Google Trends' popularity may stem from its predominant search engine market share and inherent advantages (Nuti et al., 2014; Mavragani et al., 2018; Arora et al., 2019; Mavragani and Ochoa, 2019). According to Statista (2023), Google has dominated the global search engine market since its inception and had a share of around 84.08 percent as of December 2022. This market share is far greater than other leading search engines, such as Bing (8.95%), Baidu (0.67%), and Yahoo! (2.60%). Thereby, it may represent a large proportion of the population compared to other search engine data providers. Google Trends data are also freely available, unlike other paid search engine data providers, such as Baidu Index (Fang et al., 2021). Nonetheless, it may have limited applicability in countries where Google has limited services, such as China (Fang et al., 2021; Dai and Han, 2023) (Table 1).

Another advantage is that its data represent the inputted queries of its users, thereby representing the actual behavior rather than the users' stated preferences (Mavragani and Ochoa, 2019). It can also provide real-time data that can be used to monitor behavior (Mavragani and Ochoa, 2019). Thus, it may represent difficult and time-consuming information to collect (Mavragani and Ochoa, 2019). Likewise, it provides data about web queries by automatically anonymizing the users' information (Mavragani and Ochoa, 2019; Google, n.d.). Hence, it can provide readily available data about sensitive health topics like AIDS, mental disorders, suicide, and illegal drugs (Mavragani and Ochoa, 2019). Furthermore, it has previously been used in predictions, nowcasting, and forecasting health-related topics (Mavragani and Ochoa, 2019). Hence, it has been increasingly used by health researchers over the years as an indicator of public behavior. For instance, from its inception (January 2004) to January 2014, Nuti et al. (2014) noted 70 studies that utilized Google Trends. Among them, more than a quarter (27%) were used to explore mental health topics (Nuti et al., 2014). By 2016, Mavragani et al. (2018) noted 109 studies using Google Trends.

Over the years, Google Trends has been used to explore various topics and phenomena related to psychiatry and mental health (Nuti et al., 2014; Mavragani and Ochoa, 2019), such as depression (Monzani et al., 2021; Silverio-Murillo et al., 2021; de la Rosa et al., 2022; Wang et al., 2022), anxiety (Monzani et al., 2021; de la Rosa et al., 2022; Lekkas et al., 2022), suicide (Kristoufek et al., 2016; Parker et al., 2017; Silverio-Murillo et al., 2021; de la Rosa et al., 2022), insomnia (Sycińska-Dziarnowska et al., 2021; Lekkas et al., 2022), stress (Silverio-Murillo et al., 2021), substance use disorder (Parker et al., 2017; Alibudbud and Cleofas, 2022), neurocognitive disorder (Piamonte et al., 2021), and mental health, itself (Alibudbud, 2022b). It has also been used to explore online interests and concerns about mental health service providers such as psychiatrists and psychologists (Sycińska-Dziarnowska et al., 2021). Likewise, it has been utilized in psychiatric and mental health research by using search volumes worldwide and among and between countries (Monzani et al., 2021; Silverio-Murillo et al., 2021; Sycińska-Dziarnowska et al., 2021; Alibudbud, 2022b; Alibudbud and Cleofas, 2022; de la Rosa et al., 2022; Lekkas et al., 2022; Wang et al., 2022). It has also been used to analyze specific psychiatric disciplines, such as social psychiatry (Alibudbud, 2022a). It has been correlated with survey and surveillance datasets to predict, nowcast, and forecast particular psychiatric symptoms, such as loneliness and self-harm, with varying degrees of correlation (Mavragani and Ochoa, 2019; Knipe et al., 2021). Thus, future correlations with surveillance datasets of symptoms need to be explored to determine their applicability as a predictor of symptoms at a population level.

Nonetheless, Google Trends remains an excellent and popular tool for indodemiological studies that aim to explore and understand online public concerns, interests, and behaviors about health phenomena, including mental health (Nuti et al., 2014; Mavragani and Ochoa, 2019; Mavragani, 2020; Gianfredi et al., 2021; Rovetta, 2021). A review by Gianfredi et al. (2021) reiterated that studies of online information could be used to inform mental health policies and services. Thus, it can be posited that the high utilization of infodemiological tools, including Google Trends, for psychiatric and mental health topics to inform mental health services and policies can signal the emergence of a new discipline between infodemiology, psychiatric and mental health research.

Drawing on the definition of infodemiology by Eysenbach (2009), this emerging field can be termed as “psychiatric and mental health infodemiology,” and similarly, it can be defined as the science of distribution and determinants of information in an electronic medium or in a population to inform mental health services and policies.

Google Trends remains an excellent and popular tool in psychiatric and mental health infodemiology. However, reviews suggest that studies using Google Trends may have inconsistent methodological documentation limiting the reproducibility and replicability of their findings (Nuti et al., 2014; Arora et al., 2019; Mavragani and Ochoa, 2019). Thus, this minireview describes the methodological considerations for infodemiological studies suggested by Nuti et al. (2014), Arora et al. (2019), and Mavragani and Ochoa (2019) to strengthen the replicability of results and methodological soundness of future psychiatric and mental health infodemiology. As a minimum, the three articles recommended that the following should be documented by studies utilizing Google Trends (Table 2).

While Google Trends has been a useful tool for psychiatric and mental health infodemiology, it has several limitations that must be accounted for. Therefore, there is a need to address some of its important limitations in its interpretation.

First, Google Trends only reflects the interests and behaviors of people with internet access and those who use the Google search engine (Moalong et al., 2021; Alibudbud, 2022a). Hence, it may not reflect the interests and behaviors of areas with limited internet connection and low Google market share. Second, a recent review by Mavragani and Ochoa (2019) has shown that it may have limited validity in areas with low internet penetration or freedom of speech. Thus, it can be recommended that traditional methods such as surveys be done to better understand public interest and concerns, especially if the study aims to understand the concerns of the whole public, including those who are not internet users. Similarly, other search engines, such as Bing, Baidu, Yandex, or Yahoo, can be explored in areas with low Google Search Engine usage (Mavragani, 2020). Additionally, Google Trends can be complemented with data from other highly utilized online platforms, such as Wikipedia, to understand further online public behaviors and concerns (Mavragani, 2020).

Third, Google Trends provides relative values rather than absolute search volumes and queries. Thus, a particular topic's exact number of queries for a particular topic is unknown (Nuti et al., 2014; Moalong et al., 2021). Fourth, Google samples its data to provide a dataset in Google Trends that represents all Google searches for a particular topic (Google, n.d.). However, the sampled queries are unknown (Mavragani and Ochoa, 2019; Rovetta, 2021). As such, it has been noted that their data may vary slightly based on the time of retrieval (Mavragani and Ochoa, 2019; Rovetta, 2021; Eichenauer et al., 2022).

Fifth, Google takes advantage of artificial intelligence in aggregating their search queries. However, the terms and idioms subsumed under their topic keywords are unknown due to limited transparency in Google Trends' algorithm (Nuti et al., 2014). Thus, previous studies have recommended several measures to improve reliability and replicability, such as using statistical methods (Eichenauer et al., 2022), to collect the average search volumes for several days (Rovetta, 2021), or as previously recommended, noting the setting used in their methodology including the exact keywords, query category, use of quotation marks, the rationale for keyword selection, time range, location, and date of retrieval (Nuti et al., 2014; Mavragani and Ochoa, 2019). Sixth, there can be various reasons for changes in search behaviors about specific topics (i.e., increased media coverage) (Bach and Wenz, 2020). Therefore, researchers must be clear on their assumptions about search behaviors and carefully interpret Google Trends data in light of their limitations.