e.g. mhealth
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Skip search results from other journals and go to results- 2 Journal of Medical Internet Research
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Many research projects have been affected by bots and impostor responders, often motivated by financial incentive [19,20].
The integrity of web-based research faces threats on several fronts; for example, identifying and removing bot, scam, or ineligible responses from datasets is an emerging priority [21,22]. TGD and detransition research, in particular, has been subject to scam responses and sabotage efforts [23-25].
J Med Internet Res 2025;27:e63252
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However, we could not be sure whether the high number of remaining participants were bots or actual participants who did not continue to the next part of the study, although we assumed they were bots.
Yet this scenario presented us with a unique opportunity to attempt to create a bot detection algorithm to distinguish bots from genuine participants in CS2 better using strategies previously recommended in the literature [18,22].
Interact J Med Res 2025;14:e60548
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In this approach, LLMs were configured to perform as a patient-education bot, a pretherapy screening bot, patient bots, and evaluator bots. The patient bots simulated distinct emotional personas—depressed, anxious, and frustrated—to test the adaptability and competency of the provider bots. The evaluator bots assessed the interactions based on predefined criteria. Results from the AI evaluations were cross-referenced with human expert reviews for accuracy and reliability (Figure 1).
JMIR Nursing 2025;8:e63058
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Examples of data fraud include bad actors, such as eligible individuals who submit surveys multiple times for multiple incentives, ineligible individuals who lie to meet eligibility criteria, and programmed bots. Since half of our sample was recruited on the web, we used extensive screening protocols, including (1) requiring the answering of youth-focused qualitative questions that require a typed response that would indicate residence, such as “What’s your favorite local restaurant?”
JMIR Res Protoc 2025;14:e63114
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Evaluating the Problem of Fraudulent Participants in Health Care Research: Multimethod Pilot Study
Fraudulent participants generally fall into 2 main categories: real humans who participate in a disingenuous manner and computer bots designed to impersonate human participants [18]. Both human and computer bots attempt to participate in research studies for which they are not qualified or attempt to participate multiple times in the same study [17,25]. The presence of fraudulent participants in research studies can lead to various detrimental outcomes.
JMIR Form Res 2024;8:e51530
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In this study, we aimed to examine the theoretical ability of bots to detect and respond to misinformation. In developing our methods, we found that by using natural language processing techniques, bots can learn differentiating terms such as “tanning,” “essential oils,” or “sun exposure.” These bots have the ability to post prefabricated responses to comments related to a variety of skin conditions.
JMIR Dermatol 2021;4(2):e20975
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Bots and Misinformation Spread on Social Media: Implications for COVID-19
We begin with a high-level survey of the current bot literature: how bots are defined, what technical features distinguish bots, and the detection of bots using machine learning methods. We also examine how bots spread information, including misinformation, and explore the potential consequences with respect to the COVID-19 pandemic. Finally, we analyze and present the extent to which known bots are publishing COVID-19–related content.
J Med Internet Res 2021;23(5):e26933
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Identifying Sentiment of Hookah-Related Posts on Twitter
However, Twitter has quickly become subject to third party manipulation where social bots, or computer algorithms designed to automatically produce content and engage with legitimate human accounts on Twitter, are created to influence discussions and promote specific ideas or products [12-14]. Social bots are meant to appear to be everyday individuals operating Twitter accounts that are complete with metadata (name, location, pithy quote) and a photo/image.
JMIR Public Health Surveill 2017;3(4):e74
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