Īnother trend rising is the measurement of the change in interest in controversial issues and in drug-related subjects, such as searches in prescription or illicit drugs. Up to this point, several topics have been examined, such as epilepsy, cancer, thrombosis, silicosis, and various medical procedures including cancer screening examinations, bariatric surgery, and laser eye surgery. Īs the use of Google Trends in examining human behavior is relatively novel, new methods of assessing Google health data are constantly arising. Over the past decade, the field of infodemiology has been shown to be highly valuable in assessing health topics, retrieving web-based data from, for example, Google, Twitter, social media, or combinations of ≥2 Web-based data sources. These data have the advantage of being real time, thus tackling the issue of long periods of delay from gathering data to analysis and forecasting. The monitoring and analysis of internet data fall under the research field of infodemiology, that is, employing data collected from Web-based sources aiming at informing public health and policy. Data from Google Trends have been shown to be valuable in predictions, detection of outbreaks, and monitoring interest, as detailed below, while such applications could be analyzed and evaluated by government officials and policy makers to deal with various health issues and disease occurrence. Researchers have placed a significant focus on examining Web-based search queries for health and medicine related topics. Healthcare is one of the fields in which big data are widely applied, with the number of publications in this field showing a high increase. Different regions that show the same number of searches for a term will not always have the same total search volumes ”. The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics. Otherwise places with the most search volume would always be ranked highest. Data are downloaded from the Web in “.csv” format and are adjusted as follows: “ Search results are proportionate to the time and location of a query: Each data point is divided by the total searches of the geography and time range it represents, to compare relative popularity. In addition, different terms in different regions can be compared simultaneously. Google Trends shows the changes in online interest for time series in any selected term in any country or region over a selected time period, for example, a specific year, several years, 3 weeks, 4 months, 30 days, 7 days, 4 hours, 1 hour, or a specified time-frame. The monitoring of Web-based activity is a valid indicator of public behavior, and it has been effectively used in predictions, nowcastings, and forecasting. Therefore, great potential arises from using Web-based queries to examine topics and issues that would have been difficult or even impossible to explore without the use of big data. Google Trends provides the field of big data with new opportunities, as it has been shown to be valid and has been proven valuable, accurate, and beneficial for forecasting. Online search traffic data have been suggested to be a good analyzer of internet behavior, while Google Trends acts as a reliable tool in predicting changes in human behavior subject to careful selection of the searched-for terms, Google data can accurately measure the public’s interest. The most popular tool for analyzing behavior using Web-based data is Google Trends. As internet penetration is continuously increasing, the use of search traffic data, social media data, and data from other Web-based sources and tools can assist in facilitating a better understanding and analysis of Web-based behavior and behavioral changes. The analysis of online search queries has been of notable popularity in the field of big data analytics in academic research. Big data have shown great potential in forecasting and better decision making though handling these data with conventional ways is inadequate, they are being continuously integrated in research with novel approaches and methods. Big data are characterized by the 8 Vs : volume (exponentially increasing volumes), variety (wide range of datasets), velocity (high processing speed), veracity, value, variability, volatility, and validity.
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