scholarly journals Can crowdsourced data help to optimize atopic dermatitis treatment? Comparing web search data and environmental data in Germany

2021 ◽  
Author(s):  
A. Mick ◽  
L. Tizek ◽  
M. Schielein ◽  
A. Zink
Keyword(s):  
Atopic Dermatitis ◽  
Web Search ◽  
Environmental Data ◽  
Crowdsourced Data ◽  
Search Data

Scientists Mine Web Search Data to Identify Epidemics and Adverse Events

JAMA ◽  
2013 ◽  
Vol 309 (18) ◽  
pp. 1883 ◽  
Author(s):  
Bridget M. Kuehn
Keyword(s):  
Adverse Events ◽  
Web Search ◽  
Search Data

scholarly journals Future orientation and fertility: cross-national evidence using Google search

2020 ◽  
Author(s):  
Nicolò Cavalli
Keyword(s):  
Web Search ◽  
Future Orientation ◽  
Fertility Rates ◽  
Fertility Decisions ◽  
Country Level ◽  
The World ◽  
Search Data ◽  
Quality Framework ◽  
Google Search ◽  
Cross National

Using digital traces to investigate demographic behaviours, I leverage in this paper aggregated web search data to develop a Future Orientation Index for 200 countries and territories across the world. This index is expressed as the ratio of Google search volumes for ‘next year’ (e.g., 2021) to search volumes for ‘current year’ (e.g., 2020), adjusted for country-level internet penetration rates. I show that countries with lower levels of future orientation also have higher levels of fertility. Fertility rates decrease quickly as future orientation levels increase; but at the highest levels of future orientation, this correlation flattens out. Theoretically, I reconstruct the role that varying degrees of future orientation might play in fertility decisions by incorporating advances in behavioural economics into a traditional quantity-quality framework à la Becker.

scholarly journals The Study on China’s Flu Prediction Model Based on Web Search Data

2018 ◽  
Vol 06 (03) ◽  
pp. 79-92
Author(s):  
Yan Bu ◽  
Jinhong Bai ◽  
Zhuo Chen ◽  
Mingjing Guo ◽  
Fan Yang
Keyword(s):  
Prediction Model ◽  
Web Search ◽  
Model Based ◽  
Search Data

scholarly journals Integration of Crowdsourced Images, USGS Networks, Remote Sensing, and a Model to Assess Flood Depth during Hurricane Florence

2020 ◽  
Vol 12 (5) ◽  
pp. 834
Author(s):  
Carolynne Hultquist ◽  
Guido Cervone
Keyword(s):  
Remote Sensing ◽  
Sensor Networks ◽  
Cross Sections ◽  
Satellite Remote Sensing ◽  
Environmental Data ◽  
Data Sources ◽  
Remote Sensing Imagery ◽  
Flood Depth ◽  
Crowdsourced Data ◽  
Automated Methods

Crowdsourced environmental data have the potential to augment traditional data sources during disasters. Traditional sensor networks, satellite remote sensing imagery, and models are all faced with limitations in observational inputs, forecasts, and resolution. This study integrates flood depth derived from crowdsourced images with U.S. Geological Survey (USGS) ground-based observation networks, a remote sensing product, and a model during Hurricane Florence. The data sources are compared using cross-sections to assess flood depth in areas impacted by Hurricane Florence. Automated methods can be used for each source to classify flooded regions and fuse the dataset over common grids to identify areas of flooding. Crowdsourced data can play a major role when there are overlaps of sources that can be used for validation as well providing improved coverage and resolution.

Persistent Interest Portfolios: Marrying Web Search Data with Mean–Variance Theory

2016 ◽  
Vol 25 (3) ◽  
pp. 135-141
Author(s):  
Daniel Nadler ◽  
Anatoly B. Schmidt
Keyword(s):  
Web Search ◽  
Search Data ◽  
Mean Variance

scholarly journals Revisiting the use of web search data for stock market movements

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Xu Zhong ◽  
Michael Raghib
Keyword(s):  
Web Search ◽  
Selection Process ◽  
Adaptive Strategy ◽  
Computational Social Science ◽  
Search Activity ◽  
Term Selection ◽  
Search Terms ◽  
Search Data ◽  
Market Trends ◽  
The Cost

Abstract Advances in Big Data make it possible to make short-term forecasts for market trends from previously unexplored sources. Trading strategies were recently developed by exploiting a link between the online search activity of certain terms semantically related to finance and market movements. Here we build on these earlier results by exploring a data-driven strategy which adaptively leverages the Google Correlate service and automatically chooses a new set of search terms for every trading decision. In a backtesting experiment run from 2008 to 2017 we obtained a 499% cumulative return which compares favourably with benchmark strategies. A crowdsourcing exercise reveals that the term selection process preferentially selects highly specific terms semantically related to finance (e.g. Wells Fargo Bank), which may capture the transient interests of investors, but at the cost of a shorter span of validity. The adaptive strategy quickly updates the set of search terms when a better combination is found, leading to more consistent predictability. We anticipate that this adaptive decision framework can be of value not only for financial applications, but also in other areas of computational social science, where linkages between facets of collective human behavior and online searches can be inferred from digital footprint data.

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