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Review Article
Survey Paper on Google Search Analysis Using Machine Learning
Vaastav L Sanghvi1
Vaseem Akram B2
Pratham Chaithanya K A3
1 2 3 Department of Computer Science Engineering in Data Science, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India.
Published Online: September-December 2025
Pages: 34-38
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403007
References
1. Abu Rayhan, "Exploring the Power of Google Trends: Applications, Limitations, and Future Directions," CBECL, 2024.
2. Alessandro Rovetta, "Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19," Mensana srls and Redeev srl, 2021.
3. Hyun young Choi and Hal R. Varian, "Predicting the Present with Google Trends," Google Inc., 2012.
4. Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant, "Detecting Influenza Epidemics Using Search Engine Query Data," Google.org and CDC, 2009.
5. Simeon Vosen and Torsten Schmidt, "Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends," RWI – Leibniz Institute for Economic Research, 2011.
6. Ari Seifter, Alison Schwarzwalder, Kate Geis, and John Aucott, "The Utility of Google Trends for Epidemiological Research: Lyme Disease as an Example," Johns Hopkins University, 2010.
7. Herman Anthony Carneiro and Eleftherios Mylonakis, "Google Trends: A Web-Based Tool for Real-Time Surveillance of Disease Outbreaks," Warren Alpert Medical School of Brown University, 2009.
8. Minh Nhat Tran, Lin Luo, and Sajal K. Das, "A Deep Learning Framework for Understanding Search Behavior from Google Trends Data," IEEE Transactions on Big Data, vol. 7, no. 2, pp. 389–402, 2021.
9. Yun Liu, Qian Zhao, and Jing Zhang, "Forecasting COVID-19 Cases Using Google Trends and Machine Learning: A Hybrid Model Approach," Scientific Reports, vol. 11, no. 1, 2021.
10. Artemis Lampos, Elad Yom-Tov, Yoram Cox, and Ian Thapen, "Enhancing Feature Selection Using Google Trends Data to Predict Influenza-like Illness,"Journal of Biomedical Informatics, vol. 49, pp. 148–158, 2014.
11. David Preis, Helen Susannah Moat, H. Eugene Stanley, and Steven R. Bishop, "Quantifying the Advantage of Looking Forward Using Google Trends," Scientific Reports, vol. 2, 2012.
12. Ryen W. White, Patrick Pantel, Susan T. Dumais, Jaime Teevan, and Yandong Wang, "Characterizing the Influence of Domain Expertise on Web Search Behavior," in Proceedings of the 2nd ACM International Conference on Web Search and Data Mining (WSDM), 2009, pp. 132–141.
13. Anh Tuan Nguyen, Dinh Phung, Svetha Venkatesh, and Michael Berk, "Affective and Content Analysis of Online Depression Communities," IEEE Transactions on Affective Computing, vol. 5, no. 3, pp. 217–226, 2014.
14. David Lazer, Ryan Kennedy, Gary King, and Alessandro Vespignani, "The Parable of Google Flu: Traps in Big Data Analysis," Science, vol. 343, no. 6176,pp. 1203–1205, 2014.
15. David Garcia-Gasulla, Ferran Parés, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, and Toyotaro Suzumura, "An Approach to Web Search Behavior Modeling Using Machine Learning and Large-Scale Search Engine Data," Information Processing & Management, vol. 56, no. 3, 2019.
2. Alessandro Rovetta, "Reliability of Google Trends: Analysis of the Limits and Potential of Web Infoveillance During COVID-19," Mensana srls and Redeev srl, 2021.
3. Hyun young Choi and Hal R. Varian, "Predicting the Present with Google Trends," Google Inc., 2012.
4. Jeremy Ginsberg, Matthew H. Mohebbi, Rajan S. Patel, Lynnette Brammer, Mark S. Smolinski, and Larry Brilliant, "Detecting Influenza Epidemics Using Search Engine Query Data," Google.org and CDC, 2009.
5. Simeon Vosen and Torsten Schmidt, "Forecasting Private Consumption: Survey-Based Indicators vs. Google Trends," RWI – Leibniz Institute for Economic Research, 2011.
6. Ari Seifter, Alison Schwarzwalder, Kate Geis, and John Aucott, "The Utility of Google Trends for Epidemiological Research: Lyme Disease as an Example," Johns Hopkins University, 2010.
7. Herman Anthony Carneiro and Eleftherios Mylonakis, "Google Trends: A Web-Based Tool for Real-Time Surveillance of Disease Outbreaks," Warren Alpert Medical School of Brown University, 2009.
8. Minh Nhat Tran, Lin Luo, and Sajal K. Das, "A Deep Learning Framework for Understanding Search Behavior from Google Trends Data," IEEE Transactions on Big Data, vol. 7, no. 2, pp. 389–402, 2021.
9. Yun Liu, Qian Zhao, and Jing Zhang, "Forecasting COVID-19 Cases Using Google Trends and Machine Learning: A Hybrid Model Approach," Scientific Reports, vol. 11, no. 1, 2021.
10. Artemis Lampos, Elad Yom-Tov, Yoram Cox, and Ian Thapen, "Enhancing Feature Selection Using Google Trends Data to Predict Influenza-like Illness,"Journal of Biomedical Informatics, vol. 49, pp. 148–158, 2014.
11. David Preis, Helen Susannah Moat, H. Eugene Stanley, and Steven R. Bishop, "Quantifying the Advantage of Looking Forward Using Google Trends," Scientific Reports, vol. 2, 2012.
12. Ryen W. White, Patrick Pantel, Susan T. Dumais, Jaime Teevan, and Yandong Wang, "Characterizing the Influence of Domain Expertise on Web Search Behavior," in Proceedings of the 2nd ACM International Conference on Web Search and Data Mining (WSDM), 2009, pp. 132–141.
13. Anh Tuan Nguyen, Dinh Phung, Svetha Venkatesh, and Michael Berk, "Affective and Content Analysis of Online Depression Communities," IEEE Transactions on Affective Computing, vol. 5, no. 3, pp. 217–226, 2014.
14. David Lazer, Ryan Kennedy, Gary King, and Alessandro Vespignani, "The Parable of Google Flu: Traps in Big Data Analysis," Science, vol. 343, no. 6176,pp. 1203–1205, 2014.
15. David Garcia-Gasulla, Ferran Parés, Eduard Ayguadé, Jesús Labarta, Ulises Cortés, and Toyotaro Suzumura, "An Approach to Web Search Behavior Modeling Using Machine Learning and Large-Scale Search Engine Data," Information Processing & Management, vol. 56, no. 3, 2019.
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