Short-Form Videos and Mental Health: A Knowledge-Guided Neural Topic Model

Published Online:https://doi.org/10.1287/isre.2024.1071

References

  • Abbasi A, Parsons J, Pant G, Sheng ORL, Sarker S (2024) Pathways for design research on artificial intelligence. Informs. Systems Res. 35(2):441–459.LinkGoogle Scholar
  • An M, Wang J, Li S, Zhou G (2020) Multimodal topic-enriched auxiliary learning for depression detection. Proc. 28th Internat. Conf. Comput. Linguistics (Barcelona, Spain (Online)) (International Committee on Computational Linguistics), 1078–1089.Google Scholar
  • Arsene O, Dumitrache I, Mihu I (2011) Medicine expert system dynamic Bayesian network and ontology based. Expert Systems Appl. 38(12):15253–15261.CrossrefGoogle Scholar
  • Beyari H (2023) The relationship between social media and the increase in mental health problems. Internat. J. Environment. Res. Public Health 20(3):2383.CrossrefGoogle Scholar
  • Blei DM, McAuliffe JD (2007) Supervised topic models. Proc. 21st Internat. Conf. Neural Inform. Processing Systems (NIPS’07) (Curran Associates Inc., Red Hook, NY), 121–128.Google Scholar
  • Blei DM, Kucukelbir A, McAuliffe JD (2017) Variational inference: A review for statisticians. J. Amer. Statist. Assoc. 112(518):859–877.CrossrefGoogle Scholar
  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3(January):993–1022.Google Scholar
  • Bloomberg (2023) TikTok’s algorithm keeps pushing suicide to vulnerable kids. (April 20), https://www.bloomberg.com/news/features/2023-04-20/tiktok-effects-on-mental-health-in-focus-after-teen-suicide.Google Scholar
  • Boers E, Afzali MH, Newton N, Conrod P (2019) Association of screen time and depression in adolescence. JAMA Pediatrics 173(9):853–859.CrossrefGoogle Scholar
  • Braghieri L, Levy R, Makarin A (2022) Social media and mental health. Amer. Econom. Rev. 112(11):3660–3693.CrossrefGoogle Scholar
  • ByteDance (2024) Community guidelines. Accessed May 4, 2025, https://www.tiktok.com/community-guidelines/en.Google Scholar
  • Cao Z, Li S, Liu Y, Li W, Ji H (2015) A novel neural topic model and its supervised extension. Proc. Twenty-Ninth AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA).Google Scholar
  • Card D, Tan C, Smith NA (2018) Neural models for documents with metadata. Proc. 56th Annual Meeting Assoc. Comput. Linguistics (Volume 1: Long Papers) (Association for Computational Linguistics, Stroudsburg, PA), 2031–2040.Google Scholar
  • Carpenter A (2023) Associations between TikTok use, mental health, and body image among college students. Honors theses, University of Mississippi, University.Google Scholar
  • Chai Y, Liu Y, Li W, Zhu B, Liu H, Jiang Y (2024) An interpretable wide and deep model for online disinformation detection. Expert Systems Appl. 237(B):121588.CrossrefGoogle Scholar
  • Chang Y-S, Hung W-C, Juang T-Y (2013) Depression diagnosis based on ontologies and Bayesian networks. 2013 IEEE Internat. Conf. Systems Man Cybernetics (Manchester, UK), 3452–3457.Google Scholar
  • Cheng JC, Chen AL (2022) Multimodal time-aware attention networks for depression detection. J. Intelligent Inform. Systems 59(2):319–339.CrossrefGoogle Scholar
  • Cheng H, Liu S, Sun W, Sun Q (2023) A neural topic modeling study integrating SBERT and data augmentation. Appl. Sci. 13(7):4595.CrossrefGoogle Scholar
  • DBSA (2024) The alliance insider: TikTok and youth mental health 2024. Accessed May 4, 2025, https://www.dbsalliance.org/education/newsletters/tiktok-and-youth-mental-health/.Google Scholar
  • Dieng AB, Ruiz FJ, Blei DM (2020) Topic modeling in embedding spaces. Trans. Assoc. Comput. Linguistics 8:439–453.CrossrefGoogle Scholar
  • Ghosh S, Anwar T (2021) Depression intensity estimation via social media: A deep learning approach. IEEE Trans. Comput. Soc. Systems 8(6):1465–1474.CrossrefGoogle Scholar
  • He L, Chan JC, Wang Z (2021) Automatic depression recognition using CNN with attention mechanism from videos. Neurocomputing 422(January):165–175.CrossrefGoogle Scholar
  • Jagarlamudi J, Daumé H III, Udupa R (2012) Incorporating lexical priors into topic models. Proc. 13th Conf. Eur. Chapter Assoc. Comput. Linguistics (EACL ’12) (Association for Computational Linguistics, Stroudsburg, PA), 204–213.Google Scholar
  • Jargon J (2023) TikTok feeds teens a diet of darkness. Wall Street Journal (May 13), https://www.wsj.com/tech/personal-tech/tiktok-feeds-teens-a-diet-of-darkness-8f350507?gaa_at=eafs&gaa_n=ASWzDAhEULzVQOjDqoxTtT-YczUWAFhCXH1YWsEsDrAbMa4oNcoTU0TaArkk2nVC-Ik%3D&gaa_ts=683f7853&gaa_sig=P6qHYvshKvAgGGafW5WPHKgLIUogPotQtaB-8842u20P5IEwWKypuUajeqc88u--z5F2EJ8ohrqMOjbzWt1ZUA%3D%3D.Google Scholar
  • Jiang Y, Wu Z, Tang J, Li Z, Xue X, Chang S (2018) Modeling multimodal clues in a hybrid deep learning framework for video classification. IEEE Trans. Multimedia 20(11):3137–3147.CrossrefGoogle Scholar
  • Jung H, Park H, Song T (2017) Ontology-based approach to social data sentiment analysis: Detection of adolescent depression signals. J. Medical Internet Res. 19(7):e259.CrossrefGoogle Scholar
  • Kim BR, Srinivasan K, Kong SH, Kim JH, Shin CS, Ram S (2023) ROLEX: A novel method for interpretable machine learning using robust local explanations. MIS Quart. 47(3):1303–1332.CrossrefGoogle Scholar
  • Li X, Zhang X, Zhu J, Mao W, Sun S, Wang Z, Xia C, et al. (2019) Depression recognition using machine learning methods with different feature generation strategies. Artificial Intelligence Medicine 99(August):101696.CrossrefGoogle Scholar
  • Lin Y, Gao X, Chu X, Wang Y, Zhao J, Chen C (2023) Enhancing neural topic model with multi-level supervisions from seed words. Findings Assoc. Comput. Linguistics: ACL 2023 (Association for Computational Linguistics, Toronto), 13361–13377.Google Scholar
  • LiveWorld (2023) Social channels gain credibility with healthcare practitioners. Accessed May 4, 2025, https://info.liveworld.com/hubfs/HCP-Social-Media-Pharma-Marketing-Survey-eBook-LiveWorld.pdf.Google Scholar
  • Logrieco G, Marchili MR, Roversi M, Villani A (2021) The paradox of TikTok anti-pro-anorexia videos: How social media can promote non-suicidal self-injury and anorexia. Internat. J. Environment. Res. Public Health 18(3):1041.CrossrefGoogle Scholar
  • McCashin D, Murphy CM (2023) Using TikTok for public and youth mental health: A systematic review and content analysis. Clinical Child Psych. Psychiatry 28(1):279–306.CrossrefGoogle Scholar
  • Milton A, Ajmani L, DeVito MA, Chancellor S (2023) “I see me here”: Mental health content, community, and algorithmic curation on TikTok. Proc. 2023 CHI Conf. Human Factors Comput. Systems (CHI ’23) (Association for Computing Machinery, New York), Article 480, 1–17.Google Scholar
  • Momeni E, Sageder G (2013) An empirical analysis of characteristics of useful comments in social media. Proc. 5th Annual ACM Web Sci. Conf. (WebSci ’13) (Association for Computing Machinery, New York), 258–261.Google Scholar
  • Momeni E, Cardie C, Ott M (2013) Properties, prediction, and prevalence of useful user-generated comments for descriptive annotation of social media objects. Proc. Internat. AAAI Conf. Web Social Media 7(1):390–399.CrossrefGoogle Scholar
  • NY Times (2024) Surgeon general calls for warning labels on social media platforms. (June 17), https://www.nytimes.com/2024/06/17/health/surgeon-general-social-media-warning-label.html.Google Scholar
  • Oberlo (2024) Tiktok users by country (2024). Accessed May 4, 2025, https://www.oberlo.com/statistics/tiktok-users-by-country.Google Scholar
  • Padmanabhan B, Sahoo N, Burton-Jones A (2022) Machine learning in information systems research. MIS Quart. 46(1):iii–xix.CrossrefGoogle Scholar
  • Pan D, Luo G, Zeng A, Zou C, Liang H, Wang J, Zhang T, et al. (2022) Adaptive 3DCNN-based interpretable ensemble model for early diagnosis of Alzheimer’s disease. IEEE Trans. Comput. Soc. Systems 11(1):247–266.CrossrefGoogle Scholar
  • Paul K (2022) What TikTok does to your mental health: ‘It’s embarrassing we know so little’. The Guardian (October 30), https://www.theguardian.com/technology/2022/oct/30/tiktok-mental-health-social-media.Google Scholar
  • Qu M (2022) The study on Tik Tok interactive modes and future interactive video strategy development. 2022 8th Internat. Conf. Humanities Social Sci. Res. (ICHSSR 2022) (Atlantis Press, Dordrecht, Netherlands), 1746–1750.Google Scholar
  • Ray A, Kumar S, Reddy R, Mukherjee P, Garg R (2019) Multi-level attention network using text, audio and video for depression prediction. Proc. 9th Internat. Audio/Visual Emotion Challenge Workshop (AVEC ’19) (Association for Computing Machinery, New York), 81–88.Google Scholar
  • Reed J (2021) Using NLP-based text mining to gather patient insights from social media at Roche. Accessed May 4, 2025, https://www.linguamatics.com/blog/using-nlp-based-text-mining-gather-patient-insights-social-media-roche.Google Scholar
  • Roberts ME, Stewart BM, Airoldi EM (2016) A model of text for experimentation in the social sciences. J. Amer. Statist. Assoc. 111(515):988–1003.CrossrefGoogle Scholar
  • Schlott R (2022) How TikTok has become a dangerous breeding ground for mental disorders. New York Post (March 12), https://nypost.com/2022/03/12/tiktok-has-become-a-dangerous-mental-disorder-breeding-ground/.Google Scholar
  • Smith B (2003) Blackwell Guide to the Philosophy of Computing and Information (Blackwell, Oxford, UK).Google Scholar
  • Social Shepherd (2025) 25 essential TikTok statistics you need to know in 2025. Accessed May 4, 2025, https://thesocialshepherd.com/blog/tiktok-statistics.Google Scholar
  • Srivastava A, Sutton C (2016) Autoencoding variational inference for topic models. Proc. Internat. Conf. Learn. Representations (OpenReview.net).Google Scholar
  • Statista (2023) TikTok effects on mental health and digital addiction concerns among users in the United States as of May 2023. Accessed May 4, 2025, https://www.statista.com/statistics/1409776/tiktom-us-opinions-mental-health-effects/#:∼:text=According%20to%20a%20survey%20conducted,result%20of%20using%20the%20app.Google Scholar
  • Statista (2024) Breakdown of Weibo users in China as of September 2022, by age group. Accessed May 4, 2025, https://www.statista.com/statistics/320940/china-sina-weibo-user-breakdown-by-age-group/.Google Scholar
  • Stevens K, Kegelmeyer P, Andrzejewski D, Buttler D (2012) Exploring topic coherence over many models and many topics. Proc. 2012 Joint Conf. Empirical Methods Natural Language Processing Comput. Natural Language Learn. (Association for Computational Linguistics, Stroudsburg, PA), 952–961.Google Scholar
  • Tadesse MM, Lin H, Xu B, Yang L (2019) Detection of depression-related posts in reddit social media forum. IEEE Access 7:44883–44893.CrossrefGoogle Scholar
  • Tian X, Bi X, Chen H (2023) How short-form video features influence addiction behavior? Empirical research from the opponent process theory perspective. Inform. Tech. People 36(1):387–408.CrossrefGoogle Scholar
  • Toto E, Tlachac ML, Rundensteiner EA (2021) AudiBERT: A deep transfer learning multimodal classification framework for depression screening. Proc. 30th ACM Internat. Conf. Inform. Knowledge Management (CIKM ’21) (Association for Computing Machinery, New York), 4145–4154.Google Scholar
  • Trotzek M, Koitka S, Friedrich CM (2018) Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. IEEE Trans. Knowledge Data Engrg. 32(3):588–601.CrossrefGoogle Scholar
  • Wang X, Yang Y (2020) Neural topic model with attention for supervised learning. Proc. Internat. Conf. Artificial Intelligence Statist. (PMLR, New York), 1147–1156.Google Scholar
  • Wang Y, Wang Z, Li C, Zhang Y, Wang H (2022) Online social network individual depression detection using a multitask heterogenous modality fusion approach. Inform. Sci. 609(September):727–749.CrossrefGoogle Scholar
  • Yaguara (2025) Short form video statistics of 2025 (usage & trends). Accessed May 4, 2025, https://www.yaguara.co/short-form-video-statistics/#:~:text=in%20current%20times.-,Short%20Form%20Video%20Statistics%3A%20Top%20Picks%20(2025),to%20use%20short%2Dform%20videos.Google Scholar
  • Yang Y, Zhang K, Fan Y (2023) SDTM: A supervised Bayesian deep topic model for text analytics. Inform. Systems Res. 34(1):137–156.LinkGoogle Scholar
  • Yang L, Jiang D, He L, Pei E, Oveneke MC, Sahli H (2016) Decision tree based depression classification from audio video and language information. Proc. 6th Internat. Workshop Audio/Visual Emotion Challenge (AVEC ’16) (Association for Computing Machinery, New York), 89–96.Google Scholar
  • Yang L, Sahli H, Xia X, Pei E, Oveneke MC, Jiang D (2017) Hybrid depression classification and estimation from audio video and text information. Proc. 7th Ann. Workshop Audio/Visual Emotion Challenge (AVEC ’17) (Association for Computing Machinery, New York), 45–51.Google Scholar
  • Yoon J, Kang C, Kim S, Han J (2022) D-vlog: Multimodal vlog data set for depression detection. Proc. AAAI Conf. Artificial Intelligence 36(11):12226–12234.CrossrefGoogle Scholar
  • Yu Y, Zhuang Y, Zhang J, Meng Y, Ratner A, Krishna R, Shen J, Zhang C (2023) Large language model as attributed training data generator: A tale of diversity and bias. Proc. 37th Internat. Conf. Neural Inform. Processing Systems (NIPS ’23) (Curran Associates Inc., Red Hook, NY), Article 2433, 55734–55784.Google Scholar
  • Zahra MF, Qazi TA, Ali AS, Hayat N, Ul Hassan T (2022) How TikTok addiction leads to mental health illness? Examining the mediating role of academic performance using structural equation modeling. J. Positive School Psych. 6(10):1490–1502.Google Scholar
  • Zhai K, Boyd-Graber J (2013) Online latent Dirichlet allocation with infinite vocabulary. Proc. 30th Internat. Conf. Internat. Conf. Machine Learn. (ICML’13), vol. 28 (JMLR.org), I-561–I-569.Google Scholar
  • Zhang H, Chen B, Guo D, Zhou M (2018) WHAI: Weibull hybrid autoencoding inference for deep topic modeling. Proc. Internat. Conf. Learn. Representations (OpenReview.net).Google Scholar
  • Zhang D, Zhou L, Tao J, Zhu T, Gao G (2024) KETCH: A knowledge-enhanced transformer-based approach to suicidal ideation detection from social media content. Inform. Systems Res. 36(1):572–599.Google Scholar
  • Zhao H, Phung D, Huynh V, Jin Y, Du L, Buntine W (2021) Topic modelling meets deep neural networks: A survey. Proc. Internat. Joint Conf. Artificial Intelligence (OpenReview.net).Google Scholar
  • Zheng H, Kang B, Kim H (2007) An ontology-based Bayesian network approach for representing uncertainty in clinical practice guidelines. Proc. 3rd Internat. Conf. Uncertainty Reasoning Semantic Web (Busan, Republic of Korea), vol. 327 , 85–96.Google Scholar
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