The use of nicotine or tobacco products is a leading preventable cause of cancer, heart diseases, and lung diseases in the United States [1], with cigarette smoking alone responsible for the death of half a million Americans each year [2]. Notably, sexual and gender diverse individuals, often referred to as the LGBTQ+ (lesbian, gay, bisexual, transgender, queer, intersex, asexual, Two Spirit, and other persons who identify as part of this community) populations, are particularly vulnerable to nicotine and tobacco product use [3]. Both the National Cancer Institute and the Centers for Disease Control and Prevention have recognized the LGBTQ+ populations as a critical target in their efforts to combat tobacco use disparities [4-10].

In response to the pressing need for tobacco control and the rapidly evolving landscape of the tobacco market, the National Institutes of Health (NIH) and other health foundations, including the American Cancer Society, have made substantial investments in tobacco control research and tobacco regulatory science [11,12]. According to our calculations using data from the NIH era reporter, funding for tobacco research has shown a remarkable increase, growing from US $7.7 billion in 2016 to US $11.2 billion in 2021 (Multimedia Appendix 1 [13]). Consequently, the volume of peer-reviewed publications related to tobacco has experienced a significant increase. This presents a considerable challenge in efficiently summarizing, synthesizing, and disseminating research findings, especially when it comes to addressing specific target populations, such as the LGBTQ+ populations.

One promising pathway to rapidly assessing the expanding body of literature is the use of natural language processing (NLP) models. NLP is dedicated to deciphering and comprehending how computers interpret human language, equipping them to analyze extensive data sets of natural language [14-16]. While NLP tools have garnered considerable recognition in biomedical research [4-10], aiding in tasks such as disease surveillance (eg, COVID-19) and diagnosing using medical records [17-23], their potential to expedite near real-time synthesis of evidence in tobacco control research remains untapped [24].

Another gap in existing NLP tools is the lack of applications in synthesizing social science research and modeling. A noteworthy example in the domain of tobacco research is the evaluation of the effectiveness of tobacco control policies, which are often assessed using complex statistical modelling and large-scale survey data. These methods demand a specialized semantic database for labelling studies and interpreting results. However, to the best of knowledge, such a semantic database has not been developed yet. Considering that policy interventions at federal, state, and local levels are designed to reach a large number of populations, the lack of a database to facilitate NLP applications may significantly undermine evidence synthesis and thereby the timely adoption of effective policies [25].

Furthermore, in light of the calls from entities such as the NIH and other health agencies to address tobacco use disparities within priority populations, including LGBTQ+ populations, the development NLP tools to aid in the discovery of effective policies tailored to these special populations remains uncharted territory [26-31]. There is an urgent demand for the development of NLP tools (eg, semantic database, NLP algorithms) in tobacco research that have the abilities to synthesize evidence in social science and assist in research gap discovery for priority populations.

In this pilot study, we aimed to achieve the following goals to address the identified research and application gaps: (1) compile a specialized semantic database for tobacco policy research to extract information from journal article abstracts, (2) develop NLP algorithms that comprehend the literature on nicotine and tobacco product use among sexual and gender diverse populations, and (3) compare the discoveries of the NLP algorithms with an ongoing systematic review of tobacco policy research among LGBTQ+ populations [32]. While this pilot study does not fully address the gaps by developing a comprehensive evidence synthesis or discovery tool for tobacco research, the outcomes may pave the road for future tools that can achieve this goal. Our vision is that NLP tools may be able to assist academic scholars and policy makers in prescribing public health policies, such as tobacco control policies, and addressing public health needs, such as reducing health disparities.

In total, we identified 33 articles relevant to sexual and gender diverse populations from the 2993 abstracts. Our trained model successfully extracted 773 named entities (181 unique named entities) from the 33 paper abstracts to describe the themes of these articles. Among the 773 extracted named entities, 688 were already learned by the model during training, while 70 were new time- or age-related words (eg, 18 years, 2013), 9 were new statistical terms (eg, N=20), and 6 were newly discovered and labeled within other categories. We did not observe any newly discovered policy-related terms.

In Figures 1-3, we present the hierarchy of named entities extracted from abstracts in published papers that studied nicotine or tobacco product use among sexual and gender diverse individuals. Each number on the right is the frequency of the corresponding named entity by paper abstract. Named entities with the same color belong to the same main category.

According to our tool, among the 33 tobacco studies related to LGBTQ+ populations, the most frequent use outcomes were “cigarette smoking” (n=17), “substance use” (n=16), “prevalence” (n=16), and “risk” perception (n=14). Also, for these populations, “cigarettes” (n=15) were the most frequently mentioned combustible tobacco product and “e-cigarettes are” (n=8) was the most frequently mentioned noncombustible tobacco product. In addition, for tobacco characteristics, “alcohol” (n=7) and “nicotine” (n=5) were the most mentioned attributes among LGBTQ+ tobacco research papers.

The relation statement findings suggest that a majority of the studies examined “comparison” (n=26), “association” (n=23), and “correlation” (n=6). We found no studies that explicitly used the term “causal” or “causality” in the studies.

The population characteristics mentioned in the studies illustrated that among socioeconomic status terms, the most frequently included were “demographics” (n=8) and “SES factors” (n=8). Among sex and sexual and gender minority terms, the most frequent ones were “bisexual” (n=21), “lesbian” (n=19), and “gay” (n=19). Among racial and ethnic minority group terms, the most frequent ones were “minority groups” (n=8) and “Race/ethnicity” (n=6). For age group terms, the terms included “adult” (n=14), “young adult” (n=11), “adolescent” (n=5), “students” (n=3), and “adolescents and young adults” (n=2).

The policy category showed that in these studies, the most mentioned term was “intervention” (n=16). In addition, while the general term “tobacco control” was mentioned in 6 studies, only 1 study contained any specific policy term (“smoke free air law”). As such, there was a significant gap in policy research among the published articles in the 4 leading tobacco journals between 2015 and early 2021, since only 1 study mentions specific policies when it comes to tobacco research among the LGBTQ+ populations. The statistics and methodology terms further indicated that the most used terms included “survey” (n=18) and “logistic regression” (n=10), and relatively fewer studies mentioned terms related to causal inferences, such as “experimental research” (n=4), “randomization” (n=3), and “clinical trial” (n=1). The studies mentioning “US” also dominated in the numbers, with 12 studies in total. Several studies that assessed countries with multilevel governing levels, such as Canada and the United States, also appeared to have mentioned “state,” “city,” and “province,” suggesting that attention was paid to these defined areas.

We next compared our results using the NLP tools with our ongoing systematic review. Similar to the conclusions of the ongoing systematic review, we found very few studies that yielded specific policy recommendations. This finding was further corroborated by the lack of causal inference methods labeled by the NLP tool. While our NLP tool cannot replace systematic reviews just yet, it does show potential to complement the existing methods and requires less human supervision (systematic reviews usually require at least 2 human coders).

This pilot study builds a semantic database dedicated to tobacco research and developed NLP algorithms to automatically identify, extract, and summarize textual data from published tobacco studies. We further demonstrated a user case wherein we assessed LGBTQ+ tobacco research by labeling key components of a tobacco study: tobacco use outcomes, tobacco characteristics, population characteristics, geographic locations, method and inference, and policy relevance.

It is worth noting that the components we categorized, such as “method and inference,” align with the typical sections found in scientific articles in social science, including measures, methods, results, conclusions, and hypothesis testing. As a result, our tool extracts text segments that are frequently assessed in evidence synthesis, thereby showing the potential of using NLP tools to enhance systematic reviews and facilitate meta-analyses [25].

Additionally, we leveraged the NLP algorithms we created to identify gaps in tobacco research concerning the LGBTQ+ populations and concluded that there is a scarcity of studies assessing policy impacts on this demographic using causal inference methods. This finding is consistent with our ongoing systematic review [32], highlighting how NLPs have the capacity to aid in both evidence synthesis and research gap discoveries. This, in turn, has the potential to streamline research efforts, reduce labor costs, and influence the trajectories of future research directions [51,52].

Using the NLP tool, we further found some interesting patterns in tobacco research involving LGBTQ+ populations. It appears that the product drawing the most attention in the field is cigarettes or cigarette smoking and that the number of studies of various age groups is almost evenly distributed between youth or young adults and adults. Moreover, the existing evidence body is dominated by studies coming from the United States. These patterns are consistent with the research needs to reduce cigarette smoking among LGBTQ+ populations in the United States, where 16.1% of LGBTQ+ adults and 17.4% of LGBTQ+ high schooler students smoke cigarettes—this is 4% to 6% higher than their heterosexual counterparts [53,54]. Therefore, our findings align with the ongoing research needs and the financial investments made by the US health agencies like the NIH, thereby bolstering the confidence in the NLP tool that we developed.

Finally, while the semantic database and language model in this pilot study are designed to extract and summarize key components of tobacco research, many of the terms and labeling categories are broad and applicable to public health and social science research in general, such as “methods and inference” and “relation terms.” Therefore, our tool has the potential to transform the evidence synthesis paradigm in tobacco control and public health at large by enabling more efficient and effective analyses of large volumes of textual data. Future tool development may extend its reach to other public health domains, fostering the real-time translation of research findings into evidence-based policymaking, thereby contributing significantly to the advancement of public health initiatives.

Our study has several limitations. First, for the development of keywords and the application of the NLP, we focused on 4 peer-reviewed tobacco-specific research journals, which were not representative of the entire tobacco control literature. However, considering the prominence and extensive content covered by these journals, we believe that this selection is unlikely to introduce significant selection bias or result in the omission of crucial keywords. Second, although we used our ongoing systematic review as a benchmark for the qualitative assessment of the results obtained in this pilot study, we did not perform a quantitative comparison of our findings with the ground truth derived from the systematic review. This quantitative evaluation, which might include measures like Cohen kappa, was not conducted because the systematic review has not yet been finalized. Consequently, future research endeavors are required to undertake a thorough quantitative comparison between the training data and the established ground truth using statistical testing for a more comprehensive assessment of the NLP tool’s performance.

Despite the limitations, our pilot study serves as a compelling demonstration of the capabilities of NLP tools in expediting the processes of evidence synthesis and the identification of research gaps. Expanding the scope of this pilot research to encompass other public health disciplines, extending beyond the realm of tobacco control, holds the promise of fundamentally transforming the approach to evidence synthesis. Such expansion has the potential to play a pivotal role in shaping policy development across a wide spectrum of public health domains.