![]() Xu, Q., Bo, Z., Jiang, C., Liu, Y.: Does google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility. Vosen, S., Schmidt, T.: Forecasting private consumption: survey-based indicators vs. ![]() Verma, M., Kishore, K., Kumar, M., Sondh, A.R., Aggarwal, G., Kathirvel, S.: Google search trends predicting disease outbreaks: an analysis from India. Smuts, N.: What drives cryptocurrency prices? An investigation of google trends and telegram sentiment. Preis, T., Moat, H.S., Stanley, H.E.: Quantifying trading behavior in financial markets using google trends. Lee, K., Hoti, K., Hughes, J.D., Emmerton, L.: Dr google and the consumer: a qualitative study exploring the navigational needs and online health information-seeking behaviors of consumers with chronic health conditions. Kondo, K., Ishikawa, A., Kimura, M.: Sequence to sequence with attention for influenza prevalence prediction using google trends. Kinney, M.O., Brigo, F.: What can google trends and wikipedia-pageview analysis tell us about the landscape of epilepsy surgery over time? Epilepsy Behav. Husnayain, A., Fuad, A., Su, E.C.Y.: Applications of google search trends forrisk communication in infectious disease management: a case study of covid-19 outbreak in Taiwan. Huang, M.Y., Rojas, R.R., Convery, P.D.: Forecasting stock market movements using google trend searches. Hu, H., Tang, L., Zhang, S., Wang, H.: Predicting the direction of stockmarkets using optimized neural networks with google trends. Höpken, W., Eberle, T., Fuchs, M., Lexhagen, M.: Google trends data for analysing tourists’ online search behaviour and improving demand forecasting: the case of Åre, Sweden. 30(2), 205–222 (2019)Ĭrowson, M.G., Witsell, D., Eskander, A.: Using google trends to predict pediatric respiratory syncytial virus encounters at a major health care system. 27(2), 91–103 (2019)Ĭonnor, P., Sarafidis, V., Zyphur, M.J., Keltner, D., Chen, S.: Income inequality and white-on-black racial bias in the united states: evidence from project implicit and google trends. Global Health 7(3), 185–189 (2017)Ĭhumnumpan, P., Shi, X.: Understanding new products’ market performance using google trends. In: Big Data 2019 (2019)Ĭervellin, G., Comelli, I., Lippi, G.: Is google trends a reliable tool for digital epidemiology? Insights from different clinical settings. Īlsmadi, I., Al-Abdullah, M., Alsmadi, H.: Popular search terms and stock price prediction. KeywordsĪguilera, A.M., Fortuna, F., Escabias, M., Di Battista, T.: Assessing socialinterest in burnout using google trends data. ![]() When working with Google Trends data, users must be aware of the marked risks associated with the inconsistencies in the samples. We found that beyond search volume and timespan, there are additional factors for the deviations that can only be explained by Google itself. In our experiments, the representativity claimed by the service was disproved in many cases. Our work demonstrates that the inconsistencies in Google Trends Data and the resulting contradictions in analyses and predictions are systematic and particularly large when analyzing timespans of eights months or less. In this paper, we develop an experimental setup to estimate and measure possible variation in service results for the example of Google Trends. Often, limited or no information about the reliability and validity of the service or the sampling confidence are provided by the services and, as a consequence, the data quality has to be assured by the users themselves, before using it for further analysis. The representativity of such samples is typically assured by the providers. The data providers often use sampling when calculating the request results, due to the huge data volume that would need to be processed otherwise. Search analytics and trends data is widely used by media, politicians, economists, and scientists in various decision-making processes.
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