaA.VakadarisG.Fusar-PoliP.CarvalhoA. F.CorrellC. U.HanY. J.. . .SolmiM. (2022). A large-scale meta-analytic atlas of mental health problems prevalence during the COVID-19 early pandemic. Journal of Medical Virology, 94(5), 1935–1949. https://doi.org/10.1002/jmv.27549
13.
EdwardsM. (2023). Taking the “Psycho” out of “Psycho-Killer”: The impact of criminal portrayals of psychiatric disorders in horror film. Digital Literature Review, 10(1), 37–49. https://doi.org/10.33043/DLR.10.1.37-49
GBD 2019 Mental Disorders Collaborators. (2022). Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet Psychiatry, 9(2), 137–150. https://doi.org/10.1016/s2215-0366(21)00395-3
16.
GhaiS.Magis-WeinbergL.StoilovaM.LivingstoneS.OrbenA. (2022). Social media and adolescent well-being in the Global South. Current Opinion in Psychology, 46, Article 101318. https://doi.org/10.1016/j.copsyc.2022.101318
GrishchenkoN. (2024). Spatial-temporal evolution of digital skills in the EU countries. Telematics and Informatics, 94, Article 102185. https://doi.org/10.1016/j.tele.2024.102185
20.
GronholmP. C.ThornicroftG. (2022). Impact of celebrity disclosure on mental health-related stigma. Epidemiology and Psychiatric Sciences, 31(e62), e62. https://doi.org/10.1017/S2045796022000488
21.
HuddersL.De JansS. (2021). Gender effects in influencer marketing: An experimental study on the efficacy of endorsements by same- vs. other-gender social media influencers on Instagram. International Journal of Advertising, 41(1), 128–149. https://doi.org/10.1080/02650487.2021.1997455
22.
JohnsonM.OlsonC. J. (2021). Normalizing mental illness and neurodiversity in entertainment media: Quieting the madness. Routledge.
23.
JonesS. C.GordonC. S.MizziS. (2023). Representation of autism in fictional media: A systematic review of media content and its impact on viewer knowledge and understanding of autism. Autism, 27(8), 2205–2217. https://doi.org/10.1177/13623613231155770
KimM. S.WangS.KimS. (2023). Effects of online fan community interactions on well-being and sense of virtual community. Behavioral Sciences, 13(11), Article 897. https://doi.org/10.3390/bs13110897
26.
KlimiukK. B.KreftaD.KrawczykM.BalwickiŁ. (2024). Seasonal trends in suicide attempts-keywords related searches: A google trends analysis. Healthcare, 12(13), Article 1273. https://doi.org/10.3390/healthcare12131273
27.
LawrenceM.Homer-DixonT.JanzwoodS.RockstömJ.RennO.DongesJ. F. (2024). Global polycrisis: The causal mechanisms of crisis entanglement. Global Sustainability, 7, e6. https://doi.org/10.1017/sus.2024.1
28.
MavraganiA.OchoaG. (2019). Google trends in infodemiology and infoveillance: methodology framework. JMIR Public Health and Surveillance, 5(2), e13439. https://doi.org/10.2196/13439
29.
McNamaraC.BambraC. (2025). The global polycrisis and health inequalities. International Journal of Social Determinants of Health and Health Services, 55(3), 238–248. https://doi.org/10.1177/27551938251317472
30.
MerrickE.WeissmanJ. P.PatelS. J. (2022). Utilizing Google trends to monitor coronavirus vaccine interest and hesitancies. Vaccine, 40(30), 4057–4063. https://doi.org/10.1016/j.vaccine.2022.05.070
31.
MittmannG.SchrankB.Steiner-HofbauerV. (2024). A scoping review about the portrayal of depression and anxiety in mainstream and social media. International Journal of Psychology, 59(6), 1075–1083. https://doi.org/10.1002/ijop.13232
32.
NaslundJ. A.DengD. (2021). Addressing mental health stigma in low-income and middle-income countries: A new frontier for digital mental health. Ethics, Medicine, and Public Health, 19(100719), Article 100719. https://doi.org/10.1016/j.jemep.2021.100719
33.
NiederkrotenthalerT.KirchnerS.TillB.SinyorM.TranU. S.PirkisJ.SpittalM. J. (2021). Systematic review and meta-analyses of suicidal outcomes following fictional portrayals of suicide and suicide attempt in entertainment media. EClinicalMedicine, 36, Article 100922. https://doi.org/10.1016/j.eclinm.2021.100922
34.
OakleyK.O’BrienD. (2016). Learning to labour unequally: Understanding the relationship between cultural production, cultural consumption and inequality. Social Identities, 22(5), 471–486. https://doi.org/10.1080/13504630.2015.1128800
35.
OwenP. R. (2012). Portrayals of schizophrenia by entertainment media: A content analysis of contemporary movies. Psychiatric Services, 63(7), 655–659. https://doi.org/10.1176/appi.ps.201100371
36.
OyedemiT.ServaesJ. (2016). Social inequalities, media, and communication: Theory and roots. Bloomsbury Publishing PLC.
37.
PackerS. (2017). Mental illness in popular culture. Bloomsbury Publishing USA.
38.
PelegI.WozniukV. (2019). Patterns of censorship around the world. Routledge.
39.
RaiS.ShelatK.JainD.KishenA.ChoY. M.RedkarM.Hardikar-SawantS.UngarL.GuntukuS. C. (2025). Cross-cultural differences in mental health expressions on social media [Conference session]. Proceedings of the 3rd Workshop on Cross-Cultural Considerations in NLP (C3NLP 2025), 132–142.
40.
RobinsonL.SchulzJ.BlankG.RagneddaM.OnoH.HoganB.MeschG. S.CottenS. R.KretchmerS. B.HaleT. M.DrabowiczT.YanP.WellmanB.HarperM.-G.Quan-HaaseA.DunnH. S.CasilliA. A.TubaroP.CarvathR.. . .KhilnaniA. (2020). Digital inequalities 2.0: Legacy inequalities in the information age. First Monday, 25(7). https://doi.org/10.5210/fm.v25i7.10842
41.
RodriguezA. K.AkramS.ColversonA. J.HackG.GoldenT. L.SonkeJ. (2023). Arts engagement as a health behavior: An opportunity to address mental health inequities. Community Health Equity Research & Policy, 44(3), 315–322. https://doi.org/10.1177/2752535x231175072
42.
RovettaA.CastaldoL. (2022). A new infodemiological approach through Google Trends: Longitudinal analysis of COVID-19 scientific and infodemic names in Italy. BMC Medical Research Methodology, 22(1), Article 33. https://doi.org/10.1186/s12874-022-01523-x
43.
ShalbafanM.Askari-DiarjaniA.KiasalarM.SalehiS.ZarafshanH. (2025). Responsible portrayal of mental health issues in visual media. Iranian Journal of Psychiatry, 20(3), 286–288. https://doi.org/10.18502/ijps.v20i3.19036
44.
Terroso-SaenzF.SotoJ.MuñozA. (2023). Evolution of global music trends: An exploratory and predictive approach based on Spotify data. Entertainment Computing, 44, Article 100536. https://doi.org/10.1016/j.entcom.2022.100536
45.
ThakurN.CuiS.KhannaK.KnielingV.DuggalY. N.ShaoM. (2023). Investigation of the gender-specific discourse about online learning during COVID-19 on twitter using sentiment analysis, subjectivity analysis, and toxicity analysis. Computers, 12(11), Article 221. https://doi.org/10.3390/computers12110221
ZattoniF.GülM.SoligoM.MorlaccoA.MotterleG.CollavinoJ.BarneschiA. C.MoschiniM.MoroF. D. (2020). The impact of COVID-19 pandemic on pornography habits: A global analysis of Google Trends. International Journal of Impotence Research, 33(8), 824–831. https://doi.org/10.1038/s41443-020-00380-w
51.
ZayedB. A.TalaiaA. M.GaaboobahM. A.AmerS. M.MansourF. R. (2023). Google Trends as a predictive tool in the era of COVID-19: A scoping review. Postgraduate Medical Journal, 99(1175), 962–975. https://doi.org/10.1093/postmj/qgad012
52.
ZhangH.FirdausA. (2024). What does media say about mental health: A literature review of media coverage on mental health. Journalism and Media, 5(3), 967–979. https://doi.org/10.3390/journalmedia5030061
53.
ZhaoX.CoxeS. J.TimmonsA. C.FrazierS. L. (2021). Mental health information seeking online: A google trends analysis of ADHD. Administration and Policy in Mental Health and Mental Health Services Research, 49(3), 357–373. https://doi.org/10.1007/s10488-021-01168-w
