Search results

An ever-growing body of knowledge demonstrates the correlation among real-world phenomena and search query data issued on Google, as showed in the literature survey introduced in the following. The purpose of this paper is to introduce a pipeline, implemented as a web service, which, starting with recent Google Trends, allows a decision maker to monitor Twitter’s sentiment regarding these trends, enabling users to choose geographic areas for their monitors. In addition to the positive/negative sentiments about Google Trends, the pipeline offers the ability to view, on the same dashboard, the emotions that Google Trends triggers in the Twitter population. Such a set of tools, allows, as a whole, monitoring real-time on Twitter the feelings about Google Trends that would otherwise only fall into search statistics, even if useful. As a whole, the pipeline has no claim of prediction over the trends it tracks. Instead, it aims to provide a user with guidance about Google Trends, which, as the scientific literature demonstrates, is related to many real-world phenomena (e.g. epidemiology, economy, political science).

The proposed experimental framework allows the integration of Google search query data and Twitter social data. As new trends emerge in Google searches, the pipeline interrogates Twitter to track, also geographically, the feelings and emotions of Twitter users about new trends. The core of the pipeline is represented by a sentiment analysis framework that make use of a Bayesian machine learning device exploiting deep natural language processing modules to assign emotions and sentiment orientations to a collection of tweets geolocalized on the microblogging platform. The pipeline is accessible as a web service for any user authorized with credentials.

The employment of the pipeline for three different monitoring task (i.e. consumer electronics, healthcare, and politics) shows the plausibility of the proposed approach in order to measure social media sentiments and emotions concerning the trends emerged on Google searches.

The proposed approach aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

While relevant research has considered aggregated data from mobile devices and personal computers (PCs), tourists’ search patterns on mobile devices and PCs differ significantly. This study aims to explore whether decomposing aggregated search queries based on the terminals from which these queries are generated can enhance tourism demand forecasting.

Mount Siguniang, a national geopark in China, is taken as a case study in this paper; another case, Kulangsu in China, is used as the robustness check. The authors decomposed the total Baidu search volume into searches from mobile devices and PCs. Weekly rolling forecasts were used to test the roles of decomposed and aggregated search queries in tourism demand forecasting.

Search queries generated from PCs can greatly improve forecasting performance compared to those from mobile devices and to aggregate search volumes from both terminals. Models incorporating search queries generated via multiple terminals did not necessarily outperform those incorporating search queries generated via a single type of terminal.

Major players in the tourism industry, including hotels, tourist attractions and airlines, can benefit from identifying effective search terminals to forecast tourism demand. Industry managers can also leverage search indices generated through effective terminals for more accurate demand forecasting, which can in turn inform strategic decision-making and operations management.

This study represents one of the earliest attempts to apply decomposed search query data generated via different terminals in tourism demand forecasting. It also enriches the literature on tourism demand forecasting using search engine data.

Queries as a pioneering measure of public attention on various social issues have elicited considerable scholarly attention. The purpose of this paper is to address two fundamental questions, as follows: first, how do we identify niche queries that internet users search for on specific social issues?; and second, what are the measurement properties of queries data in gauging public attention on social issues?

The present study uses public attention on environmental issues in the USA as the empirical setting of research. An iterative framework is developed to identify niche queries to measure public attention on environmental issues. The measurement properties of queries data are assessed by comparing the dynamics of public attention on environmental issues captured by queries data with that measured by the “most important problem” (MIP) question in Gallup opinion polls.

A list of 39 niche queries that internet users search for on environmental issues is identified. The dynamics of public attention on environmental issues determined by the search trends of these 39 queries is found to positively correlate with that measured by Gallup MIP polls, whereas both dynamics can forecast each other well in a 12-month time frame.

The findings of the study possess methodological and practical implications. The study shows that queries data are complementary to, rather than substitutes of, public opinion polls in measuring public attention on environmental issues. The iterative framework developed in the study can be applied in future studies to help researchers identify valid queries to measure public attention on other social issues, as it can minimize researchers’ subjective biases in selecting search queries. Policymakers and environmentalists can utilize our approach to monitor the status of public attention on environmental issues and implement campaigns to mobilize favorable public opinion when the decline of public attention is predicted by the trends of web search queries.

Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.

Theoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.

The results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.

The results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.

To the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.

This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.

The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.

As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.

This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.

The purpose of this paper is to examine the feasibility of discovering business information from search engine query data. Specifically the study tried to determine whether search volumes of company names are correlated with the companies’ business performance and position data.

The top 50 US companies in the 2012 Fortune 500 list were included in the study. The following business performance and position data were collected: revenues, profits, assets, stockholders’ equity, profits as a percentage of revenues, and profits as a percentage of assets. Data on the search volumes of the company names were collected from Google Trends, which is based on search queries users enter into Google. Google Trends data were collected in the two scenarios of worldwide searches and US searches.

The study found significant correlations between search volume data and business performance and position data, suggesting that search engine query data can be used to discover business information. Google Trends’ worldwide search data were better than the US domestic search data for this purpose.

The study is limited to only one country and to one year of data.

Publicly available search engine query data such as those from Google Trends can be used to estimate business performance and position data which are not always publicly available. Search engine query data are timelier than business data.

This is the first study to establish a relationship between search engine query data and business performance and position data.

This study aims to propose an artificial neural network to identify automatically topic changes in a user session by using the statistical characteristics of queries, such as time intervals and query reformulation patterns.

A sample data log from the Norwegian search engine FAST (currently owned by Overture) is selected to train the neural network and then the neural network is used to identify topic changes in the data log.

A total of 98.4 percent of topic shifts and 86.6 percent of topic continuations were estimated correctly.

Content analysis of search engine user queries is an important task, since successful exploitation of the content of queries can result in the design of efficient information retrieval algorithms for search engines, which can offer custom‐tailored services to the web user. Identification of topic changes within a user search session is a key issue in the content analysis of search engine user queries.

The purpose of this paper is to investigate the search behavior of institutional repository (IR) users in regard to subjects as a means of estimating the potential impact of applying a controlled subject vocabulary to an IR.

Google Analytics data were used to record cases where users arrived at an IR item page from an external web search and subsequently downloaded content. Search queries were compared against the Faceted Application of Subject Terminology (FAST) schema to determine the topical nature of the queries. Queries were also compared against the item’s metadata values for title and subject using approximate string matching to determine the alignment of the queries with current metadata values.

A substantial portion of successful user search queries to an IR appear to be topical in nature. User search queries matched values from FAST at a higher rate than existing subject metadata. Increased attention to subject description in IR records may provide an opportunity to improve the search visibility of the content.

The study is limited to a particular IR. Data from Google Analytics does not provide comprehensive search query data.

The study presents a novel method for analyzing user search behavior to assist IR managers in determining whether to invest in applying controlled subject vocabularies to IR content.

The purpose of this paper is to investigate the usefulness of search query volume data in forecasting demand for hotel rooms and identify the best econometric forecasting model.

The authors used search volume data on five related queries to predict demand for hotel rooms in a specific tourist city and employed three ARMA family models and their ARMAX counterparts to evaluate the usefulness of these data. The authors also evaluated three widely used causal econometric models – ADL, TVP, and VAR – for comparison.

All three ARMAX models consistently outperformed their ARMA counterparts, validating the value of search volume data in facilitating the accurate prediction of demand for hotel rooms. When the three causal econometric models were included for forecasting competition, the ARX model produced the most accurate forecasts, suggesting its usefulness in forecasting demand for hotel rooms.

To demonstrate the usefulness of this data type, the authors focused on one tourist city with five specific tourist‐related queries. Future studies could focus on other aspects of tourist consumption and on more destinations, using a larger number of queries to increase accuracy.

Search volume data are an early indicator of travelers' interest and could be used to predict various types of tourist consumption and activities, such as hotel occupancy, spending, and event attendance.

The paper's findings validate the value of search query volume data in predicting hotel room demand, and the paper is the first of its kind in the field of tourism and hospitality research.

The application of “Google” econometrics (Geco) has evolved rapidly in recent years and can be applied in various fields of research. Based on accepted theories in existing economic literature, this paper seeks to contribute to the innovative use of research on Google search query data to provide a new innovative to property research.

In this study, existing data from Google Insights for Search (GI4S) is extended into a new potential source of consumer sentiment data based on visits to a commonly‐used UK online real‐estate agent platform (Rightmove.co.uk). In order to contribute to knowledge about the use of Geco's black box, namely the unknown sampling population and the specific search queries influencing the variables, the GI4S series are compared to direct web navigation.

The main finding from this study is that GI4S data produce immediate real‐time results with a high level of reliability in explaining the future volume of transactions and house prices in comparison to the direct website data. Furthermore, the results reveal that the number of visits to Rightmove.co.uk is driven by GI4S data and vice versa, and indeed without a contemporaneous relationship.

This study contributes to the new emerging and innovative field of research involving search engine data. It also contributes to the knowledge base about the increasing use of online consumer data in economic research in property markets.