magazine issue 9 2025 / Issue

Social media use towards self-diagnosing and health anxiety

• written by Lili R. Romann
• edited by Jana Dreston

Reviewers: Lea Sulprizio and an undisclosed reviewer.

Editorial Assistant: Zoey Chapman.

Self-diagnosing occurs when individuals who are not medical professionals intrinsically establish themselves as living with a particular medical diagnosis. Self-diagnosing is becoming all the more prominent due to increasing communication about health, symptoms, and diagnoses online, namely on algorithmically-dominated social media platforms. While this can be an empowering process for individuals suffering from health issues, it can also perpetuate health anxiety. Users should carefully review health information which they consume on social media and online.  

Figure 1.

You might have already engaged in self-diagnosing. Perhaps a friend or relational other has described their symptoms and the diagnosis they have received from a doctor. You compared their symptoms to yours and a lightbulb goes off – there’s a word, a diagnosis, for what you’ve been feeling.  

You type your symptoms into Google’s search bar. Via the results, you have now identified three chronic illnesses and/or diseases you could have. Better yet, you are on Instagram or TikTok. You felt funny this morning, or the past few weeks. Maybe you told someone about it – maybe you have not. Either way, the top of your ‘For You Page’ (FYP; TikTok’s algorithmically-dominated feed) is the answer – someone telling their diagnosis story, involving the very same symptoms you have been experiencing. You move to the comments. Suddenly, there are many others discussing the experience of these symptoms. In the present article, you can find how online health information has and will continue to impact everyday individuals. 

Proliferation of social media-related health content  

There is more health information available than ever before, mainly due to social media [1]. Online health information (whether via the internet and/or social media) can be powerful. Our access to online health information enhances agency and opportunities for ownership over our health through independent investigations of symptoms, diagnoses, and treatments. Considering the influx of health information available at any given moment, individuals might be engaging in higher levels of self-diagnosing but also experience more health anxiety as a result (i.e., heightened and persistent worry about health and illness [3]). This is especially true given that so much health information specific to individuals is promoted via their social media algorithms [2]. 

Social science scholarship has examined self-diagnosing on the social media platform TikTok in the context of different health conditions, such as ADHD [4] and autism [5]. Interestingly, within an examination of TikTok videos related to premenstrual dysphoric disorder, there was no difference in the number of videos posted by individuals with medical credentials (e.g., OB/GYN) and patients [6]. Thus, it appears there are no designated roles between doctor and patient on social media. No matter who you are, you might be suggesting health remedies. This, of course, can be quite enticing for viewers feeling unidentifiable symptoms.  

Figure 2.

Algorithmically-dominated social media spaces  

Central to platforms, such as TikTok [7] and Instagram, is the algorithm. All major social media platforms use algorithms, which determine how content is filtered, selected, ranked, and recommended to users through a set of rules, influenced by user and platform data [8]. Social media algorithms are considered “black box” [9], as their innerworkings are concealed from the public. Yet researchers do know they are built minding and fixated on every second a user lingers over or rewatches a piece of content. Many users are aware of this, too and even “train” their algorithms to display desired personalized content [10]. Rather than connecting with those in your network (e.g., friends and followers), these social media spaces are centered around an algorithmically-generated feed, specific to that platform. It was shown that social media algorithms lead users’ social media feeds to be closely specific to their previous experiences. They “excel at giving people niche content that they are interested in” [11].  

Previous research has examined how individuals interact with mental illness-related content on TikTok [2]. An analysis of almost 16,000 TikTok comments found that self-diagnosing was found to be a typical reaction – however not a universal one – to consuming health content. Users posted comments such as “I never thought I was bipolar until this video” and “WAIT SO I HAVE AN ED [ eating disorder]?”.  

The researchers also noted that users received videos related to their mental health experiences in a timely manner. They described that the experiences shared through various videos within their feed lined up very closely with the experiences of their day. For example, one user shared: “I was diagnosed today with bipolar disorder, fully convinced that I had (just) ADHD. But all these symptoms 100% match me. Thank you for sharing.” This suggests that health-related information may populate on users’ feeds, generated by algorithms, in a very timely manner. However, users are concerned that their feeds are suspiciously specific [12]. Thus, individuals who are suffering with their mental health might be especially prone to this kind of algorithmic exposure (i.e., exposure via their social media algorithm, not on their own volition or searching; [13]). It is imperative to acknowledge that individuals who are suffering vulnerable/sensitive experiences should be wary of what they are consuming on social media.  

Health anxiety & self-advocacy  

Importantly, medical professionals in the United States identify both positives and negatives about online health information-seeking. Doctors can quell patients’ anxiety related to online health information-seeking by pointing them towards trustworthy, scientific sources, or by asking them to curb their scrolling altogether. However, online health information-seeking and finding medical information online can be empowering for patients who are struggling or have endured dismissal by medical providers in the past [14]. 

Health care workers concern for online health information-seeking is made up of two concerns: the high levels of health misinformation disseminated on social media, as well as perpetuation of health anxiety [15]. However, this kind of health communication in social media spaces can also be very beneficial for users who are suffering from chronic illness [16] similar mental health conditions, especially as it can reduce stigma [17].  

Figure 3.

Conclusion 

In sum, self-diagnosing is a prominent practice which individuals should be aware of, especially as they encounter an influx of health information (both physical and mental) in their everyday lives from social media, the internet, peers, and beyond. While online health information-seeking can be a form of self-advocacy, it can also perpetuate health anxiety and potentially incorrect self-diagnosis, which is a worthy concern of social media researchers. Doing due-diligence by following up your health-related suspicions with multiple sources and medical/health professionals is a useful way to combat the potential negatives of online health information consumption, no matter how badly we want to trust that random user on TikTok. 

Bibliography 

[1] Chen, J., & Wang, Y. (2021). Social media use for health purposes: Systematic review. Journal of Medical Internet Research, 23(5), e17917. https://doi.org/10.2196/17917  

[2] Romann, L. R. & Oeldorf-Hirsch, A. (2025). Exploring algorithmic cultivation: Sensitive self-disclosure, self-diagnosis, and hazardous mental health communication on TikTok. Journal of Media Psychology. Advance online publication. https://doi.org/10.1027/1864-1105/a000474   

[3] American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). https://doi.org/10.1176/appi.books.9780890425596  

[4] Gilmore, R., Beezhold, J., Selwyn, V., Howard, R., Bartolome, I., & Henderson, N. (2022). Is TikTok increasing the number of self-diagnoses of ADHD in young people? European Psychiatry, 65(S1), S571-S571. https://doi.org/10.1192/j.eurpsy.2022.1463 

[5] Alper, M., Rauchberg, J. S., Simpson, E., Guberman, J., & Feinberg, S. (2025). TikTok as algorithmically mediated biographical illumination: Autism, self-discovery, and platformed diagnosis on# #autisktok. New Media & Society, 27(3), 1378-1396. https://doi.org/10.1177/14614448231193091 

[6] Romann, L. R. & Pfender, E. J. (2024). Disseminating premenstrual dysphoric disorder information on TikTok: A content analysis. Health Communication. Advance online publication. https://doi.org/10.1080/10410236.2024.2442685  

[7] Bhandari, A., & Bimo, S. (2022). Why’s everyone on TikTok now? The algorithmized self and the future of self-making on social media. Social Media + Society, 8(1). https://doi.org/10.1177/20563051221086241 

[8] Adisa, D. (2023, October 30). Everything you need to know about social media algorithms. Sprout Social. https://sproutsocial.com/insights/social-media-algorithms/  

[9] Hargittai, E., Gruber, J., Djukaric, T., Fuchs, J., & Brombach, L. (2020). Black box measures? How to study people’s algorithm skills. Information, Communication & Society, 23(5), 764–775. https://doi.org/10.1080/1369118X.2020.1713846 

[10] Simpson, E., Haumann, A., & Seamann, B. (2022). How to “tame” your algorithm: LGBTQ+ Interaction 6(GROUP), 1–27. https://doi.org/10.1145/3492841  

[11] Narayanan A. (2023). Understanding social media recommendation algorithms. Knight First Amendment Institute at Columbia University.  https://knightcolumbia.org/content/understanding-social-media-recommenda...

[12] Oeldorf-Hirsch, A., Romann, L. R., Witkowitch, I., & Chen, J. (2025). Exploring social media users’ experiences with algorithmic transparency cues. New Media & Society. Advance online publication. https://doi.org/10.1177/14614448251339493  

[13] Romann, L. R. & Oeldorf-Hirsch, A. (2025, June). Be your own advocate: Inquiring about user perceptions of trauma-informed design on social media. Extended abstract presented at the International Communication Association in Denver, CO. 

[14] van Uden-Kraan, C. F., Drossaert, C. H., Taal, E., Seydel, E. R., & van de Laar, M. A. (2009). Participation in online patient support groups endorses patients’ empowerment. Patient Education and Counseling, 74(1), 61–69. https://doi.org/10.1016/j.pec.2008.07.044  

[15] Wangler, J., & Jansky, M. (2020). General practitioners’ challenges and strategies in dealing with Internet-related health anxieties—results of a qualitative study among primary care physicians in Germany. Wiener Medizinische Wochenschrift, 170(13), 329–339. https://doi.org/10.1007/s10354-020-00777-8 

[16] Groenevelt, I., Haan, S. D., & Slatman, J. (2022). Doing bodies in YouTube videos about contested illnesses. Body & Society, 28(4), 28–52. https://doi.org/10.1177/1357034X221134436 

[17] Zhang, Z., Morgan, A., Armstrong, G., Campbell, A., & Reavley, N. (2024). Public mental illness disclosure on social media and the effects on stigma toward people with mental illness: A systematic review. Stigma and Health. Advance online publication. https://doi.org/10.1037/sah0000531  

Pictures 

Image 1:  Unsplash.com 

Image 2: Pexels.com 

Image 3: Pixabay.com