Health Benefits and Adverse Effects of Kratom: A Social Media Text-Mining Approach

Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom’s benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom’s effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor embedding (t-SNE) technique to identify kratom’s benefits and adverse effects. Results: The analysis showed that kratom aids in addiction recovery and managing opiate withdrawal, alleviates anxiety, depression, and chronic pain, enhances mood, energy, and overall mental well-being, and improves quality of life. Conversely, it may induce nausea, upset stomach, and constipation, elevate heart risks, affect respiratory function, and threaten liver health. Additional reported side effects include brain damage, weight loss, seizures, dry mouth, itchiness, and impacts on sexual function. Conclusion: This combined approach underscores its effectiveness in providing a comprehensive understanding of diverse reactions to kratom, complementing traditional research methodologies used to study kratom

Perception of Bias in ChatGPT: Analysis of Social Media Data

In this study, we aim to analyze the public perception of Twitter users with respect to the use of ChatGPT and the potential bias in its responses. Sentiment and emotion analysis were also analyzed. Analysis of 5,962 English tweets showed that Twitter users were concerned about six main types of biases, namely: political, ideological, data & algorithmic, gender, racial, cultural, and confirmation biases. Sentiment analysis showed that most of the users reflected a neutral sentiment, followed by negative and positive sentiment. Emotion analysis mainly reflected anger, disgust, and sadness with respect to bias concerns with ChatGPT use

A Comparative Analysis of the Interpretability of LDA and LLM for Topic Modeling: The Case of Healthcare Apps

This study compares the interpretability of the topics resulting from three topic modeling techniques, namely, LDA, BERTopic, and RoBERTa. Using a case study of three healthcare apps (MyChart, Replika, and Teladoc), we collected 39,999, 52,255, and 27,462 reviews from each app, respectively. Topics were generated for each app using the three topic models and labels were assigned to the resulting topics. Comparative qualitative analysis showed that BERTopic, RoBERTa, and LDA have relatively similar performance in terms of the final list of resulting topics concerning human interpretability. The LDA topic model achieved the highest rate of assigning labels to topics, but the labeling process was very challenging compared to BERTopic and RoBERTa, where the process was much easier and faster given the fewer numbers of focused words in each topic. BERTopic and RoBERTa generated more cohesive topics compared to the topics generated by LDA

Drivers and Challenges of Wearable Devices Use: Content Analysis of Online Users Reviews

With recent advancements in wearable device technologies, there is still a need to investigate drivers and challenges associated with the use of these devices. Following a content analysis approach, this study leverages recent “found large-scale” data to better understand the drivers and challenges that affect the adoption and use of such devices. Analyzing a total of 16,717 online reviews about wearable devices, the findings emphasized the importance of various functionalities (perceived usefulness), appeal, and a number of device design features as the most prominent drivers, while concerns about quality, credibility, and perceived value as potential challenges to wearable adoption and continued use. The findings could inform theoretical models for technology adoption and continued use and can also provide guidance to the design and development of wearable devices

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data

Background: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. Objective: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. Methods: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. Results: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. Conclusions: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics

Dark Web Analytics : A Comparative Study of Feature Selection and Prediction Algorithms

The value and size of information exchanged through dark-web pages are remarkable. Recently Many researches showed values and interests in using machine-learning methods to extract security-related useful knowledge from those dark-web pages. In this scope, our goals in this research focus on evaluating best prediction models while analyzing traffic level data coming from the dark web. Results and analysis showed that feature selection played an important role when trying to identify the best models. Sometimes the right combination of features would increase the model’s accuracy. For some feature set and classifier combinations, the Src Port and Dst Port both proved to be important features. When available, they were always selected over most other features. When absent, it resulted in many other features being selected to compensate for the information they provided. The Protocol feature was never selected as a feature, regardless of whether Src Port and Dst Port were available

Dark Side of GenAI: A Blackbox Analysis of X

Recent advancements in generative artificial intelligence (GenAI) have raised many fears, risks, and concerns (Kim 2023; Okey et al. 2023). To shed light on the dark side of GenAI, we collected 55,916 posts from X (formerly Twitter). Based on the content of these posts, we manually labeled a sample set with the corresponding dark side, then identified a short, comprehensive list of GenAI dark sides. Using this list, we trained the ReadMe classifier, a supervised learning algorithm on Brandwatch (“Crimson Hexagon and Brandwatch” 2020), to classify the remaining posts. Further analysis, including emotion analysis and analysis of professions and interests yielded several insights. We found that most posts have a negative sentiment with a total count of 20,777 (89.9%) posts. The emotion analysis showed that majority of the users expressed anger with 15,688 posts (43.4%), followed by fear (7,307 posts, 20.2%), sadness (5,790 posts, 16%), joy (4,255 posts, 11.8%), disgust (2,896 posts, 8%), and surprise (252 posts, 0.7%). Regarding professions, the top four groups expressing concerns about the dark side of GenAI, were the executives group (2,703, 30%), followed by the artists group (1,305, 14%) and software developers and the IT group (1,151 ,13%), then the teachers and lecturers group (1,019, 11%). Similarly, based on interest, the top four groups expressing concerns about the dark side of GenAI, were the technology group (4,446 posts, 18%), followed by those interested in business (3,605 posts, 15%), those who are interested in books (2,831 posts, 11%), and those who are mainly interested in family and parenting (1,892 posts, 8%). Regarding the identified dark sides of GenAI, overall, we identified seven dark sides. Most posts discussed concerns about misinformation and digital deception (18,745 posts, 53%), followed by degradation in quality (8,981 posts, 25%), plagiarism (2,004 posts, 6%), job losses due to automation (1,618 posts, 5%), security and privacy concerns (1,516 posts, 4%), bias (1,454 posts, 4%), ethical concerns (7,92 posts, 4%), and legal and defamation issues (455 posts, 1%). This research not only reported the different types of dark sides discussed on X but also ranked the most discussed topics according to the volume of posts, interests, and professions

A Comparative Analysis of Anti-vax Discourse on Twitter Before and After COVID-19 Onset

This study aimed to identify and assess the prevalence of vaccine-hesitancy-related topics on Twitter in the periods before and after the Coronavirus Disease 2019 (COVID-19) outbreak. Using a search query, 272,780 tweets associated with anti-vaccine topics and posted between 1 January 2011, and 15 January 2021, were collected. The tweets were classified into a list of 11 topics and analyzed for trends during the periods before and after the onset of COVID-19. Since the beginning of COVID-19, the percentage of anti-vaccine tweets has increased for two topics, “government and politics” and “conspiracy theories,” and decreased for “developmental disabilities.” Compared to tweets regarding flu and measles, mumps, and rubella vaccines, those concerning COVID-19 vaccines showed larger percentages for the topics of conspiracy theories and alternative treatments, and a lower percentage for developmental disabilities. The results support existing anti-vaccine literature and the assertion that anti-vaccine sentiments are an important public-health issue

Factors Affecting Users’ Satisfaction with Telehealth Apps: Analysis of Users Reviews using BERT

Telehealth mobile apps and telehealth services are increasingly used by patients, particularly, post-COVID-19. This study examines factors related to users’ satisfaction with these apps and services by analyzing reviews from actual telehealth app users. A total of 53,209 reviews were collected from nine telehealth apps on the Google Play store. Using BERT embeddings, UMAP, and HDBSCAN, topics were generated and labeled to identify these factors. Results showed that telehealth app users expressed several factors related to satisfaction with telehealth apps, which could impact the acceptability and adoption of such apps. These include usability (ease of use), usefulness, convenience and efficiency, cost and affordability, technical performance and connectivity, professionalism and expertise, and comprehensive care support