Cannabis legalization is emerging as a critical issue in global health and policy landscapes. The World Drug Report 2022 of the United Nations Office on Drugs and Crime underscored the increase in daily cannabis consumption and its associated health ramifications in regions that have legalized the substance [1]. On June 9, 2022, Thailand embarked on a significant move by removing cannabis from Narcotic Category 5, making Thailand the first country in Southeast Asia to legalize cannabis [2]. This allowed individuals to sell cannabis after registering and applying through the Thai FDA website (https://plookganja.fda.moph.go.th/) [3]. People who would like to grow cannabis for in-house consumption are encouraged to register, but registration is not mandatory. There is also no limit on the number of cannabis plants that can be grown, sold, or possessed by individuals. The government discourages but does not prohibit recreational use of cannabis. Despite these provisions, Thailand lacks some regulations present in other countries, notably concerning tetrahydrocannabinol possession limits, plant cultivation, non-medical product tracking, and driving under the influence [4–6].

Twitter is the most suitable social media platform for gathering public opinion on a theme or event. Its real-time feed allows the immediate capture and analysis of user reactions. Hashtags and trends categorize discussions, making it easy to track specific topics. Public visibility enables access to a wide range of opinions from experts and the general public. Moreover, Twitter provides diverse perspectives that are essential for a comprehensive understanding of public sentiment on cannabis legalization, encompassing potential health implications, societal attitudes, and areas where regulation may be needed. Whereas other platforms offer opinion-gathering features, Twitter’s focus on real-time updates, hashtags, and public conversations makes it ideal for tracking and analyzing public opinion. Studies have already utilized Twitter for similar purposes. For example, to analyze public sentiment, Mann et al. collected the top hashtags and the topics discussed from Twitter during the United States House of Representatives’ vote regarding cannabis decriminalization [7].

Due to the lack of clear regulations during legalization and insufficient previous studies concerning the misuse of cannabis [8], it is important to quickly understand how people perceive legislative change and to see which health-related issues have been raised during this period. In this study, we utilized Twitter’s application programming interface (API) to systematically collect tweets in both Thai and English while cannabis legalization took place in Thailand. The data were preprocessed for further analysis by natural language processing (NLP) tools, including sentiment analysis and topic modeling, to understand sentiment changes and health topics discussed on Twitter during this period. With this information we aimed to improve cannabis oversight by public health agencies following legalization.

The overall workflow of the methodology is presented in Supplementary Figure S1.

This study demonstrated the use of Twitter as a social media platform to monitor public perception regarding the recent legalization of cannabis in Thailand. One recent study used data from Facebook to analyze the content and emotional tone of Thai-language posts related to cannabis and kratom during April and November 2015 [15], when possession of either substance was illegal. This indicates that social media can be an informative means of observing opinions regarding cannabis use.

Our results revealed a spike in the number of tweets for both cannabis (TH) and cannabis (EN) associated with cannabis legalization on June 9, 2022. Moreover, we observed that the small spike in cannabis (EN) during May 11–13, 2022 was related to the “Thai officials are giving away 1 million free cannabis plants for citizens to grow at home” campaign [16]. One study that collected data from Twitter during November 2016 demonstrated that the peak in the tweet count on November 8 was related to the United States presidential election: the newly elected president supported the use of cannabis for medical purposes and pushed for more states to allow votes on recreational marijuana legalization [17]. Together, these studies show that key public events could influence the volume of tweets. Future studies will focus on how public events or news related to cannabis influence public responses on social media and identify the characteristics of these events that are associated with positive and negative sentiments. Policymakers and relevant officials could use timely social media monitoring to capture prevalent public concerns or questions, setting key messages for effective public communication.

As shown by our study, negative sentiment was higher than positive sentiment across the periods analyzed for both cannabis (TH) and cannabis (EN). Moreover, a sentiment-analysis comparison before and after legalization showed that negative sentiment increased after the country announced legalization. This change may be explained by two points. First, there have been reports of people becoming ill after consuming food containing cannabis [18,19], which may have raised concerns over food contamination. Second, there is confusion about taking legal action. As a result, concerns may rise for people in Thailand and elsewhere, including sellers, growers, chefs, and consumers. However, this supposition is contradicted by a study from New Zealand, which collected tweets between July 2009 and August 2020, a period before the vote to legalize cannabis for recreational use; it found a positive view of cannabis [20]. A study of cannabis-related tweets in the United States between March and May 2016 found that personal tweets elicited more positive than negative sentiments [21], with more positive sentiment found in states with fewer restrictions. A similar result was found in a study analyzing sentiments toward cannabis-related tweets in the United States and Canada between 2017 and 2019, in which the increase in positive sentiment was correlated to states where cannabis was legal for adult recreational use [22]. The sentiment changes in our results differ from other studies, and not only because of differences in social or general characteristics; the perception of incomplete regulation may be the reason for negative sentiment on Twitter and in other nations’ viewpoints [2,23]. The negative sentiments in our findings may be explained by baseline marijuana regulations in the Thai setting, which have relied on a medical marijuana policy since 2019. Further research should identify factors associated with different sentiments between EN tweets and tweets in other languages on the same topics.

Our findings from topic modeling revealed a health-related emphasis. A manual examination of these topics resulted in nine health categories. Some of these categories were also used in other cannabis-related Twitter studies [24–27]. Regarding our result on “concern over the effects of cannabis on children, teenagers, pregnant women, and individuals with illnesses,” one study used a topic-modeling approach to investigate tweets related to cannabis use in pregnancy [24]. The study identified nine topic clusters, including effects of cannabis during pregnancy, cannabis exposure on infants, and legalization and police. The topic “discussion of whether cannabis could be used to treat certain diseases or physical barriers (e.g., cancer or pain)” appeared in our topic analysis, which agrees with a study in which scraping posts containing cannabis-related terms and exploring the topic discussion suggested that posters thought cannabis might help relieve many health conditions, such as Crohn’s disease, sleep, pain, depression, and cancer [25]. Another topic from our results, “concern over cannabis-containing food,” is consistent with a study of cannabis edibles on Twitter by other researchers, who reported a prevailingly positive sentiment toward edibles. However, their analysis of the content of negative sentiments demonstrated the unreliability of consumption of edibles [27]. The “cannabis tourism” category in our results was found solely in cannabis (EN). One study raised the concern that countries with decriminalization and legalization of cannabis could become an attractive destination for tourists [26]. Thus, the study recommended vigilance regarding vulnerable travelers, particularly those with mental disorders. In this manner, topic-modeling analyses of Twitter data could be used to observe health-related concerns during the legalization of cannabis.

A strength of our study is that Twitter allows us to quickly capture a variety of health-related concerns regarding cannabis legalization. Moreover, this study may help policymakers and healthcare professionals set up a proper oversight program for cannabis to prevent misuse, minimize risks when cannabis is used in food or personal care, and educate vulnerable tourists regarding legalization. Real-time monitoring through Twitter may also enable tailoring fast actions to educate the public on related topics.

However, this study does have several limitations. First, due to the voluntary nature of Twitter, our data may not represent the general population; instead, it was inherently limited to those who choose to use Twitter to publicly express their opinions, which may impose a sampling bias. Second, Twitter’s character limit may constrain the depth and complexity of expressible sentiments, potentially preventing us from capturing nuanced feelings. In addition, the selection and naming of topics were based on the agreement of two researchers with medical backgrounds, which might limit the diversity of perspectives. Experts from fields such as sociology, linguistics, public health, and policymaking could have provided additional insights into topic selection. Moreover, medical viewpoints might have had a strong influence on the topics while potentially ignoring social, legal, or cultural factors. Lastly, the process of sentiment analysis may have struggled with detecting ambiguous phrases, slang, and sarcasm, leading to possible inaccuracies in interpretation.