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Software . 2023
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Software . 2023
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Source code for "Enhancing the predictive power of Google Trends data through network analysis: An infodemiology study of COVID-19"

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 27 Jun 2023 English Publisher:Zenodo
Authors: Chu, Amanda M. Y.; Chong, Andy C. Y.; Lai, H. T. Nick; Tiwari, Agnes; So, Mike K. P.;

doi: 10.5281/zenodo.8084831 , 10.5281/zenodo.8084832

Source code for "Enhancing the predictive power of Google Trends data through network analysis: An infodemiology study of COVID-19"

Abstract

This code repository contains the Python scripts to reproduce the results in the paper "Enhancing the predictive power of Google Trends data through network analysis: An infodemiology study of COVID-19" by Chu, Chong, Lai, Tiwari and So. For latest documentation, please refer to https://github.com/social-data-analytics/covid-19-google-trends-network

If you use this software, please cite it as below.

Keywords

Internet search volumes, infoveillance, network connectedness, Google Trends, network analysis, infodemiology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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