AIMC Topic: Social Media

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AI-Generated "Slop" in Online Biomedical Science Educational Videos: Mixed Methods Study of Prevalence, Characteristics, and Hazards to Learners and Teachers.

JMIR medical education
BACKGROUND: Video-sharing sites such as YouTube (Google) and TikTok (ByteDance) have become indispensable resources for learners and educators. The recent growth in generative artificial intelligence (AI) tools, however, has resulted in low-quality, ...

Enhancing sarcasm detection on social media: A comprehensive study using LLMs and BERT with multi-headed attention on SARC.

PloS one
Sarcasm detection in natural language processing (NLP) remains a complex challenge, especially in social media, where contextual clues are often subtle. This study addresses this challenge by leveraging transformer-based models, including BERT, GPT-3...

Identifying Stigma Phenotypes in Social Media Narratives of Substance Use: Observational Study.

Journal of medical Internet research
BACKGROUND: Individuals with substance use problems experience stigma in different contexts. Identifying characteristic situations in which stigma occurs or manifests-stigma phenotypes-can serve as important leverage points for future intervention.

Automated framework for multi-domain social media text analysis for business strategy employing multilayer perceptron with Word2Vec features and LIME XAI.

PloS one
Sentiment analysis is a pivotal domain in Natural Language Processing (NLP), particularly for understanding opinions expressed in sequential and textual data with the usage of machine learning. It involves identifying and categorizing emotions expres...

Towards better Hebrew clickbait detection: Insights from BERT and data augmentation.

PloS one
Clickbait headlines, designed to entice readers with sensationalized or misleading content, pose significant challenges in the digital landscape. They exploit curiosity to generate traffic and revenue, often at the cost of spreading misinformation an...

Sentiment analysis of cancer screening in Chinese social media: Qualitative studies based on machine learning.

PloS one
PURPOSE: Explore public perceptions and sentiments about cancer screening on social media. The dissemination of misinformation and negative attitudes continue to impede the access of many individuals with perceived risk to cancer screening services d...

Monitoring Opioid-Related Social Media Chatter Using Natural Language Processing and Large Language Models: Temporal Analysis.

JMIR infodemiology
BACKGROUND: Opioid overdose is a global public health emergency, with the United States experiencing high rates of morbidity and mortality due to prescription and illicit opioid use. Traditional public health monitoring systems often fail to provide ...

Enhanced audience sentiment analysis in IoT-integrated metaverse media communication.

PloS one
The convergence of Metaverse technologies, Internet of Things (IoT), and consumer electronics has given rise to an imperative need for scalable, real-time sentiment analysis that can process heterogeneous, high-velocity media flows. The traditional a...

From words to action? A scoping review on automatic sentiment analysis of patient experience comments from online sources and surveys.

BMJ health & care informatics
BACKGROUND: Automatic analysis of free-text patient comments enables the efficient processing of large feedback volumes, reducing reliance on manual review. A 2021 review examined natural language processing (NLP) and sentiment analysis (SA) in patie...

Assessing Large Language Models in Building a Structured Dataset From AskDocs Subreddit Data: Methodological Study.

Journal of medical Internet research
BACKGROUND: In an era marked by a growing reliance on digital platforms for health care consultation, the subreddit r/AskDocs has emerged as a pivotal forum. However, the vast, unstructured nature of forum data presents a formidable challenge; the ex...