Lijiang Shen

All models are wrong, but some are useful. — George Box (1976)This is the second and last editorial in my role as the editor of Communication Methods and Measures (CMM). Six years ago, when I took ...

BACKGROUND The rapid growth of the internet and social media platforms has transformed them into major sources of information for the public. However, the widespread dissemination of misinformation on social media—particularly regarding vaccines—poses a critical threat to public health by undermining public trust, reducing vaccine uptake, and increasing vaccine hesitancy. As the volume of online data continues to expand, there is a growing need for an end-to-end automated and reliable content analysis of large-scale data to identify prevalent misinformation narratives and public concerns. OBJECTIVE The goal of this paper is to analyze misinformation related to COVID-19 and HPV vaccines, both separately and jointly, to identify and compare the dominant themes contributing to vaccine hesitancy. To do this, we apply the Misinformation Detection and Inoculation Process (MDIP) method—an end-to-end automated framework for health misinformation analysis introduced in (1). Besides, by deploying MDIP across multiple vaccine misinformation datasets and obtaining consistent, interpretable thematic structures, we demonstrate its effectiveness as a scalable framework for analyzing health misinformation. METHODS We utilized MDIP (1) to analyze unstructured social media data. Accordingly, first, a fine-tuned BERT model was employed to detect misinformation posts. Second, we applied BERTopic with an assignment algorithm to cluster documents. Finally, unlike traditional word-level models, we used structured prompt to automatically generate sentence-level topic descriptions and hierarchical themes, enabling granular analysis of narratives. RESULTS The analysis revealed COVID-19 misinformation was predominantly fueled by Conspiracy Theories (45.90%), including narratives such as microchips and 5G. In contrast, HPV misinformation was overwhelmingly driven by Safety and Concerns (58.98%), including infertility and adverse effects. Collectively, the combined analysis reveals a fundamental convergence in the determinants of vaccine hesitancy, demonstrating that public apprehension is universally rooted in safety concerns and skepticism regarding institutional motives. Temporal analysis showed that misinformation intensity is reactive, consistently spiking in response to specific regulatory approvals and public health milestones. CONCLUSIONS The proposed content analysis of HPV and COVID-19 includes four types of analysis: describing topics, categorizing topics into themes, examining theme distribution, and tracking changes over time. This approach helps extract the major concerns and dynamics surrounding the HPV and COVID-19 vaccines.

It is common to experience fatigue after prolonged exertions, especially when there is no sufficient break. Contemporary concepts such as compassion fatigue (Kinnick et al., 1996), social media fat...

Background: The rapid growth of social media as an information channel has enabled the swift spread of inaccurate or false health information, significantly impacting public health. This widespread dissemination of misinformation has caused confusion, eroded trust in health authorities, led to noncompliance with health guidelines, and encouraged risky health behaviors. Understanding the dynamics of misinformation on social media is essential for devising effective public health communication strategies. Objective: This study aims to present a comprehensive and automated approach that leverages large language models (LLMs) and machine learning techniques to detect misinformation on social media, uncover the underlying causes and themes, and generate refutation arguments, facilitating control of its spread and promoting public health outcomes by inoculating people against health misinformation. Methods: We use 2 datasets to train 3 LLMs, namely, BERT, T5, and GPT-2, to classify documents into 2 categories: misinformation and nonmisinformation. In addition, we use a separate dataset to identify misinformation topics. To analyze these topics, we applied 3 topic modeling algorithms—Latent Dirichlet Allocation, Top2Vec, and BERTopic—and selected the optimal model based on performance evaluated across 3 metrics. Using a prompting approach, we extract sentence-level representations for the topics to uncover their underlying themes. Finally, we design a prompt text capable of identifying misinformation themes effectively. Results: The trained BERT model demonstrated exceptional performance, achieving 98% accuracy in classifying misinformation and nonmisinformation, with a 44% reduction in false-positive rates for artificial intelligence–generated misinformation. Among the 3 topic modeling approaches used, BERTopic outperformed the others, achieving the highest metrics with a Coherence Value of 0.41, Normalized Pointwise Mutual Information of −0.086, and Inverse Rank-Biased Overlap of 0.99. To address the issue of unclassified documents, we developed an algorithm to assign each document to its closest topic. In addition, we proposed a novel method using prompt engineering to generate sentence-level representations for each topic, achieving a 99.6% approval rate as “appropriate” or “somewhat appropriate” by 3 independent raters. We further designed a prompt text to identify themes of misinformation topics and developed another prompt capable of detecting misinformation themes with 82% accuracy. Conclusions: This study presents a comprehensive and automated approach to addressing health misinformation on social media using advanced machine learning and natural language processing techniques. By leveraging LLMs and prompt engineering, the system effectively detects misinformation, identifies underlying themes, and provides explanatory responses to combat its spread. The proposed method was tested on an English language COVID-19–related dataset and has not been evaluated on real-world online social media data; the experiments were conducted offline.

Background The rapid growth of social media as an information channel has enabled the swift spread of inaccurate or false health information, significantly impacting public health. This widespread dissemination of misinformation has caused confusion, eroded trust in health authorities, led to noncompliance with health guidelines, and encouraged risky health behaviors. Understanding the dynamics of misinformation on social media is essential for devising effective public health communication strategies. Objective This study aims to present a comprehensive and automated approach that leverages Large Language Models (LLMs) and Machine Learning (ML) techniques to detect misinformation on social media, uncover the underlying causes and themes, and generate refutation arguments, facilitating control of its spread and promoting public health outcomes by inoculating people against health misinformation. Methods We use two datasets to train three LLMs, namely BERT, T5, and GPT-2, to classify documents into two categories: misinformation and non-misinformation. Additionally, we employ a separate dataset to identify misinformation topics. To analyze these topics, we apply three topic modeling algorithms—Latent Dirichlet Allocation (LDA), Top2Vec, and BERTopic—and selected the optimal model based on performance evaluated across three metrics. Using a prompting approach, we extract sentence-level representations for the topics to uncover their underlying themes. Finally, we design a prompt text capable of identifying misinformation themes effectively. Results The trained BERT model demonstrated exceptional performance, achieving 98% accuracy in classifying misinformation and non-misinformation, with a 44% reduction in false positive rates for AI-generated misinformation. Among the three topic modeling approaches employed, BERTopic outperformed the others, achieving the highest metrics with a Coherence Value (CV) of 0.41, Normalized Pointwise Mutual Information (NPMI) of -0.086, and Inverted RBO (IRBO) of 0.99. To address the issue of unclassified documents, we developed an algorithm to assign each document to its closest topic. Additionally, we proposed a novel method using prompt engineering to generate sentence-level representations for each topic, achieving a 99.6% approval rate as “appropriate” or “somewhat appropriate” by three independent raters. We further designed a prompt text to identify themes of misinformation topics and developed another prompt capable of detecting misinformation themes with 80% accuracy. Conclusions This study presents a comprehensive and automated approach to addressing health misinformation on social media using advanced machine learning and natural language processing techniques. By leveraging large language models (LLMs) and prompt engineering, the system effectively detects misinformation, identifies underlying themes, and provides explanatory responses to combat its spread. (Journal of Medical Internet Research) doi:

BACKGROUND The rapid growth of social media as an information channel has enabled the swift spread of inaccurate or false health information, significantly impacting public health. This widespread dissemination of misinformation has caused confusion, eroded trust in health authorities, led to noncompliance with health guidelines, and encouraged risky health behaviors. Understanding the dynamics of misinformation on social media is essential for devising effective public health communication strategies. OBJECTIVE This study aims to present a comprehensive and automated approach that leverages Large Language Models (LLMs) and Machine Learning (ML) techniques to detect misinformation on social media, uncover the underlying causes and themes, and generate refutation arguments, facilitating control of its spread and promoting public health outcomes by inoculating people against health misinformation. METHODS We use two datasets to train three LLMs, namely BERT, T5, and GPT-2, to classify documents into two categories: misinformation and non-misinformation. Additionally, we employ a separate dataset to identify misinformation topics. To analyze these topics, we apply three topic modeling algorithms—Latent Dirichlet Allocation (LDA), Top2Vec, and BERTopic—and selected the optimal model based on performance evaluated across three metrics. Using a prompting approach, we extract sentence-level representations for the topics to uncover their underlying themes. Finally, we design a prompt text capable of identifying misinformation themes effectively. RESULTS The trained BERT model demonstrated exceptional performance, achieving 98% accuracy in classifying misinformation and non-misinformation, with a 44% reduction in false positive rates for AI-generated misinformation. Among the three topic modeling approaches employed, BERTopic outperformed the others, achieving the highest metrics with a Coherence Value (CV) of 0.41, Normalized Pointwise Mutual Information (NPMI) of -0.086, and Inverted RBO (IRBO) of 0.99. To address the issue of unclassified documents, we developed an algorithm to assign each document to its closest topic. Additionally, we proposed a novel method using prompt engineering to generate sentence-level representations for each topic, achieving a 99.6% approval rate as "appropriate" or "somewhat appropriate" by three independent raters. We further designed a prompt text to identify themes of misinformation topics and developed another prompt capable of detecting misinformation themes with 80% accuracy. CONCLUSIONS This study presents a comprehensive and automated approach to addressing health misinformation on social media using advanced machine learning and natural language processing techniques. By leveraging large language models (LLMs) and prompt engineering, the system effectively detects misinformation, identifies underlying themes, and provides explanatory responses to combat its spread.

Employing a US national sample (N = 1041), this project investigates the relationship between media usage and vaccine hesitancy, as well as the potential variations in this relationship based on vaccine types and individuals’ racial backgrounds. Findings revealed that participants held different levels of acceptance of different vaccines. Compared to other racial groups, African Americans held a lower level of positive attitudes toward different types of vaccines; however, racial groups did not differ significantly in terms of intention to receive vaccines. Individuals’ exposure to information emphasizing various aspects of vaccination either mitigate or enhance their vaccine hesitancy. In addition, the number of social media platforms individuals regularly use is negatively associated with their vaccine hesitancy. The study offers valuable insights for addressing vaccine hesitancy in diverse populations. Theoretical and practical implications were discussed.

Low statistical power undermines a credible scientific discipline. This paper presents a statistical power assessment of experimental communication research based on a random sample of 416 studies published in five central communication journals over the last two decades (2001–2023). Our analyses showed that there was a lack of attention to power and power analysis, with the majority of studies not even mentioning statistical power. We also demonstrated that between-subjects studies were overall underpowered for two-group, one-way multi-group, and factorial designs. Our findings suggest that conducting power analyses to ensure adequate statistical power should be a key priority for experimental communication research.