AIMC Topic: Social Media

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Novel 59-layer dense inception network for robust deepfake identification.

Scientific reports
The exponential growth of Artificial Intelligence (AI) has led to the emergence of cutting edge methods and a plethora of new tools for media editing. The use of these tools has also facilitated the spread of false information, propaganda, and harass...

Comparing traditional natural language processing and large language models for mental health status classification: a multi-model evaluation.

Scientific reports
The substantial increase in mental health disorders globally necessitates scalable, accurate tools for detecting and classifying these conditions in digital environments. This study addresses the critical challenge of automated mental health classifi...

Enhancing sarcasm detection in sentiment analysis for cyberspace safety using advanced deep learning techniques.

Scientific reports
Social media has become an integral part of daily life, with platforms like Twitter serving as popular outlets for users to share information and express grievances. While social media offers numerous benefits, it can also be misused for cyberbullyin...

Comparison of physician and large language model chatbot responses to online ear, nose, and throat inquiries.

Scientific reports
Large language models (LLMs) can potentially enhance the accessibility and quality of medical information. This study evaluates the reliability and quality of responses generated by ChatGPT-4, an LLM-driven chatbot, compared to those written by physi...

A fake news detection model using the integration of multimodal attention mechanism and residual convolutional network.

Scientific reports
To improve the accuracy and efficiency of fake news detection, this study proposes a deep learning model that integrates residual networks with attention mechanisms. Building on traditional convolutional neural networks, the model incorporates multi-...

An explainable RoBERTa approach to analyzing panic and anxiety sentiment in oral health education YouTube comments.

Scientific reports
Online videos are vital for health education and medical decision-making, but their comment sections often spread misinformation, causing anxiety and confusion. This study identifies stress-inducing comments in oral health education content, aiming t...

Online continuous learning of users suicidal risk on social media.

Artificial intelligence in medicine
Suicide is a tragedy for family and society. With social media becoming an integral part of people's life nowadays, assessing suicidal risk based on one's social media behavior has drawn increasing research attentions. The majority of the works train...

Sentiment Analysis Using a Large Language Model-Based Approach to Detect Opioids Mixed With Other Substances Via Social Media: Method Development and Validation.

JMIR infodemiology
BACKGROUND: The opioid crisis poses a significant health challenge in the United States, with increasing overdoses and death rates due to opioids mixed with other illicit substances. Various strategies have been developed by federal and local governm...

Navigating the Maze of Social Media Disinformation on Psychiatric Illness and Charting Paths to Reliable Information for Mental Health Professionals: Observational Study of TikTok Videos.

Journal of medical Internet research
BACKGROUND: Disinformation on social media can seriously affect mental health by spreading false information, increasing anxiety, stress, and confusion in vulnerable individuals, as well as perpetuating stigma. This flood of misleading content can un...

Assessing the accuracy and consistency of large language models in triaging social media posts for psychological distress.

Psychiatry research
Advances in artificial intelligence, particularly in natural language processing, offer promising tools for addressing mental health challenges in online contexts, potentially identifying at-risk individuals and informing timely interventions. This s...