One of the first papers that considered Google Trends for unemployment nowcasting was written by Choi and Varian (2009a). The authors attempted to nowcast initial jobless claims for unemployment benefits by using the keywords “Jobs” and “Welfare & Unemployment”. The results revealed that the out-of-sample mean absolute error was reduced by 15.74 and 12.90% for the long- and short-term models, respectively. In the same year, followed papers by D’Amuri (2009) and Askitas and Zimmermann (2009). The work of D’Amuri (2009) limited itself to only one keyword “jobs” whereas Askitas and Zimmermann (2009) used four different categories of keywords: “unemployment office or agency”, “unemployment rate”, “Personnel Consultant”, and keywords that relate to the most popular job search engines in Germany. Both authors concluded that Google Trends helped improve forecasting errors.

As depicted in Table 1, Chadwick and Sengul (2012) nowcasted Türkiye’s unemployment rate using keywords that related to “looking for a job”, “job announcements”, “CV”, and “career” and provided supportive evidence for using Google Trends. Fondeur and Karamé (2013) achieved success in forecasting French youth unemployment with only one keyword “job”. The RMSE was improved on average by 13.6%. Several recent papers are still using a limited range of keywords. For instance, Aaronson et al. (2022), Maas (2019), and Larson and Sinclair (2021) employed a single search term (“unemployment”), D’Amuri and Marcucci (2017) used “jobs”, whereas Simionescu (2020), Simionescua and Cifuentes-Faura (2021) used two terms “unemployment” and “job offers”. Despite the use of single or double keywords, Google Trends improved accuracy.

On the other hand, some researchers claim to have achieved mixed results. Pavlicek and Kristoufek (2015) used the keywords “work” or “jobs” for Visegrad countries in multiple languages. The authors concluded that for Hungary and Slovakia, user search trails can be integrated easily; however, for Poland and Slovakia, the results were not consistent. Similar findings were achieved by Nagao et al. (2019), who limited themselves to only two terms: “jobs” and “job offer”. The authors found no consistency regarding improved accuracy, particularly when considering the long-term nowcasts, whereas Barreira et al. (2013), using “unemployment” and “unemployment benefits”, found an improvement in three out of the four countries analyzed.

Unfortunately, few studies have attempted to incorporate a higher query volume. Among these papers is one by Schiavoni et al. (2021), who studied the Netherlands’ job market. The authors included 85 keyword search terms but limited the query to words that strongly relate to the job process (e.g., CV, cover letter, job vacancies). A slightly different approach was adopted by Caperna et al. (2020) and Yi et al. (2021), who suggested not only examining the “unemployment” keyword but also checking the most-searched terms and, when filtering them, determining which related to work and jobs. Yi et al. (2021) obtained 25 keywords for the USA region and successfully integrated them into the forecasting model. In the case of Caperna et al. (2020), the search term queries were extremely varied; for example, for Estonia, 3 keywords were chosen and 178 for Italy, whereas for some countries, such as Luxemburg or Malta, none were found. The results of Google Trends benefits for Caperna et al. (2020) also varied, and further methods were required to find significant relationships.

The analysis of the existing studies reveals that the authors limited themselves to one or two basic keywords, and the expansion of keywords was highly constrained to the job application procedure. This approach led some researchers to conclude that Google Trends offered no additional benefit to nowcasting models or that trends are inconsistent over longer periods. However, the pitfalls or further improvement of the models could be partially attributed to Google Trends keyword mining process. By only considering one keyword for forecasting, the authors cast aside the big picture and the nuances of behavioral economics that are profoundly related to unemployment numbers. For instance, Liu et al. (2021) and Wanberg et al. (2019) found strong links between unemployment and mental health. In times of downturn, the fear of job loss can trigger mental distress, particularly when loss of employment benefits is taken into account. The study by Breuer (2015) argued that unemployment increases the risk of suicide, while Butterworth et al. (2012) found that poor mental health leads to a longer duration of unemployment for both men and women. Furthermore, studies by Sotis (2021) and Askitas (2015) found that when labor market conditions deteriorate, searches for health symptoms proliferate; therefore, a dimension of mental health keywords (MHKs) could provide substantial benefits to model precision. Unfortunately, to our knowledge, no study is available for nowcasting unemployment that includes MHKs.

Furthermore, a contentious issue among labor economists has been to analyze the trade-offs between leisure and labor. More importantly, Havitz et al. (2004) and Goodman et al. (2016) claim that unemployed people spend more time in leisure to mitigate the stress of joblessness. In the Internet age, leisure time is frequently spent consuming free entertainment, such as YouTube, online video games, and downloading movie torrents via sites such as “The Pirate Bay” (Lehdonvirta 2013). A highly regarded study in this area was undertaken by Dilmaghani (2019) that included torrent websites as a proxy for leisure activities and managed to nowcast unemployment numbers more accurately. However, leisure-related keywords still have more to offer. For instance, Frangos et al. (2011) discovered that people involved in unemployment programs were more likely to develop a problematic pornography habit. This is because, according to Uzieblo and Prescott (2020), pornography may act as a stress reliever in times of high anxiety. Similarly, some kind of association between unemployment and gambling persists. Khanthavit (2021) found that unemployment and gambling have a circular relationship, while Pallesen et al. (2021) and Mallorquí-Bagué et al. (2017) determined that internet gaming disorder was more prominent among single unemployed persons. Thus, an increase in online gaming, gambling platforms, and sites, such as “OnlyFans”, search activity could help to improve Google Trends mining process.

Another negative behavior associated with unemployment is domestic abuse (Anderberg et al. 2016; Bhalotra et al. 2021). According to Anderberg et al. (2016), women who are at risk of job loss become more economically reliant on their partners. This reliance can then lead male partners who have a predisposition to violence to reveal their abusive tendencies. Thus, high female unemployment leads to an elevated risk of intimate partner violence. As a way out, people may use Google searches to find public shelter. A study by Berniell and Facchini (2021) reported a high Google search intensity in 11 countries for keywords such as “domestic abuse” or “domestic violence hotline” during the COVID-19 lockdowns. The strong correlation between unemployment and domestic abuse might also assist nowcasting.

Many other important keywords should also be incorporated into the nowcasting model. Some of these include “gig economy”, “recipes”, “hobbies”, “Netflix”, “payday loans”, lottery tickets”, “alcohol”, “layoffs”, “auto insurance”, “hardship letter”, or words related to comfort food (Dávalos et al. 2011; Askitas 2015; Agarwal et al. 2016; Chadi and Hetschko 2017; Huang et al. 2018; Gabrielyan and Just 2020; McKinsey & Company 2020; Gamze and Aydan 2021).

Previous studies used a wide range of methods to nowcast unemployment numbers. Mulero and García-Hiernaux (2021), Larson and Sinclair (2021), Simionescu (2020), Caperna et al. (2020), Nagao et al. (2019), Dilmaghani (2019), D’Amuri and Marcucci (2017), Pavlicek and Kristoufek (2015), Chadwick and Sengul (2012) and Choi and Varian (2009a) used some form of autoregressive models. Yi et al. (2021) suggested a PRISM approach, and Schiavoni et al. (2021) incorporated a well-known model of DFM, whereas Maas (2019) attempted the MIDAS model. Although each model has its own strengths and weaknesses, the recent developments in neural networks deserve deeper exploration. As stated by Brownlee (2018) and Bruneckiene et al. (2021), neural networks may provide a solution for capturing the non-linear patterns in the data that can be difficult to detect using conventional econometrical methods as well as offering the ability to use a higher volume of data. Unfortunately, few studies have attempted to forecast unemployment numbers using Google Trends in conjunction with LSTMs. Fenga and Son-Turan (2022) used a feed-forward neural network for counterfactual predictions, whereas Singhania and Kundu (2020) deployed the LSTM model for monthly data. According to Singhania and Kundu (2020), the authors attempted, at first, to use the VAR model but were soon faced with a major drawback as the VAR model can only be used to capture the relationship between search trends of a limited number of keywords due to growth in the complexity and parameters. As such, the authors attempted to use LSTMs as an alternative. In the end, the LSTMs significantly outperformed the VAR model but were not used for nowcasting. The latter results could be a good indication to use the LSTM model in this paper as it can help to deal with a large number of keywords in an efficient manner.