Assessing mental health signals among sexual and gender minorities using Twitter data

Exploring the relationship between government stringency and preventative social behaviours during the COVID-19 pandemic in the United Kingdom

We constructed a preventive social behaviours (PSB) Index using survey questions that were aligned with WHO recommendations, and used linear regression to assess the impact of reported COVID-19 deaths (RCD), people's confidence of government handling of the pandemic (CGH) and government stringency (GS) in the United Kingdom (UK) over time on the PSB index. We used repeated, nationally representative, cross-sectional surveys in the UK over the course of 41 weeks from 1st April 2020 to January 28th, 2021, including a total of 38,092 participants. The PSB index was positively correlated with the logarithm of RCD (R: 0.881, p < .001), CGH (R: 0.592, p < .001) and GS (R: 0.785, p < .001), but was not correlated with time (R: -0.118, p = .485). A multivariate linear regression analysis suggests that the log of RCD (coefficient: 0.125, p < .001), GS (coefficient: 0.010, p = .019), and CGH (coefficient: 0.0.009, p < .001) had a positive and significant impact on the PSB Index, while time did not affect it significantly. These findings suggest that people's behaviours could have been affected by multiple factors during the pandemic, with the number of COVID-19 deaths being the largest contributor towards an increase in protective behaviours in our model.

Qualitative and Artificial Intelligence-based Sentiment Analyses of Anti-LGBTI+ Hate Speech on Twitter in Turkey

The aim of this study was to evaluate hate speech in Turkish LGBTI+-related tweets during a one-month period of artificial intelligence-based sentiment analyses. Turkish tweets related to LGBTI+, were retrieved using Python library Tweepy and were evaluated by sentiment analysis. The researchers then performed a qualitative analysis of the most frequently liked and retweeted tweets (n = 556). Sentiment analysis revealed that 69.5% of tweets were negative, 23.3% were neutral, and 7.2% were positive. The qualitative analysis was grouped under seven themes: LGBTI+ Club; Terrorism and Terrorist Organization Membership; Perversion, Illness, Immorality; Presence in History; Religious References; Insults; and Humiliation. The results of this study show that anti-LGBTI+ hate speech in Turkey is significant in terms of both quality and quantity. As LGBTI+ individuals are at risk for excess mental distress and disorders, it is important to understand the risks and other factors that ameliorate stress and contribute to mental health in social media.

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.

Biases in using social media data for public health surveillance: A scoping review

OBJECTIVES

A landscape scan of the methods that are used to either assess or mitigate biases when using social media data for public health surveillance, through a scoping review.

MATERIALS AND METHODS

Following best practices, we searched two literature databases (i.e., PubMed and Web of Science) and covered literature published up to July 2021. Through two rounds of screening (i.e., title/abstract screening, and then full-text screening), we extracted study objectives, analysis methods, and the methods used to assess or address the different biases from the eligible articles.

RESULTS

We identified a total of 2,856 articles from the two databases. After the screening processes, we extracted and synthesized 20 studies that either assessed or mitigated biases when leveraging social media data for public health surveillance. Researchers have tried to assess or address several different types of biases such as demographic bias, keyword bias, and platform bias. In particular, we found 11 studies that tried to measure the reliability of the research findings from social media data by comparing them with other data sources.

DISCUSSION AND CONCLUSION

We synthesized the types of biases and the methods used to assess or address the biases in studies that use social media data for public health surveillance. We found very few studies, despite the large number of publications using social media data, considered the various bias issues that are present from data collection to analysis methods. Overlooking bias can distort the study results and lead to unintended consequences, especially in the field of public health surveillance. These research gaps warrant further investigations more systematically. Strategies from other fields for addressing biases can be introduced for future public health surveillance systems that use social media data.

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

BACKGROUND

A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods.

OBJECTIVE

This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field.

METHODS

The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings.

RESULTS

We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%).

CONCLUSIONS

ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health.

Deep learning models in detection of dietary supplement adverse event signals from Twitter

Objective

The objective of this study is to develop a deep learning pipeline to detect signals on dietary supplement-related adverse events (DS AEs) from Twitter.

Materials and Methods

We obtained 247 807 tweets ranging from 2012 to 2018 that mentioned both DS and AE. We designed a tailor-made annotation guideline for DS AEs and annotated biomedical entities and relations on 2000 tweets. For the concept extraction task, we fine-tuned and compared the performance of BioClinical-BERT, PubMedBERT, ELECTRA, RoBERTa, and DeBERTa models with a CRF classifier. For the relation extraction task, we fine-tuned and compared BERT models to BioClinical-BERT, PubMedBERT, RoBERTa, and DeBERTa models. We chose the best-performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (ie, iDISK).

Results

DeBERTa-CRF model outperformed other models in the concept extraction task, scoring a lenient microaveraged F1 score of 0.866. RoBERTa model outperformed other models in the relation extraction task, scoring a lenient microaveraged F1 score of 0.788. The end-to-end pipeline built on these 2 models was able to extract DS indication and DS AEs with a lenient microaveraged F1 score of 0.666.

Conclusion

We have developed a deep learning pipeline that can detect DS AE signals from Twitter. We have found DS AEs that were not recorded in an existing knowledge base (iDISK) and our proposed pipeline can as sist DS AE pharmacovigilance.

Coding and Classifying Knowledge Exchange on Social Media: a Comparative Analysis of the #Twitterstorians and AskHistorians Communities

As social media become a staple for knowledge discovery and sharing, questions arise about how self-organizing communities manage learning outside the domain of organized, authority-led institutions. Yet examination of such communities is challenged by the quantity of posts and variety of media now used for learning. This paper addresses the challenges of identifying (1) what information, communication, and discursive practices support successful online communities, (2) whether such practices are similar on Twitter and Reddit, and (3) whether machine learning classifiers can be successfully used to analyze larger datasets of learning exchanges. This paper builds on earlier work that used manual coding of learning and exchange in Reddit 'Ask' communities to derive a coding schema we refer to as 'learning in the wild'. This schema of eight categories: explanation with disagreement, agreement, or neutral presentation; socializing with negative, or positive intent; information seeking; providing resources; and comments about forum rules and norms. To compare across media, results from coding Reddit's AskHistorians are compared to results from coding a sample of #Twitterstorians tweets (n = 594). High agreement between coders affirmed the applicability of the coding schema to this different medium. LIWC lexicon-based text analysis was used to build machine learning classifiers and apply these to code a larger dataset of tweets (n = 69,101). This research shows that the 'learning in the wild' coding schema holds across at least two different platforms, and is partially scalable to study larger online learning communities.

Exploring Eating Disorder Topics on Twitter: Machine Learning Approach

Background

Eating disorders (EDs) are a group of mental illnesses that have an adverse effect on both mental and physical health. As social media platforms (eg, Twitter) have become an important data source for public health research, some studies have qualitatively explored the ways in which EDs are discussed on these platforms. Initial results suggest that such research offers a promising method for further understanding this group of diseases. Nevertheless, an efficient computational method is needed to further identify and analyze tweets relevant to EDs on a larger scale.

Objective

This study aims to develop and validate a machine learning–based classifier to identify tweets related to EDs and to explore factors (ie, topics) related to EDs using a topic modeling method.

Methods

We collected potential ED-relevant tweets using keywords from previous studies and annotated these tweets into different groups (ie, ED relevant vs irrelevant and then promotional information vs laypeople discussion). Several supervised machine learning methods, such as convolutional neural network (CNN), long short-term memory (LSTM), support vector machine, and naïve Bayes, were developed and evaluated using annotated data. We used the classifier with the best performance to identify ED-relevant tweets and applied a topic modeling method—Correlation Explanation (CorEx)—to analyze the content of the identified tweets. To validate these machine learning results, we also collected a cohort of ED-relevant tweets on the basis of manually curated rules.

Results

A total of 123,977 tweets were collected during the set period. We randomly annotated 2219 tweets for developing the machine learning classifiers. We developed a CNN-LSTM classifier to identify ED-relevant tweets published by laypeople in 2 steps: first relevant versus irrelevant (F₁ score=0.89) and then promotional versus published by laypeople (F₁ score=0.90). A total of 40,790 ED-relevant tweets were identified using the CNN-LSTM classifier. We also identified another set of tweets (ie, 17,632 ED-relevant and 83,557 ED-irrelevant tweets) posted by laypeople using manually specified rules. Using CorEx on all ED-relevant tweets, the topic model identified 162 topics. Overall, the coherence rate for topic modeling was 77.07% (1264/1640), indicating a high quality of the produced topics. The topics were further reviewed and analyzed by a domain expert.

Conclusions

A developed CNN-LSTM classifier could improve the efficiency of identifying ED-relevant tweets compared with the traditional manual-based method. The CorEx topic model was applied on the tweets identified by the machine learning–based classifier and the traditional manual approach separately. Highly overlapping topics were observed between the 2 cohorts of tweets. The produced topics were further reviewed by a domain expert. Some of the topics identified by the potential ED tweets may provide new avenues for understanding this serious set of disorders.

Mining Twitter to Assess the Determinants of Health Behavior towards Palliative Care in the United States

Palliative care is a specialized service with proven efficacy in improving patients' quality-of-life. Nevertheless, lack of awareness and misunderstanding limits its adoption. Research is urgently needed to understand the determinants (e.g., knowledge) related to its adoption. Traditionally, these determinants are measured with questionnaires. In this study, we explored Twitter to reveal these determinants guided by the Integrated Behavioral Model. A secondary goal is to assess the feasibility of extracting user demographics from Twitter data-a significant shortcoming in existing studies that limits our ability to explore more fine-grained research questions (e.g., gender difference). Thus, we collected, preprocessed, and geocoded palliative care-related tweets from 2013 to 2019 and then built classifiers to: 1) categorize tweets into promotional vs. consumer discussions, and 2) extract user gender. Using topic modeling, we explored whether the topics learned from tweets are comparable to responses of palliative care-related questions in the Health Information National Trends Survey.