Understanding Social Media Information Sharing in Individuals with Depression: Insights from the Elaboration Likelihood Model and Schema Activation Theory

References

• Pennycook G, Rand DG. The Psychology of Fake News. Trends Cognitive Sci. 2021;25(5):388–402. doi:10.1016/j.tics.2021.02.007
   PubMed Web of Science ®Google Scholar
• Cinelli M, De Francisci Morales G, Galeazzi A, Quattrociocchi W, Starnini M. The echo chamber effect on social media. Proc Natl Acad Sci. 2021;118(9):e2023301118. doi:10.1073/pnas.2023301118
   PubMedGoogle Scholar
• Gao Y, Liu F, Gao L. Echo chamber effects on short video platforms. Sci Rep. 2023;13(1):6282. doi:10.1038/s41598-023-33370-1
   PubMed Web of Science ®Google Scholar
• Scheibenzuber C, Neagu LM, Ruseti S, et al. Dialog in the echo chamber: fake news framing predicts emotion, argumentation and dialogic social knowledge building in subsequent online discussions. Computers in Human Behavior. 2023;140:107587. doi:10.1016/j.chb.2022.107587
   Web of Science ®Google Scholar
• Baumann F, Lorenz-Spreen P, Sokolov IM, Starnini M. Modeling Echo Chambers and Polarization Dynamics in Social Networks. Phys Rev Lett. 2020;124(4):048301. doi:10.1103/PhysRevLett.124.048301
   PubMed Web of Science ®Google Scholar
• Dubois E, Blank G. The echo chamber is overstated: the moderating effect of political interest and diverse media. Inform Comm Soc. 2018;21(5):729–745. doi:10.1080/1369118X.2018.1428656
   Web of Science ®Google Scholar
• De Biasio A, Monaro M, Oneto L, Ballan L, Navarin N. On the problem of recommendation for sensitive users and influential items: simultaneously maintaining interest and diversity. Knowledge-Based Syst. 2023;275:110699. doi:10.1016/j.knosys.2023.110699
   Web of Science ®Google Scholar
• Vos T, Lim SS, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–1222. doi:10.1016/S0140-6736(20)30925-9
   PubMed Web of Science ®Google Scholar
• Radovic A, Gmelin T, Stein BD, Miller E. Depressed adolescents’ positive and negative use of social media. Journal of Adolescence. 2017;55(1):5–15. doi:10.1016/j.adolescence.2016.12.002
   PubMedGoogle Scholar
• Zhu W, Mou J, Benyoucef M, Kim J, Hong T, Chen S. Understanding the relationship between social media use and depression: a review of the literature. Online Information Rev. 2023;47(6):1009–1035. doi:10.1108/OIR-04-2021-0211
   Web of Science ®Google Scholar
• Khaleel I, Wimmer BC, Peterson GM, et al. Health information overload among health consumers: a scoping review. Patient Educ Couns. 2020;103(1):15–32. doi:10.1016/j.pec.2019.08.008
   PubMed Web of Science ®Google Scholar
• Soroya SH, Farooq A, Mahmood K, Isoaho J, Zara SE. From information seeking to information avoidance: understanding the health information behavior during a global health crisis. Information Processing and Management. 2021;58(2):102440. doi:10.1016/j.ipm.2020.102440
   PubMed Web of Science ®Google Scholar
• Islam AKMN, Laato S, Talukder S, Sutinen E. Misinformation sharing and social media fatigue during COVID-19: an affordance and cognitive load perspective. Technol Forecasting Social Change. 2020;159:120201. doi:10.1016/j.techfore.2020.120201
   PubMed Web of Science ®Google Scholar
• Laato S, Islam AKMN, Islam MN, Whelan E. What drives unverified information sharing and cyberchondria during the COVID-19 pandemic?. Eur J Inf Syst. 2020;29(3):288–305. doi:10.1080/0960085X.2020.1770632
   Web of Science ®Google Scholar
• Bathina KC, ten Thij M, Lorenzo-Luaces L, Rutter LA, Bollen J. Individuals with depression express more distorted thinking on social media. Nat Hum Behav. 2021;5(4):458–466. doi:10.1038/s41562-021-01050-7
   PubMed Web of Science ®Google Scholar
• Yao X, Yu G, Tian X, Tang J. Patterns and Longitudinal Changes in Negative Emotions of People with Depression on Sina Weibo. Telemed E-Health. 2020;26(6):734–743. doi:10.1089/tmj.2019.0108
   PubMed Web of Science ®Google Scholar
• LeMoult J, Gotlib IH. Depression: a cognitive perspective. Clinic Psychol Rev. 2019;69:51–66. doi:10.1016/j.cpr.2018.06.008
   PubMed Web of Science ®Google Scholar
• Keller AS, Leikauf JE, Holt-Gosselin B, Staveland BR, Williams LM. Paying attention to attention in depression. Transl Psychiatry. 2019;9(1):1–12. doi:10.1038/s41398-019-0616-1
   PubMed Web of Science ®Google Scholar
• Piani MC, Maggioni E, Delvecchio G, Brambilla P. Sustained attention alterations in major depressive disorder: a review of fMRI studies employing Go/No-Go and CPT tasks. J Affective Disorders. 2022;303:98–113. doi:10.1016/j.jad.2022.02.003
   PubMed Web of Science ®Google Scholar
• Yoon S, Kleinman M, Mertz J, Brannick M. Is social network site usage related to depression? A meta-analysis of Facebook–depression relations. J Affective Disorders. 2019;248:65–72. doi:10.1016/j.jad.2019.01.026
   PubMed Web of Science ®Google Scholar
• Kreski N, Platt J, Rutherford C, et al. Social Media Use and Depressive Symptoms Among United States Adolescents. J Adolesc Health. 2021;68(3):572–579. doi:10.1016/j.jadohealth.2020.07.006
   PubMed Web of Science ®Google Scholar
• Boer M, Stevens GWJM, Finkenauer C, de Looze ME, van den Eijnden RJJM. Social media use intensity, social media use problems, and mental health among adolescents: investigating directionality and mediating processes. Computers in Human Behavior. 2021;116:106645. doi:10.1016/j.chb.2020.106645
   Web of Science ®Google Scholar
• Valkenburg PM, Van Driel II, Beyens I. The associations of active and passive social media use with well-being: a critical scoping review. New Med Soc. 2022;24(2):530–549. doi:10.1177/14614448211065425
   Web of Science ®Google Scholar
• Aubry R, Quiamzade A, Meier LL. Depressive symptoms and upward social comparisons during Instagram use: a vicious circle. Pers Individ Dif. 2024;217:112458. doi:10.1016/j.paid.2023.112458
   Web of Science ®Google Scholar
• Biglbauer S, Lauri Korajlija A. Do socially anxious and non-anxious individuals differ in their social media use?. Computers in Human Behavior. 2023;149:107970. doi:10.1016/j.chb.2023.107970
   Web of Science ®Google Scholar
• Kelley SW, Gillan CM. Using language in social media posts to study the network dynamics of depression longitudinally. Nat Commun. 2022;13(1):870. doi:10.1038/s41467-022-28513-3
   PubMed Web of Science ®Google Scholar
• Eichstaedt JC, Smith RJ, Merchant RM, et al. Facebook language predicts depression in medical records. Proc Natl Acad Sci. 2018;115(44):11203–11208. doi:10.1073/pnas.1802331115
   PubMed Web of Science ®Google Scholar
• Yang X, McEwen R, Ong LR, Zihayat M. A big data analytics framework for detecting user-level depression from social networks. International Journal of Information Management. 2020;54:102141. doi:10.1016/j.ijinfomgt.2020.102141
   Web of Science ®Google Scholar
• Yang K, Zhang T, Ananiadou S. A mental state Knowledge–aware and Contrastive Network for early stress and depression detection on social media. Information Processing and Management. 2022;59(4):102961. doi:10.1016/j.ipm.2022.102961
   Web of Science ®Google Scholar
• Cacheda F, Fernandez D, Novoa FJ, Carneiro V. Early Detection of Depression: social Network Analysis and Random Forest Techniques. J Med Internet Res. 2019;21(6):e12554. doi:10.2196/12554
   PubMed Web of Science ®Google Scholar
• Gori A, Topino E, Griffiths MD. The associations between attachment, self-esteem, fear of missing out, daily time expenditure, and problematic social media use: a path analysis model. Addict. Behav. 2023;141:107633. doi:10.1016/j.addbeh.2023.107633
   PubMed Web of Science ®Google Scholar
• Matz SC. Personal echo chambers: openness-to-experience is linked to higher levels of psychological interest diversity in large-scale behavioral data. J Personality Social Psychol. 2021;121(6):1284–1300. doi:10.1037/pspp0000324
   PubMed Web of Science ®Google Scholar
• Susmann MW, Xu M, Clark JK, et al. Persuasion amidst a pandemic: insights from the Elaboration Likelihood Model. Eur Rev Social Psychol. 2022;33(2):323–359. doi:10.1080/10463283.2021.1964744
   Web of Science ®Google Scholar
• Beck AT, Haigh EAP. Advances in Cognitive Theory and Therapy: the Generic Cognitive Model. Annu Rev Clin Psychol. 2014;10(1):1–24. doi:10.1146/annurev-clinpsy-032813-153734
   PubMedGoogle Scholar
• Dozois DJA, Beck AT. Cognitive Schemas, Beliefs and Assumptions. In: Risk Factors in Depression. Elsevier; 2008:119–143. doi:10.1016/B978-0-08-045078-0.00006-X
   Google Scholar
• Chancellor S, De Choudhury M. Methods in predictive techniques for mental health status on social media: a critical review. Npj Digit Med. 2020;3(1):1–11. doi:10.1038/s41746-020-0233-7
   PubMed Web of Science ®Google Scholar
• Adarsh V, Arun Kumar P, Lavanya V, Gangadharan GR. Fair and Explainable Depression Detection in Social Media. Information Processing and Management. 2023;60(1):103168. doi:10.1016/j.ipm.2022.103168
   Web of Science ®Google Scholar
• Zhang T, Yang K, Alhuzali H, Liu B, Ananiadou S. PHQ-aware depressive symptoms identification with similarity contrastive learning on social media. Information Processing and Management. 2023;60(5):103417. doi:10.1016/j.ipm.2023.103417
   Web of Science ®Google Scholar
• Rodriguez M, Aalbers G, McNally RJ. Idiographic Network Models of Social Media Use and Depression Symptoms. Cogn Ther Res. 2022;46(1):124–132. doi:10.1007/s10608-021-10236-2
   Web of Science ®Google Scholar
• Sun L. Social media usage and students’ social anxiety, loneliness and well-being: does digital mindfulness-based intervention effectively work?. BMC Psychology. 2023;11(1):362. doi:10.1186/s40359-023-01398-7
   PubMedGoogle Scholar
• Victor SA, Ibrahim MS, Yusuf S, et al. Social media addiction and depression among adolescents in two Malaysian states. Int J Adolesc Youth. 2024;29(1):2292055. doi:10.1080/02673843.2023.2292055
   Web of Science ®Google Scholar
• Zheng H, Goh DHL, Lee EWJ, Lee CS, Theng YL. Understanding the effects of message cues on COVID-19 information sharing on Twitter. J Assoc Inf Sci Technol. 2022;73(6):847–862. doi:10.1002/asi.24587
   PubMed Web of Science ®Google Scholar
• Evans JSBT, Stanovich KE. Dual-Process Theories of Higher Cognition: advancing the Debate. Perspect Psychol Sci. 2013;8(3):223–241. doi:10.1177/1745691612460685
   PubMedGoogle Scholar
• Shahab MH, Ghazali E, Mohtar M. The role of elaboration likelihood model in consumer behaviour research and its extension to new technologies: a review and future research agenda. Int J Consum Stud. 2021;45(4):664–689. doi:10.1111/ijcs.12658
   Web of Science ®Google Scholar
• Yang S, Zhou C, Chen Y. Do topic consistency and linguistic style similarity affect online review helpfulness? An elaboration likelihood model perspective. Information Processing and Management. 2021;58(3):102521. doi:10.1016/j.ipm.2021.102521
   Web of Science ®Google Scholar
• Ba Z, Zhao Y, Song S, Zhu Q. Does the involvement of charities matter? Exploring the impact of charities’ reputation and social capital on medical crowdfunding performance. Information Processing and Management. 2022;59(3):102942. doi:10.1016/j.ipm.2022.102942
   Web of Science ®Google Scholar
• Bhattacherjee S. Influence Processes for Information Technology Acceptance: an Elaboration Likelihood Model. MIS Quarterly. 2006;30(4):805. doi:10.2307/25148755
   Web of Science ®Google Scholar
• Dozois DJA. The importance of social connectedness: from interpersonal schemas in depression to relationship functioning and well-being. Canadian Psychol. 2021;62(2):174–180. doi:10.1037/cap0000253
   Web of Science ®Google Scholar
• Matsumoto N, Katahira K, Kawaguchi J. Cognitive Reactivity Amplifies the Activation and Development of Negative Self-schema: a Revised Mnemic Neglect Paradigm and Computational Modelling. Cogn Ther Res. 2023;47(1):38–51. doi:10.1007/s10608-022-10332-x
   Web of Science ®Google Scholar
• Gilboa A, Marlatte H. Neurobiology of Schemas and Schema-Mediated Memory. Trends Cognitive Sci. 2017;21(8):618–631. doi:10.1016/j.tics.2017.04.013
   PubMed Web of Science ®Google Scholar
• Jiwa M, Cooper PS, Chong TTJ, Bode S. Hedonism as a motive for information search: biased information-seeking leads to biased beliefs. Sci Rep. 2023;13(1):2086. doi:10.1038/s41598-023-29429-8
   PubMed Web of Science ®Google Scholar
• Auerbach RP, Stanton CH, Proudfit GH, Pizzagalli DA. Self-referential processing in depressed adolescents: a high-density event-related potential study. J Abnormal Psychol. 2015;124(2):233–245. doi:10.1037/abn0000023
   PubMed Web of Science ®Google Scholar
• Vieira C, Kuss DJ, Griffiths MD. Early maladaptive schemas and behavioural addictions: a systematic literature review. Clinic Psychol Rev. 2023;105:102340. doi:10.1016/j.cpr.2023.102340
   PubMed Web of Science ®Google Scholar
• Beck AT. The evolution of the cognitive model of depression and its neurobiological correlates. Am J Psychiatry. 2008;165(8):969–977. doi:10.1176/appi.ajp.2008.08050721
   PubMed Web of Science ®Google Scholar
• Disner SG, Beevers CG, Haigh EAP, Beck AT. Neural mechanisms of the cognitive model of depression. Nat Rev Neurosci. 2011;12(8):467–477. doi:10.1038/nrn3027
   PubMed Web of Science ®Google Scholar
• Zhang Y, Li X, Fan W. User Adoption of Physician’s Replies in an Online Health Community: an Empirical Study. Journal of the Association for Information Science and Technology. 2020;71(10):1179–1191. doi:10.1002/asi.24319
   Web of Science ®Google Scholar
• Westerman D, Spence PR, Van Der Heide B. Social Media as Information Source: recency of Updates and Credibility of Information*. J Comput-Mediated Commun. 2014;19(2):171–183. doi:10.1111/jcc4.12041
   Web of Science ®Google Scholar
• Lee EJ, Shin SY. Mediated Misinformation: questions Answered, More Questions to Ask. Am Behav Sci. 2021;65(2):259–276. doi:10.1177/0002764219869403
   Web of Science ®Google Scholar
• Srivastava V, Kalro AD. Enhancing the Helpfulness of Online Consumer Reviews: the Role of Latent (Content) Factors. Journal of Interactive Marketing. 2019;48:33–50. doi:10.1016/j.intmar.2018.12.003
   Web of Science ®Google Scholar
• Cyr D, Head M, Lim E, Stibe A. Using the elaboration likelihood model to examine online persuasion through website design. Inf Manage. 2018;55(7):807–821. doi:10.1016/j.im.2018.03.009
   Web of Science ®Google Scholar
• Chen Q, Min C, Zhang W, Wang G, Ma X, Evans R. Unpacking the black box: how to promote citizen engagement through government social media during the COVID-19 crisis. Computers in Human Behavior. 2020;110:106380. doi:10.1016/j.chb.2020.106380
   PubMed Web of Science ®Google Scholar
• Lerner JS, Li Y, Valdesolo P, Kassam KS. Emotion and Decision Making. Annual Review of Psychology. 2015;66(1):799–823. doi:10.1146/annurev-psych-010213-115043
   PubMed Web of Science ®Google Scholar
• Stieglitz S, Dang-Xuan L. Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior. J Manage Inf Syst. 2013;29(4):217–248. doi:10.2753/MIS0742-1222290408
   Web of Science ®Google Scholar
• Goldenberg A, Gross JJ. Digital Emotion Contagion. Trends Cognitive Sci. 2020;24(4):316–328. doi:10.1016/j.tics.2020.01.009
   PubMed Web of Science ®Google Scholar
• Brady WJ, Gantman AP, Van Bavel JJ. Attentional capture helps explain why moral and emotional content go viral. J Exp Psychol Gen. 2020;149(4):746–756. doi:10.1037/xge0000673
   PubMed Web of Science ®Google Scholar
• Robertson CE, Pröllochs N, Schwarzenegger K, Pärnamets P, Van Bavel JJ, Feuerriegel S. Negativity drives online news consumption. Nat Hum Behav. 2023;7(5):812–822. doi:10.1038/s41562-023-01538-4
   PubMed Web of Science ®Google Scholar
• Horner CG, Galletta D, Crawford J, Shirsat A. Emotions: the Unexplored Fuel of Fake News on Social Media. J Manage Inf Syst. 2021;38(4):1039–1066. doi:10.1080/07421222.2021.1990610
   Web of Science ®Google Scholar
• Waterloo SF, Baumgartner SE, Peter J, Valkenburg PM. Norms of online expressions of emotion: comparing Facebook, Twitter, Instagram, and WhatsApp. New Med Soc. 2018;20(5):1813–1831. doi:10.1177/1461444817707349
   PubMed Web of Science ®Google Scholar
• Tellis GJ, MacInnis DJ, Tirunillai S, Zhang Y. What Drives Virality (Sharing) of Online Digital Content? The Critical Role of Information, Emotion, and Brand Prominence. J Marketing. 2019;83(4):1–20. doi:10.1177/0022242919841034
   Web of Science ®Google Scholar
• Sui J, Humphreys GW. The Integrative Self: how Self-Reference Integrates Perception and Memory. Trends Cognitive Sci. 2015;19(12):719–728. doi:10.1016/j.tics.2015.08.015
   PubMed Web of Science ®Google Scholar
• Xu X, Liu J, Liu JH. The effect of social media environments on online emotional disclosure: tie strength, network size and self-reference. Online Information Rev. 2023. doi:10.1108/OIR-04-2022-0245
   Web of Science ®Google Scholar
• Zhang R, Zhang M, Sima J, Liu F, Zou F, Luo Y. Self-reference processing of fat-face and sick-face in individuals with different disgust sensitivity: evidence from behavioral and neuroelectrophysiology. Neuropsychologia. 2022;175:108368. doi:10.1016/j.neuropsychologia.2022.108368
   PubMed Web of Science ®Google Scholar
• Kim K, Banquer AM, Resnik SN, Johnson JD, Fernandez L. Self-reference and cognitive effort: source memory for affectively neutral information is impaired following negative compared to positive self-referential processing. J Cognitive Psychol. 2022;34(7):833–845. doi:10.1080/20445911.2022.2067553
   Web of Science ®Google Scholar
• Constable MD, Becker ML, Oh YI, Knoblich G. Affective compatibility with the self modulates the self-prioritisation effect. Cognition & Emotion. 2021;35(2):291–304. doi:10.1080/02699931.2020.1839383
   PubMed Web of Science ®Google Scholar
• Phua J, Kim J. Starring in your own Snapchat advertisement: influence of self-brand congruity, self-referencing and perceived humor on brand attitude and purchase intention of advertised brands. Telematic Informatic. 2018;35(5):1524–1533. doi:10.1016/j.tele.2018.03.020
   Web of Science ®Google Scholar
• Yang J, Jiang M. Demystifying congruence effects in Instagram in-feed native ads: the role of media-based and self-based congruence. J Res Interactive Marketing. 2021;15(4):685–708. doi:10.1108/JRIM-03-2020-0048
   Web of Science ®Google Scholar
• Kim DH, Yoo JJ, Lee WN. The influence of self-concept on ad effectiveness: interaction between self-concept and construal levels on effectiveness of advertising. J Marketing Commun. 2018;24(7):734–745. doi:10.1080/13527266.2016.1235601
   Google Scholar
• Ma E, Liu J, Li K. Exploring the mechanism of live streaming e-commerce anchors’ language appeals on users’ purchase intention. Front Psychol. 2023;14. doi:10.3389/fpsyg.2023.1109092
   Web of Science ®Google Scholar
• Dunlop SM, Wakefield M, Kashima Y. Pathways to Persuasion: cognitive and Experiential Responses to Health-Promoting Mass Media Messages. Commun Res. 2010;37(1):133–164. doi:10.1177/0093650209351912
   Web of Science ®Google Scholar
• Ku HH, Chen Y. Naming product colors with an individual’s identity and product evaluation: self-referencing as a mediator. J Product Brand Management. 2023;32(6):958–971. doi:10.1108/JPBM-12-2021-3791
   Web of Science ®Google Scholar
• Beck AT, Alford BA. Depression: Causes and Treatment. 2nd ed. University of Pennsylvania Press; 2009.
   Google Scholar
• Everaert J, Vrijsen JN, Martin-Willett R, Van De Kraats L, Joormann J. A meta-analytic review of the relationship between explicit memory bias and depression: depression features an explicit memory bias that persists beyond a depressive episode. Psychol Bull. 2022;148(5–6):435–463. doi:10.1037/bul0000367
   Web of Science ®Google Scholar
• Everaert J, Podina IR, Koster EHW. A comprehensive meta-analysis of interpretation biases in depression. Clinic Psychol Rev. 2017;58:33–48. doi:10.1016/j.cpr.2017.09.005
   PubMed Web of Science ®Google Scholar
• Würtz F, Kube T, Woud ML, Margraf J, Blackwell SE. Reduced Belief Updating in the Context of Depressive Symptoms: an Investigation of the Associations with Interpretation Biases and Self-Evaluation. Cogn Ther Res. 2024. doi:10.1007/s10608-023-10454-w
   PubMed Web of Science ®Google Scholar
• Chen L, Wang Q, Xu T. Working memory function in patients with major depression disorder: a narrative review. Clin Psychol Psychother. 2023;30(2):281–293. doi:10.1002/cpp.2811
   PubMed Web of Science ®Google Scholar
• Grahek I, Everaert J, Krebs RM, Koster EHW. Cognitive Control in Depression: toward Clinical Models Informed by Cognitive Neuroscience. Clin Psychol Sci. 2018;6(4):464–480. doi:10.1177/2167702618758969
   Web of Science ®Google Scholar
• Miller EK, Lundqvist M, Bastos AM. Working Memory 2.0. Neuron. 2018;100(2):463–475. doi:10.1016/j.neuron.2018.09.023
   PubMed Web of Science ®Google Scholar
• Gotlib IH, Joormann J. Cognition and depression: current status and future directions. Annu Rev Clin Psychol. 2010;6:285–312. doi:10.1146/annurev.clinpsy.121208.131305
   PubMed Web of Science ®Google Scholar
• Malhi GS, Mann JJ. Depression. Lancet. 2018;392(10161):2299–2312. doi:10.1016/S0140-6736(18)31948-2
   PubMed Web of Science ®Google Scholar
• Herrman H, Patel V, Kieling C, et al. Time for united action on depression: a Lancet–World Psychiatric Association Commission. Lancet. 2022;399(10328):957–1022. doi:10.1016/S0140-6736(21)02141-3
   PubMed Web of Science ®Google Scholar
• Zhang D, Xie H, He Z, Wei Z, Gu R. Impaired Working Memory Updating for Emotional Stimuli in Depressed Patients. Front Behav Neurosci. 2018;12:65. doi:10.3389/fnbeh.2018.00065
   PubMed Web of Science ®Google Scholar
• Hudson A, Wilson MJG, Green ES, Itier RJ, Henderson HA. Are you as important as me? Self-other discrimination within trait-adjective processing. Brain Cognition. 2020;142:105569. doi:10.1016/j.bandc.2020.105569
   PubMed Web of Science ®Google Scholar
• Alashoor T, Keil M, Smith HJ, McConnell AR. Too Tired and in Too Good of a Mood to Worry About Privacy: explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing. Inf Syst Res. 2023;34(4):1415–1436. doi:10.1287/isre.2022.1182
   Web of Science ®Google Scholar
• Pang H, Ruan Y. Determining influences of information irrelevance, information overload and communication overload on WeChat discontinuance intention: the moderating role of exhaustion. J Retailing Consum Serv. 2023;72:103289. doi:10.1016/j.jretconser.2023.103289
   Web of Science ®Google Scholar
• Zhu L, Li H, He W, Hong C. What influences online reviews’ perceived information quality? Perspectives on information richness, emotional polarity and product type. Electronic Library. 2020;38(2):273–296. doi:10.1108/EL-09-2019-0208
   Web of Science ®Google Scholar
• Lopes AI, Dens N, De Pelsmacker P, De Keyzer F. Which cues influence the perceived usefulness and credibility of an online review? A conjoint analysis. OIR. 2020;45(1):1–20. doi:10.1108/OIR-09-2019-0287
   Google Scholar
• Westerwick A. Effects of Sponsorship, Web Site Design, and Google Ranking on the Credibility of Online Information. J Comput Mediat Commun. 2013;18(2):80–97. doi:10.1111/jcc4.12006
   Web of Science ®Google Scholar
• Laros FJM, Steenkamp JBEM. Emotions in consumer behavior: a hierarchical approach. Journal of Business Research. 2005;58(10):1437–1445. doi:10.1016/j.jbusres.2003.09.013
   Web of Science ®Google Scholar
• Ahn J, Kim HK, Kahlor LA, Atkinson L, Noh GY. The Impact of Emotion and Government Trust on Individuals’ Risk Information Seeking and Avoidance during the COVID-19 Pandemic: a Cross-country Comparison. J Health Commun. 2021;26(10):728–741. doi:10.1080/10810730.2021.1999348
   PubMed Web of Science ®Google Scholar
• Negrão JG, Bazán PR, De Azevedo Neto RM, Lacerda SS, Ekman E, Kozasa EH. Baseline emotional state influences on the response to animated short films: a randomized online experiment. Front Psychol. 2022;13:1009429. doi:10.3389/fpsyg.2022.1009429
   PubMed Web of Science ®Google Scholar
• Zhang K, Zhang J, Yang J. The influence of human elements in photographs on tourists’ destination perceptions and intentions. Tourism Manage. 2023;95:104684. doi:10.1016/j.tourman.2022.104684
   Web of Science ®Google Scholar
• Zhang X, Zhang K, Li S, Koenitz D. Effects of store fixture shape at retail checkout: evidence from field and online studies. Prod Oper Manage. 2023;32(10):3158–3173. doi:10.1111/poms.14028
   Web of Science ®Google Scholar
• Chen L, Unsworth K, Zhang L, Zhang ZD. The curvilinear effect of negative affect on voice behavior from the perspective of activation theory. Curr Psychol. 2023;42(31):27497–27515. doi:10.1007/s12144-022-03853-x
   Web of Science ®Google Scholar
• Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175–191. doi:10.3758/BF03193146
   PubMed Web of Science ®Google Scholar
• Zhao H, Fu S, Chen X. Promoting users’ intention to share online health articles on social media: the role of confirmation bias. Information Processing and Management. 2020;57(6):102354. doi:10.1016/j.ipm.2020.102354
   PubMed Web of Science ®Google Scholar
• Chua AYK, Banerjee S. Intentions to trust and share online health rumors: an experiment with medical professionals. Computers in Human Behavior. 2018;87:1–9. doi:10.1016/j.chb.2018.05.021
   Web of Science ®Google Scholar
• Abbey JD, Meloy MG. Attention by design: using attention checks to detect inattentive respondents and improve data quality. J Oper Manage. 2017;53-56(1):63–70. doi:10.1016/j.jom.2017.06.001
   Google Scholar
• Burnkrant RE, Unnava HR. Effects of Self-Referencing on Persuasion. J Consum Res. 1995;22(1):17–26. doi:10.1086/209432
   Web of Science ®Google Scholar
• Escalas JE. Self-Referencing and Persuasion: narrative Transportation versus Analytical Elaboration. J Consum Res. 2007;33(4):421–429. doi:10.1086/510216
   Web of Science ®Google Scholar
• Furlanetto LM, Mendlowicz MV, Romildo Bueno J. The validity of the Beck Depression Inventory-Short Form as a screening and diagnostic instrument for moderate and severe depression in medical inpatients. J Affective Disorders. 2005;86(1):87–91. doi:10.1016/j.jad.2004.12.011
   PubMed Web of Science ®Google Scholar
• Wang YP, Gorenstein C. Psychometric properties of the Beck Depression Inventory-II: a comprehensive review. Braz J Psychiatry. 2013;35:416–431. doi:10.1590/1516-4446-2012-1048
   PubMed Web of Science ®Google Scholar
• Sultan S, Luminet O, Hartemann A. Cognitive and anxiety symptoms in screening for clinical depression in diabetes A systematic examination of diagnostic performances of the Hads and BDI-SF. J Affective Disorders. 2010;123(1):332–336. doi:10.1016/j.jad.2009.09.022
   PubMed Web of Science ®Google Scholar
• Hammen C. Risk Factors for Depression: an Autobiographical Review. Ann Rev Clin Psychol. 2018;14(1):1–28. doi:10.1146/annurev-clinpsy-050817-084811
   PubMedGoogle Scholar
• Albert K, Gau V, Taylor WD, Newhouse PA. Attention bias in older women with remitted depression is associated with enhanced amygdala activity and functional connectivity. J Affective Disorders. 2017;210:49–56. doi:10.1016/j.jad.2016.12.010
   PubMed Web of Science ®Google Scholar
• Yang W, Zhang JX, Ding Z, Xiao L. Attention Bias Modification Treatment for Adolescents With Major Depression: a Randomized Controlled Trial. J Am Acad Child Adolesc Psychiatry. 2016;55(3):208–218.e2. doi:10.1016/j.jaac.2015.12.005
   PubMed Web of Science ®Google Scholar
• Michikyan M. Depression symptoms and negative online disclosure among young adults in college: a mixed-methods approach. J Ment Health. 2020;29(4):392–400. doi:10.1080/09638237.2019.1581357
   PubMed Web of Science ®Google Scholar
• Brailovskaia J, Schillack H, Margraf J. Tell me why are you using social media (SM)! Relationship between reasons for use of SM, SM flow, daily stress, depression, anxiety, and addictive SM use – an exploratory investigation of young adults in Germany. Computers in Human Behavior. 2020;113:106511. doi:10.1016/j.chb.2020.106511
   Web of Science ®Google Scholar
• Feldhege J, Moessner M, Bauer S. Who says what? Content and participation characteristics in an online depression community. J Affective Disorders. 2020;263:521–527. doi:10.1016/j.jad.2019.11.007
   PubMed Web of Science ®Google Scholar
• Kramer ADI, Guillory JE, Hancock JT. Experimental evidence of massive-scale emotional contagion through social networks. Proc Natl Acad Sci. 2014;111(24):8788–8790. doi:10.1073/pnas.1320040111
   PubMed Web of Science ®Google Scholar
• Van Kleef GA, Côté S. The Social Effects of Emotions. Annual Review of Psychology. 2022;73(1):629–658. doi:10.1146/annurev-psych-020821-010855
   PubMedGoogle Scholar
• LeMoult J, Kircanski K, Prasad G, Gotlib IH. Negative Self-Referential Processing Predicts the Recurrence of Major Depressive Episodes. Clin Psychol Sci. 2017;5(1):174–181. doi:10.1177/2167702616654898
   PubMed Web of Science ®Google Scholar
• Renner F, Siep N, Lobbestael J, Arntz A, Peeters FPML, Huibers MJH. Neural correlates of self-referential processing and implicit self-associations in chronic depression. J Affective Disorders. 2015;186:40–47. doi:10.1016/j.jad.2015.07.008
   PubMed Web of Science ®Google Scholar
• Verduyn P, Van Mechelen I, Tuerlinckx F. The relation between event processing and the duration of emotional experience. Emotion. 2011;11(1):20–28. doi:10.1037/a0021239
   PubMed Web of Science ®Google Scholar
• Curci A, Rimé B. The temporal evolution of social sharing of emotions and its consequences on emotional recovery: a longitudinal study. Emotion. 2012;12(6):1404–1414. doi:10.1037/a0028651
   PubMed Web of Science ®Google Scholar
• Watkins ER, Roberts H. Reflecting on rumination: consequences, causes, mechanisms and treatment of rumination. Behaviour Research and Therapy. 2020;127:103573. doi:10.1016/j.brat.2020.103573
   PubMed Web of Science ®Google Scholar
• Shaw ZA, Hilt LM, Starr LR. The developmental origins of ruminative response style: an integrative review. Clinic Psychol Rev. 2019;74:101780. doi:10.1016/j.cpr.2019.101780
   PubMed Web of Science ®Google Scholar
• Spendelow JS, Simonds LM, Avery RE. The Relationship between Co-rumination and Internalizing Problems: a Systematic Review and Meta-analysis. Clin Psychol Psychother. 2017;24(2):512–527. doi:10.1002/cpp.2023
   PubMed Web of Science ®Google Scholar
• Bhat IH, Gupta S, Bhat GM. Effect of social media usage on major depressive disorder among generation Z: a study in Indian context. Information Discovery and Delivery. 2023. doi:10.1108/IDD-07-2022-0071
   Web of Science ®Google Scholar
• Huang L, Zhang J, Duan W, He L. Peer relationship increasing the risk of social media addiction among Chinese adolescents who have negative emotions. Curr Psychol. 2023;42(9):7673–7681. doi:10.1007/s12144-021-01997-w
   Web of Science ®Google Scholar
• Lăzăroiu G, Kovacova M, Siekelova A, Vrbka J. Addictive Behavior of Problematic Smartphone Users: the Relationship between Depression, Anxiety, and Stress. Rev Contemporary Philosophy. 2020;19:50–56. doi:10.22381/RCP1920204_
   Google Scholar
• Kliestik T, Scott J, Musa H, Suler P. Addictive Smartphone Behavior, Anxiety Symptom Severity, and Depressive Stress. Analysis Metaphysics. 2020;19:45–51. doi:10.22381/AM1920204
   Google Scholar
• Green M, Kovacova M, Valaskova K. Smartphone Addiction Risk, Depression Psychopathology, and Social Anxiety. Analysis Metaphysics. 2020;19:52–58. doi:10.22381/AM1920205
   Google Scholar
• Bowden-Green T, Hinds J, Joinson A. Understanding neuroticism and social media: a systematic review. Pers Individ Dif. 2021;168:110344. doi:10.1016/j.paid.2020.110344
   Web of Science ®Google Scholar
• Dalvi-Esfahani M, Niknafs A, Alaedini Z, Barati Ahmadabadi H, Kuss DJ, Ramayah T. Social Media Addiction and Empathy: moderating impact of personality traits among high school students. Telematic Informatic. 2021;57:101516. doi:10.1016/j.tele.2020.101516
   Web of Science ®Google Scholar
• Clak DA, Beck AT, Alford BA. Scientific Foundations of Cognitive Theory and Therapy of Depression. 1st ed. Wiley; 1999.
   Google Scholar
• Mahmoudi A, Jemielniak D, Ciechanowski L. Echo Chambers in Online Social Networks: a Systematic Literature Review. IEEE Access. 2024;12:9594–9620. doi:10.1109/ACCESS.2024.3353054
   Web of Science ®Google Scholar
• Diaz Ruiz C, Nilsson T. Disinformation and Echo Chambers: how Disinformation Circulates on Social Media Through Identity-Driven Controversies. J Public Policy Marketing. 2023;42(1):18–35. doi:10.1177/07439156221103852
   Web of Science ®Google Scholar
• Xing Y, Zhang JZ, Storey VC, Koohang A. Diving into the divide: a systematic review of cognitive bias-based polarization on social media. J Enterp Inf Manage. 2024;37(1):259–287. doi:10.1108/JEIM-09-2023-0459
   Web of Science ®Google Scholar
• Cuijpers P, Noma H, Karyotaki E, Cipriani A, Furukawa TA. Effectiveness and Acceptability of Cognitive Behavior Therapy Delivery Formats in Adults With Depression: a Network Meta-analysis. JAMA Psychiatry. 2019;76(7):700–707. doi:10.1001/jamapsychiatry.2019.0268
   PubMed Web of Science ®Google Scholar
• Moshe I, Terhorst Y, Philippi P, et al. Digital Interventions for the Treatment of Depression: a Meta-Analytic Review. Psychol Bull. 2021;147(8):749–786. doi:10.1037/bul0000334
   PubMed Web of Science ®Google Scholar
• Furukawa TA, Suganuma A, Ostinelli EG, et al. Dismantling, optimising, and personalising internet cognitive behavioural therapy for depression: a systematic review and component network meta-analysis using individual participant data. Lancet Psychiatry. 2021;8(6):500–511. doi:10.1016/S2215-0366(21)00077-8
   PubMed Web of Science ®Google Scholar
• Nahum-Shani I, Shaw SD, Carpenter SM, Murphy SA, Yoon C. Engagement in digital interventions. Am Psychologist. 2022;77(7):836–852. doi:10.1037/amp0000983
   PubMed Web of Science ®Google Scholar
• Karyotaki E, Efthimiou O, Miguel C, et al. Internet-Based Cognitive Behavioral Therapy for Depression: a Systematic Review and Individual Patient Data Network Meta-analysis. JAMA Psychiatry. 2021;78(4):361–371. doi:10.1001/jamapsychiatry.2020.4364
   PubMed Web of Science ®Google Scholar
• Kreuter MW, Skinner CS. Tailoring: what’s in a name?. Health Educ Res. 2000;15(1):1–4. doi:10.1093/her/15.1.1
   PubMed Web of Science ®Google Scholar