Generative artificial intelligence and machine learning methods to screen social media content.

Background: Social media research is confronted by the expansive and constantly evolving nature of social media data. Hashtags and keywords are frequently used to identify content related to a specific topic, but these search strategies often result in large numbers of irrelevant results. Therefore, methods are needed to quickly screen social media content based on a specific research question.

Vaping, Acculturation, and Social Media Use Among Mexican American College Students: Protocol for a Mixed Methods Web-Based Cohort Study.

Background: The tobacco industry has a history of targeting minority communities, including Hispanic individuals, by promoting vaping through social media. This marketing increases the risk of vaping among Hispanic young adults, including college students. In Texas, college enrollment among Mexican Americans has significantly increased over recent years.

Identification and Classification of Images in e-Cigarette-Related Content on TikTok: Unsupervised Machine Learning Image Clustering Approach.

Background: Previous studies identified e-cigarette content on popular video and image-based social media platforms such as TikTok. While machine learning approaches have been increasingly used with text-based social media data, image-based analysis such as image-clustering has been rarely used on TikTok. Image clustering can identify underlying patterns and structures across large sets of images, enabling more streamlined distillation and analysis of visual data on TikTok.

Examining hurricane-related social media topics longitudinally and at scale: A transformer-based approach.

Instead of turning to emergency phone systems, social media platforms, such as Twitter, have emerged as alternative and sometimes preferred venues for members of the public in the US to communicate during hurricanes and other natural disasters. However, relevant posts are likely to be missed by responders given the volume of content on platforms. Previous work successfully identified relevant posts through machine-learned methods, but depended on human annotators.

Problematic social media use and vaping among Mexican-American college students.

Introduction: Social media use and vaping nicotine are highly prevalent in the daily lives of young adults, especially among Mexican-American college students. The excessive and compulsive use of social media platforms, coupled with the urge to stay continuously connected, can lead to problematic social media use. To date, no studies have explored the impact of problematic social media use on the daily patterns of vaping among this vulnerable population.

The Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Study.

Background: Social media posts that portray vaping in positive social contexts shape people's perceptions and serve to normalize vaping. Despite restrictions on depicting or promoting controlled substances, vape-related content is easily accessible on TikTok. There is a need to understand strategies used in promoting vaping on TikTok, especially among susceptible youth audiences.

Identifying E-cigarette Content on TikTok: Using a BERTopic Modeling Approach.

Introduction: The use of hashtags is a common way to promote e-cigarette content on social media. Analysis of hashtags may provide insight into e-cigarette promotion on social media. However, the examination of text data is complicated by the voluminous amount of social media data.

Using Computer Vision to Detect E-cigarette Content in TikTok Videos.

Introduction: Previous research has identified abundant e-cigarette content on social media using primarily text-based approaches. However, frequently used social media platforms among youth, such as TikTok, contain primarily visual content, requiring the ability to detect e-cigarette-related content across large sets of videos and images. This study aims to use a computer vision technique to detect e-cigarette-related objects in TikTok videos.

Content Analysis of YouTube Videos Related to E-Cigarettes and COVID-19.

Background: E-cigarettes are frequently promoted on social media and portrayed in ways that are attractive to youth. While the COVID-19 pandemic significantly affected people's lives, less is known about how the pandemic influenced e-cigarette-related marketing and information on social media. This study examined how e-cigarettes were portrayed on youtube, one of the most popular social media platforms during the COVID-19 pandemic.

Influence of User Profile Attributes on e-Cigarette-Related Searches on YouTube: Machine Learning Clustering and Classification.

Background: The proliferation of e-cigarette content on YouTube is concerning because of its possible effect on youth use behaviors. YouTube has a personalized search and recommendation algorithm that derives attributes from a user's profile, such as age and sex. However, little is known about whether e-cigarette content is shown differently based on user characteristics.

COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic.

Background: The volume of COVID-19-related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning-based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment.

Content Analysis of YouTube Videos Related to E-cigarettes and COVID-19.

Introduction: E-cigarettes are frequently promoted on social media and portrayed in ways that are attractive to youth. While COVID-19 pandemic significantly affected people's lives, less known is how the pandemic influenced e-cigarette-related marketing and information on social media. This study identifies how e-cigarettes are portrayed during the COVID-19 pandemic on YouTube, one of the most popular social media platforms.

Understanding e-cigarette content and promotion on YouTube through machine learning.

Introduction: YouTube is a popular social media used by youth and has electronic cigarette (e-cigarette) content. We used machine learning to identify the content of e-cigarette videos, featured e-cigarette products, video uploaders, and marketing and sales of e-cigarette products.

Methods: We identified e-cigarette content using 18 search terms (eg, e-cig) using fictitious youth viewer profiles and predicted four models using the metadata as the input to supervised machine learning: (1) video themes, (2) featured e-cigarette products, (3) channel type (ie, video uploaders) and (4) discount/sales.

An Exploration of e-Cigarette-Related Search Items on YouTube: Network Analysis.

Background: e-Cigarette use among youth is high, which may be due in part to pro-e-cigarette content on social media such as YouTube. YouTube is also a valuable resource for learning about e-cigarette use, trends, marketing, and e-cigarette user perceptions. However, there is a lack of understanding on how similar e-cigarette-related search items result in similar or relatively mutually exclusive search results.

CoVerifi: A COVID-19 news verification system.

There is an abundance of misinformation, disinformation, and "fake news" related to COVID-19, leading the director-general of the World Health Organization to term this an 'infodemic'. Given the high volume of COVID-19 content on the Internet, many find it difficult to evaluate veracity. Vulnerable and marginalized groups are being misinformed and subject to high levels of stress.

Development of a health communication campaign to promote the Texas prescription monitoring program.

Given the role opioid overprescribing has played in the current overdose crisis, reducing the supply of prescription opioids available for misuse has gained widespread support. Prescription monitoring programs (PMPs) have been identified as a tool for achieving this goal, but little is known about how to promote PMP use to prescribers. This paper describes the process of developing a health communication campaign to support the adoption of the Texas PMP.

Social media processes in disasters: Implications of emergent technology use.

This article seeks to extend social science scholarship on social media technology use during disruptive events. Though social media's role in times of crisis has been previously studied, much of this work tends to focus on first-responders and relief organizations. However, social media use during disasters tends to be decentralized and this organizational structure can promote different types of messages to top-down information systems.

Who tweets about cancer? An analysis of cancer-related tweets in the USA.

Cancer patients, family members and friends are increasingly using social media. Some oncologists and oncology centres are engaging with social media, and advocacy groups are using it to disseminate information and coordinate fundraising efforts. However, the question of whether such social media activity corresponds to areas with higher incidence of cancer or higher access to cancer centres remains understudied.

Modeling virtual organizations with Latent Dirichlet Allocation: a case for natural language processing.

This paper explores a variety of methods for applying the Latent Dirichlet Allocation (LDA) automated topic modeling algorithm to the modeling of the structure and behavior of virtual organizations found within modern social media and social networking environments. As the field of Big Data reveals, an increase in the scale of social data available presents new challenges which are not tackled by merely scaling up hardware and software. Rather, they necessitate new methods and, indeed, new areas of expertise.