AIMC Topic: Social Media

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Natural Language Processing and Machine Learning Techniques for Analyzing Conversations About Nutritional Yeasts in the United States and France: Retrospective Social Media Listening Study.

JMIR infodemiology
BACKGROUND: Nutritional yeast, an inactive form of Saccharomyces cerevisiae, has recently become increasingly popular as a food supplement and healthy ingredient, especially among individuals following plant-based diets. It is valued for its health b...

Social media interaction and built environment effects on urban walking experience: A machine learning analysis of Shanghai Citywalk.

PloS one
In fast-paced urban environments, Citywalk has emerged as a key leisure activity for urban residents to alleviate stress and enhance emotional well-being. From the perspective of virtual-physical interaction, this study integrates social media data w...

Harnessing deep learning to monitor people's perceptions towards climate change on social media.

Scientific reports
Social media has become a popular stage for people's views over climate change. Monitoring how climate change is perceived on social media is relevant for informed decision-making. This work advances the way social media users' perceptions and reacti...

Trade-offs between machine learning and deep learning for mental illness detection on social media.

Scientific reports
Social media platforms provide valuable insights into mental health trends by capturing user-generated discussions on conditions such as depression, anxiety, and suicidal ideation. Machine learning (ML) and deep learning (DL) models have been increas...

Validating Emotion Analysis on Social Media Text for Detecting Psychological Distress: A Cross-Sectional Survey.

Issues in mental health nursing
This study investigates the relationship between self-reported psychological distress and emotions in social media posts, using a deep learning-based emotion analysis model. A cross-sectional design was used, collecting data from Instagram and Thread...

Stigmatisation of gambling disorder in social media: a tailored deep learning approach for YouTube comments.

Harm reduction journal
BACKGROUND: The stigmatisation of gamblers, particularly those with a gambling disorder, and self-stigmatisation are considered substantial barriers to seeking help and treatment. To develop effective strategies to reduce the stigma associated with g...

Large-Scale Deep Learning-Enabled Infodemiological Analysis of Substance Use Patterns on Social Media: Insights From the COVID-19 Pandemic.

JMIR infodemiology
BACKGROUND: The COVID-19 pandemic intensified the challenges associated with mental health and substance use (SU), with societal and economic upheavals leading to heightened stress and increased reliance on drugs as a coping mechanism. Centers for Di...

TriLex: A fusion approach for unsupervised sentiment analysis of short texts.

PloS one
In recent years, online customer reviews and social media platforms have significantly impacted individuals' daily lives. Despite the generally short nature of textual content on these platforms, they convey a wide range of user sentiments. However, ...

A comprehensive framework for multi-modal hate speech detection in social media using deep learning.

Scientific reports
As social media platforms evolve, hate speech increasingly manifests across multiple modalities, including text, images, audio, and video, challenging traditional detection systems focused on single modalities. Hence, this research proposes a novel M...

Summarizing Online Patient Conversations Using Generative Language Models: Experimental and Comparative Study.

JMIR medical informatics
BACKGROUND: Social media is acknowledged by regulatory bodies (eg, the Food and Drug Administration) as an important source of patient experience data to learn about patients' unmet needs, priorities, and preferences. However, current methods rely ei...