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18 results for “Google Trends”
Google Trends and Wikipedia Page Views
<p><strong>Abstract</strong> (our paper)</p> <p>The frequency of a web search keyword generally reflects the degree of public interest in a particular subject matter. Search logs are therefore useful resources for trend analysis. However, access to search logs is typically restricted to search engine providers. In this paper, we investigate whether search frequency can be estimated from a different resource such as Wikipedia page views of open data. We found frequently searched keywords to have remarkably high correlations with Wikipedia page views. This suggests that Wikipedia page views can be an effective tool for determining popular global web search trends.</p> <p><strong>Data</strong></p> <p>personal-name.txt.gz:<br> The first column is the Wikipedia article id, the second column is the search keyword, the third column is the Wikipedia article title, and the fourth column is the total of page views from 2008 to 2014.</p> <p>personal-name_data_google-trends.txt.gz, personal-name_data_wikipedia.txt.gz:<br> The first column is the period to be collected, the second column is the source (Google or Wikipedia), the third column is the Wikipedia article id, the fourth column is the search keyword, the fifth column is the date, and the sixth column is the value of search trend or page view.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Mitsuo Yoshida, Yuki Arase, Takaaki Tsunoda, Mikio Yamamoto. Wikipedia Page View Reflects Web Search Trend. <em>Proceedings of the 2015 ACM Web Science Conference (WebSci '15)</em>. no.65, pp.1-2, 2015.<br> http://dx.doi.org/10.1145/2786451.2786495<br> http://arxiv.org/abs/1509.02218 (author-created version)</p> <p><strong>Note</strong></p> <p>The raw data of Wikipedia page views is available in the following page.<br> http://dumps.wikimedia.org/other/pagecounts-raw/</p>
Immigration Trends Using Google Data
<p>For a country to efficiently monitor international migration, quick access to information on migration flows is helpful. However, traditional data sources fail to provide immediate information on migration flows and do not facilitate the correct anticipation of these flows in the short term. To tackle this issue, this paper evaluates the predictive capacity of big data to estimate the current level or to predict short-term flows. The results show that Google Trends can provide information that reflects the attractiveness of Switzerland for to immigrants from different countries and predict, to some extent, current and future (short-term) migration flows of adults arriving from Spain or Italy. However, the predictions appear not to be satisfactory for other flows<br> (from France and Germany). Additional studies based on alternative approaches are needed to validate or overturn our study results.</p> <p>Data and codebook along DDI standard</p>
Replication File for Paper "The Role of Time, Weather and Google Trends in Understanding and Predicting Web Survey Response"
<p>This updated zip file contains the necessary codes and data files to reproduce all the findings presented in the accepted manuscript "The Role of Time, Weather and Google Trends in Understanding and Predicting Web Survey Response" by Qixiang Fang, Joep Burger, Ralph Meijers and Kees van Berkel. </p> <p>Compared with the last one, this new version provides more instructions in the README.txt files and indicates the exact versions of R, R studio and the R packages used.</p>
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
As search engines have become the main information resources of our daily life, studies about search behavior on the internet have gained great popularity with the growing knowledge of how the search behavior itself can affect our daily decisions, e.g. what to purchase, where to travel and even how to define beauty. However, there is no consensus conclusion whether the search behavior itself or the linguistic meaning behind it that can affect their decision. After analyzing the linguistic meanings of 13,915 English words obtained from Google Trends and its profit gained from the US house market by automatic transactions. It is found that linguistic meanings can affect financial decision results as word clusters with supervised machine learning methods.
Google Trends normalized hits for COVID-19 generated in Switzerland
<p>This dataset contains the normalized hits of the queries on COVID-19 generated in Switzerland between July 2019 and July 2020, the timeline of the normalized hits per Canton, the top associated queries, the frequency analysis of the top associated queries, the categorization of the top associated queries, and the rising queries. </p> <p>Query: Coronavirus + covid + 2019-nCoV + SARS-CoV2</p> <p>Query type: keyword</p> <p>Timeframe: 01/01/2020 - 11/06/2021</p> <p>Date of search: 22/06/2021</p> <p>Data source: Web searches</p> <p>Location:CH (by canton)</p> <p>Query category:all</p> <p>Rationale for keywords: main semi-synonims for covid-19</p> <p>This dataset has been generated by and used for the <a href="https://www.ibme.uzh.ch/en/Biomedical-Ethics/Research/Ongoing-Research/Public-Health-Ethics/PubliCo.html">PubliCo research project</a>.</p>
Global Google Trends Internet Search Data 2022 to 2024
<p>Global Google Trends Internet Search Data 2022 to 2024 [RSVs] for fasting, diet, nutrition, liver, GLP-1 RAs</p>
Twitter and Google Trend data about heat waves in India 2010-2017
<p>The dataset contains:</p> <p>1) The list of tweets corresponding to the keywords "heat wave india" and "heatwave india" between 2010 and 2017.</p> <p>2) The daily count of the same tweets</p> <p>3) The monthly Google Trends data corresponding to the keywords "heat wave", "heatwave", "heat wave india", and "heatwave india" limited to the searches from India in the period 2010-2017</p> <p>The Twitter data has been obtained wth the Python package Get-Old-Tweets (https://github.com/Jefferson-Henrique/GetOldTweets-python); the Google Trends data are obtained from the Google Trends webpage (https://trends.google.com/trends/?geo=US).</p>
Google search trends for different vaccines from 1st June 2014 to 31st May 2019
<p>This is "Additional file" for the manuscript "Global search trends on common vaccine-related information in English on the internet"</p>
Search Behavior can Affect Financial Decision Results: A Behavior Study of Google Trends Data and Linguistic Scale
Open the record for dataset details and reuse information.
Interesse de pesquisa sobre podcast no Google Trends
<p>Tivemos a comprovação do aumento pelo interesse em <em>podcasts </em>ao realizarmos uma pesquisa no Google Trends, ferramenta amplamente utilizada por jornalistas e profissionais que atuam no mercado de <em>Search Engine Optimization</em> (SEO), a qual nos permitiu constatar, através da busca pela palavra ‘<em>podcast</em>’ no Brasil, considerando o recorte temporal dos últimos três anos (de janeiro de 2017 a dezembro de 2019), que a ascensão de pesquisa por ‘<em>podcast’</em> no Brasil deu-se entre os dias 25 e 31 de agosto de 2019, período que coincidiu com o lançamento do <em>podcast</em> O Assunto, mediado pela jornalista Renata Lo Prete, do portal de notícias G1, cujo primeiro episódio (denominação atribuída, por padrão, a cada áudio do <em>podcast</em>) foi publicado no dia 26 de agosto.</p> <p>Ainda com base em pesquisa no Google Trends, em janeiro de 2020, constatamos que o Ceará ocupava a 10ª posição em interesse de pesquisa por <em>podcast</em>. Além desses dados, o Google Trends nos apresentou os assuntos e as consultas relacionadas, as quais indicaram que os usuários que pesquisaram por ‘<em>podcast’</em> também pesquisaram por outros temas, dentre eles, o significado da palavra e como instalar o Spotify.</p>
Could Google Trends be used to predict methamphetamine-related crime? An analysis of search volume data in Switzerland, Germany, and Austria
<p>Data for paper submitted to PLoS One on 2016-07-08. Title: Could Google Trends be used to predict methamphetamine-related crime? An analysis of search volume data in Switzerland, Germany, and Austria</p> <p>Authors: Alex Gamma, Roman Schleifer, Wolfgang Weinmann, Anna Buadze, Michael Liebrenz</p> <p>Format: ZIP-file</p> <p>Contains:<br /> - Two datafiles, each as .csv and .dta (Stata version 11) file.<br /> - README file with instructions</p> <p> </p> <p> </p>
Interest in insect die-off and intention for action using Google trends
<p><span>1. The publication of "More than 75 percent decline over 27 years in total flying insect biomass in protected areas" by Hallmann et al. in October 2017 gained vast media coverage in Germany. The insect crisis as conservation topic has received little attention among the public before, but since media influences people's awareness, we investigated i) whether the study publication induced </span><span>increased awareness among the German public for insect die-off, and ii) whether it contributed to people's intentions to undertake insect protecting actions. </span></p> <p><span>2. We used Google Trends to examine the people's internet activity in terms of keywords relevant to our research question.</span></p> <p><span>3. A high peak in Google searches for insect die-off (Insektensterben) was indeed visible just after the study publication, and search volume remained significantly higher for the following six months, confirming that the topic gained attention. </span></p> <p><span>4. Searches for the three keywords insect hotel, bee friendly and bee meadow increased significantly over the summers of the years 2017 to 2019. This suggests that intentions to undertake these simple insect protecting actions rose as well. The results propose that media should use the window of opportunity opened by shocking news about a crisis to spread information on feasible counteractions. </span></p> <p><span>5. </span><span>Due to the prevailing topicality in the media and the already increased awareness and willingness to action among the population, conservation organizations can take advantage of the situation by communicating practical conservation measures to the general public in cooperation with media agencies or via own channels such as press releases and social media campaigns.</span></p> <div> <div> <div class="msocomtxt"></div> </div> </div>
Google Trends time series for the term "topic modeling"
<p>Dataset received from Google Trends for the phrase "topic modeling'' on 31 January 2024 using the URL <a href="https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de">https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de</a></p> <p><em>Data obtained from Google LLC, which is the ultimate owner of these data. Published for academic and non-commercial replication purposes only.</em></p>
Google Trend Enhanced Deep Learning Dataset for Renewable Energy Asset Price Prediction
<h3>Overview</h3> <p>This dataset accompanies the research paper titled <strong>“<a href="https://doi.org/10.1016/j.knosys.2024.112733">A Google Trend Enhanced Deep Learning Model for the Prediction of Renewable Energy Asset Price</a>”</strong> by Dr. Nachiketa Mishra, Dr. Lalatendu Mishra, Balaji Dinesh, P M Kavyassree . The study investigates the predictive efficiency of various forecasting models using oil prices and investor sentiment for renewable energy assets, specifically focusing on renewable energy ETFs such as ICLN, PBD, and QCLN.</p> <p>The dataset contains the processed inputs and raw data used in the analysis, including sentiment indices derived from Google Trends and traditional financial indices.</p> <h3>Citation :</h3> <p>Please cite this dataset as:</p> <ul> <li>Mishra, L., Dinesh, B., Kavyassree, P.M. and Mishra, N., 2024. A Google Trend enhanced deep learning model for the prediction of renewable energy asset price. <em>Knowledge-Based Systems</em>, p.112733.</li> </ul> <pre><code>@bibtex<br><br>@article{MISHRA2025112733,<br>title = {A Google Trend enhanced deep learning model for the prediction of renewable energy asset price},<br>journal = {Knowledge-Based Systems},<br>volume = {308},<br>pages = {112733},<br>year = {2025},<br>issn = {0950-7051},<br>doi = {https://doi.org/10.1016/j.knosys.2024.112733},<br>url = {https://www.sciencedirect.com/science/article/pii/S0950705124013674},<br>author = {Lalatendu Mishra and Balaji Dinesh and P.M. Kavyassree and Nachiketa Mishra},<br>}</code><code><br></code></pre> <h2>Code : </h2> <p>Refer Repository URL provided</p> <h2>Directory Structure and Description</h2> <pre><code>📦 data ├── 📂 etf-data │ ├── 📜 ICLN_INPUT.csv # Input data for ICLN │ ├── 📜 PBD_INPUT.csv # Input data for PBD │ ├── 📜 QCLN_INPUT.csv # Input data for QCLN │ └── 📂 raw-data # Original unprocessed data │ ├── 📂 market-data # ETF market prices and oil volatility (OVX) │ ├── 📂 navs # Net Asset Value (NAV) data │ └── 📂 volatility # Volatility data (GARCH and Moving Average models) ├── 📂 google-trends │ ├── 📜 keys.txt # Keywords for Google Trends search │ ├── 📂 trends │ ├── 📂 first-principal-components # Final Google Trend Index (PCA) │ ├── 📂 formatted-trends # Cleaned trends data │ └── 📂 raw-google-trends # Raw fetched Google Trends data</code></pre> <pre>Key Files</pre> <ul> <li><strong>ICLN_INPUT.csv</strong>, <strong>PBD_INPUT.csv</strong>, <strong>QCLN_INPUT.csv</strong>: Processed inputs for the prediction models of each ETF.</li> <li><strong>raw-data</strong>: Contains original data for market prices, NAVs, and volatility measures (GARCH, Moving Average).</li> <li><strong>google-trends</strong>: Data related to Google search trends, including raw, formatted, and the final index derived using Principal Component Analysis (PCA).</li> </ul> <h3>Usage Notes</h3> <ol> <li><strong>Google Trends Data</strong>: The Google Trend Index constructed from the keywords can be found in the <code>first-principal-components</code> folder. This index was a key input in the predictive models and used to construct modified indices in data>*_INPUT.csv’s.</li> <li><strong>Reproducibility</strong>: For reproducing the results from the study, you can directly use the inputs provided under <code>/data</code> to build predictive models.</li> <li><strong>Modifications</strong>: If you aim to modify or extend the dataset, be cautious of the index construction process, particularly around Principal Component Analysis (PCA) in the Google Trends data.</li> </ol> <h2>License</h2> <p>This dataset is released under the <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong> license. You are free to share and adapt the data, provided appropriate credit is given.</p> <h2>Contact Information</h2> <p>For any questions or further information, please contact:</p> <ul> <li><strong>Dr. Nachiketa Mishra</strong>: Department of Mathematics, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India</li> <li><strong>Dr. Lalatendu Mishra</strong>: Department of Management Sciences, Indian Institute of Technology Kanpur, India</li> <li><strong>Balaji Dinesh</strong>: Department of Computer Science, Indian Institute of Information Technology Design and Manufacturing Kancheepuram, India. email : <a href="mailto:balajidinesh918@gmail.com">balajidinesh918@gmail.com</a></li> </ul>
Searches for ED and COVID-19 from Google Trends
<p><em>Searches for ED and COVID-19 from Google Trends (provided by Google as Relative Search Volumes (RSVs): they are normalized with a minimum value of 0 and a maximum value of 100) and weekly COVID-19 cases from WHO (in absolute numbers) in the four time periods. Please note that in the first time period Google provides search data RSVs monthly, while in the other three periods this is done weekly. Time Period 1: January 1, 2004 to December 31, 2016, Time Period 2: January 1, 2017 to December 31, 2019, Time Period 3: January 1, 2020 to December 31, 2021, Time Period 4: January 1, 2022 to December 31, 2022. </em></p>
Interest in insect die-off and intention for action using Google trends
Open the record for dataset details and reuse information.
Kohls Google Trend dataset
<p>Dataset taken from https://sites.google.com/site/timeserieschain/home/Kohls_data.mat?attredirects=0&revision=1 who adopted it from http://www.www2015.it/documents/proceedings/proceedings/p721.pdf .</p> <p>Stored on Zenodo as backup for Stumpy Time Series Chains tutorial https://stumpy.readthedocs.io/en/latest/Tutorial_Time_Series_Chains.html.</p> <p> </p>
Google Trends of Global Surgery
ClinicalTrials.gov study NCT05012085. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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