I’ve been tracking Google Trends Trending Search rankings of 11 countries daily for over a month now. Here are insights and patterns I found in the data.
Google publishes trending search data of many countries in Google Trends website. The data is updated daily and consists of terms. The terms are ranked based on their search volume, with most searched terms listed in the top ranks. To track these ranks, I recently built Google Trends Trending Search Monitor data dashboard. The data dashboard tracks the terms ranks daily since 17th February 2022. The countries covered in the dashboard are United States, United Kingdom, Indonesia, Australia, Singapore, Japan, South Korea, Russia, Germany, France, India. At the time this report is written, the data collected consists of 11068 rows from 45 days of tracking. Furthermore, for the English speaking countries and Indonesia the term ranks are manually classified into news categories. Actually the terms in Google Trends data are already topics, but for the dashboard I generalize those topics into news categories. The news categories are referred to as terms topic in this report.
In all countries with categorized terms, Sports and Entertainment dominate the trending search. In all of these countries as a whole, Sports forms 46% of all trending search terms count, while Entertainment forms 27% of all trending search terms count.
Proportion of terms topic varies in each countries with categorized terms. This variety of proportions may reflect the different interests and needs of societies in each country. Sports and Entertainment still dominate in every country. However, the third terms topic after Sports and Entertainment differ in many countries. In Indonesia, Arts and Culture topic is in the third position. In western countries (United States, United Kingdom, Australia), International topic is in the third position, possibly due to the recent trending Russia – Ukraine conflict. In Singapore, Business and Finance topic is in the third position.
In the time series graph of terms topic, most of the terms topic data points have continuous lines, eventhough they change rank position. The time series graph of terms, however, is different. Most of the terms are not connected to another date. Many of the connected terms are connected to a date several days after the initial appearance of the terms. Such patterns suggest that many terms topics appear in the trends almost daily. We can find one certain news category topics almost every day in the trends rank, eventhough the trending search terms they comprise of vary. However, specific trending search terms appear only occasionally. Certain terms that occur today most likely don’t appear tomorrow. Furthermore, this may suggest that people’s attention span for a specific issue is short, spanning only a day. In the next day, there would likely be other terms of the same news category that appear in the trends. Specific trending search terms that occur continuously in the trends, then, should be considered exceptional and worth further investigation.
Admittedly, there are rooms for improvement regarding methods I used to analyze the trending search. For example, the terms can be tokenized (cut into single words) before being analyzed. Indeed, there are trending search terms that contain the same single word and pertain to a similar issue. These terms should be connected in the time series graph of terms. However, they are disconnected in this analysis because they are considered as totally different issues. If these terms are connected, we may have a graph with more continuous lines between the terms on different dates.
In summary, based on Google trending search data I’ve been tracking for the last 45 days, Sports and Entertainment topics dominate in four English speaking countries and Indonesia. The composition of trending search terms based on news categories vary between countries. Such composition may reflect interests and needs of the people in each country. Looking at the time series graphs, it appears that many of the same news categories topics appear almost daily in the trends. Meanwhile, specific terms appear sparsely in the trends. This means that people may search for the same news category almost every day, but it is likely that the specific issues they are looking are different from the previous day.
The Python script for this analysis can be found at Github (content ID: 1282). The resulting dataset can be found at Kaggle.