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Case Studies
Published in Michael Muhlmeyer, Shaurya Agarwal, Information Spread in a Social Media Age, 2021
Michael Muhlmeyer, Shaurya Agarwal
But where does one begin? Certainly, a case study subject must be chosen, and data must be collected. Data collection can be a lengthy and potentially costly endeavor without first pinpointing the subject of the study. Luckily, there are a number of tools to at least help a researcher obtain a preliminary sense of the data on a person, group, or event as a good first step. One such tool, for example, is the popular Google Trends. Google Trends compiles and organizes real Google searches, allowing you to enter a search term and see the common and trending terms related to those you entered. In principle, people are searching for things they find curious, news, and events they wish to know more about, topics being discussed as a culture, and more. Yes, searches alone do not give researchers a good or detailed picture of information spread. Still, it might provide a clue as to a starting point, before resorting to web scraping, purchasing data, or other methods of obtaining data. Additionally, trending searches can give hints of topics that should have significant amounts of interest and data to eventually conduct a case study of adequate population size (the more data points, the better). A cursory “trending” search shows that one popular search trend in the past several years has been the Apple iPhone. It is unsurprising, as the iPhone has been a trendy consumer device since its initial release in 2007. Figure 10.1 shows the Google Trends search for iPhones from 2004 to 2020.
A novel seasonal decomposition-based short-term forecasting framework with Google Trends data
Published in Yafei Zheng, Kin Keung Lai, Shouyang Wang, Forecasting Air Travel Demand, 2018
Yafei Zheng, Kin Keung Lai, Shouyang Wang
Web search data from the search engines has been proven to be a great predictor of macroeconomic activities (e.g., Goel et al., 2010; Choi and Varian, 2012; Wu and Brynjolfsson, 2015). The main reason for this connection between the web search data and macroeconomic activities would be that those search data connect closely with users’ daily life and can reflect the users’ opinions and interests on economic events. Generally, web search data are defined as search volumes of a specific term or phrases via search engines during a period of time. A larger search volume tends to represent more attention and interest on this subject from the users, thus it could be used to help forecast demand to a great extent. Among all famous search engines, Google is the most popular with about 65.2 percent market share according to the comScore marketing research, so the search data from Google are more representative than other search engines. As a public tool, Google Trends provides time sequence search data by dividing the count of each search keyword by total online search queries which are submitted during that week. The Google Trends data are available at a weekly or monthly frequency since January 2004. Hence, the Google Trends data can provide a comprehensive and feasible way to measure the public opinions and interests, and are therefore considered as another important data source for the economic forecasting research. Due to the close connection between the air travel demand and passengers’ behavior, we are trying to incorporate the Google Trends data into our demand forecasting models in this chapter.
The COVID-19 pandemic and shareholder value: impact and mitigation
Published in International Journal of Production Research, 2023
Maximilian Klöckner, Christoph G. Schmidt, Stephan M. Wagner
Second, we use Google Trends data to examine the level of public attention towards the COVID-19 pandemic. The public attention towards a specific topic is associated with the release of new information, allowing us to assess the time when available information has been absorbed by stock prices (e.g. Cziraki, Mondria, and Wu 2021). Similar to Gaertner, Hoopes, and Williams (2020), we use publicly available Google Trends data to determine a suitable event window. Google Trends data reflects the number of searches for a given keyword during a specified period in a certain country, indexed between 0 and 100. Figure 3 illustrates the Google Trends indices for the U.S. and China during the COVID-19 pandemic. The observed pattern is similar to the severity of the COVID-19 pandemic (see Figure 2), as we also see a sharp increase in China in late January, followed by a U.S. peak in March 2020. The public attention towards the pandemic also supports the selection of an (0, 80) event window.
Google trends data need validation: Comment on Durmuşoğlu (2017)
Published in Human and Ecological Risk Assessment: An International Journal, 2019
As access to the internet increases worldwide, data generated by search engines, social media, and other digital platforms have the potential to provide novel and interesting insights into patterns and trends of public interest in environmental topics (Ladle et al.2016; Sutherland et al.2018). In fact, there have been a number of studies taking advantage of such data to explore public interest in a number of environmental topics including climate change, ecosystem services, and biodiversity conservation (e.g., Anderegg and Goldsmith 2014; Correia et al.2018; Funk and Rusowsky 2014; Troumbis 2017a). Many of these studies take advantage of the Google Trends platform, which provides data on the relative volume of Google searches for a given term over a defined time-period and region of interest. Its quick adoption by the scientific community is perhaps best explained by ease of use: Google Trends data are readily available for collection (either through its on-line platform or dedicated programming interfaces), have high temporal (weekly to monthly) and spatial (from global to regional) resolution, are updated in near-real time, and cover a period of more than 10 years.