AIMC Topic: Social Media

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[Digital innovations in vaccination communication].

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
Despite the significant success of vaccinations, increasing vaccine hesitancy poses a threat to public health, making effective vaccination communication essential. Both personalized, needs-based conversations between healthcare providers and patient...

Understanding the Engagement and Interaction of Superusers and Regular Users in UK Respiratory Online Health Communities: Deep Learning-Based Sentiment Analysis.

Journal of medical Internet research
BACKGROUND: Online health communities (OHCs) enable people with long-term conditions (LTCs) to exchange peer self-management experiential information, advice, and support. Engagement of "superusers," that is, highly active users, plays a key role in ...

Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models.

BMC medical informatics and decision making
BACKGROUND: Despite recent progress in misinformation detection methods, further investigation is required to develop more robust fact-checking models with particular consideration for the unique challenges of health information sharing. This study a...

Understanding Citizens' Response to Social Activities on Twitter in US Metropolises During the COVID-19 Recovery Phase Using a Fine-Tuned Large Language Model: Application of AI.

Journal of medical Internet research
BACKGROUND: The COVID-19 pandemic continues to hold an important place in the collective memory as of 2024. As of March 2024, >676 million cases, 6 million deaths, and 13 billion vaccine doses have been reported. It is crucial to evaluate sociopsycho...

Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media.

Journal of biomedical informatics
OBJECTIVE: Adverse event (AE) extraction following COVID-19 vaccines from text data is crucial for monitoring and analyzing the safety profiles of immunizations, identifying potential risks and ensuring the safe use of these products. Traditional dee...

Larger models yield better results? Streamlined severity classification of ADHD-related concerns using BERT-based knowledge distillation.

PloS one
This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural language processing (NLP) applications. After the model creation, we applied the resulting model, LastBER...

Assessing Familiarity, Usage Patterns, and Attitudes of Medical Students Toward ChatGPT and Other Chat-Based AI Apps in Medical Education: Cross-Sectional Questionnaire Study.

JMIR medical education
BACKGROUND: There has been a rise in the popularity of ChatGPT and other chat-based artificial intelligence (AI) apps in medical education. Despite data being available from other parts of the world, there is a significant lack of information on this...

Preliminary exploration of ChatGPT-4 shows the potential of generative artificial intelligence for culturally tailored, multilingual antimicrobial resistance awareness messaging.

American journal of veterinary research
OBJECTIVE: Antimicrobial resistance (AMR), a global threat driven by factors such as improper antimicrobial use in humans and animals, is projected to cause 10 million annual deaths by 2050. For behavior change, public health messages must be tailore...

Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study.

JMIR cancer
BACKGROUND: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information a...

From social media to artificial intelligence: improving research on digital harms in youth.

The Lancet. Child & adolescent health
In this Personal View, we critically evaluate the limitations and underlying challenges of existing research into the negative mental health consequences of internet-mediated technologies on young people. We argue that identifying and proactively add...