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Data · dataset · 2026

<p>Correlation between search terms, infection cases, and media mentions.</p>

Listed in DMU Figshare and UCL Research Data Repository — shown once because both records carry DOI 10.1371/journal.pone.0358063.g003

<p>a) and b) show the Pearson correlation between each cluster and infection cases (left) and media mentions (right) during the pandemic period for both the flu (top) and COVID-19 (bottom).

Description

In contrast, c) and d) present the Linear Regression model’s <i>R</i><sup>2</sup> performance. The asterisks represent the p-value of a <i>t</i>-test with the null hypothesis that mean correlation is zero (*0.01 < <i>p</i> < 0.05; **0.001 < <i>p</i> < 0.01; ***<i>p</i> < 0.001; ns <i>p</i> > 0.05).

In a) and b), correlations can be both positive and negative, indicating whether search volumes increase or decrease with cases or media mentions. In c) and d), however, only the absolute correlation values are considered, as predictive power depends on correlation strength rather than direction. Also, c) and d) illustrate the cumulative contribution of individual search terms to the model’s performance.

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Terms are added sequentially from the most to the least correlated with infection cases (left to right, in <i>Cases</i> panels) and from the least to the most correlated with media mentions (left to right, in <i>Media</i> panels). The black line represents the Linear Regression <i>R</i><sup>2</sup>, showing how predictive performance evolves as new terms are added, while the dashed line shows the <i>R</i><sup>2</sup> obtained for C1 as a reference.

We focus on the Linear Regression model here as it provides a more interpretable baseline for assessing the contribution of different feature sets. The colors of the vertical bars correspond to the clusters each term belongs to, allowing a comparison of cluster contributions.</p>

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Where it is published

Catalogue records · 1

Topics

Provenance · 2 source records, 17 field assertions
SourceKeyLast seenRaw
DMU Figshareoai:figshare.com:article/3404844110 d agoJSON v1
UCL Research Data Repositoryoai:figshare.com:article/3404844110 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0
concepts[disease].local:disease:cancermapping · rdr ucl ac ukvocabulary-mapper@1.0.0keywords['Cancer']
concepts[disease].local:disease:cancermapping · figshare dmu ac ukvocabulary-mapper@1.0.0keywords['Cancer']
concepts[field].anzsrc:field:320211mapping · rdr ucl ac ukvocabulary-mapper@1.0.0keywords['Infectious Diseases']
concepts[field].anzsrc:field:320211mapping · figshare dmu ac ukvocabulary-mapper@1.0.0keywords['Infectious Diseases']
concepts[field].local:field:earth-environmentalmapping · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0
concepts[field].local:field:earth-environmentalmapping · rdr ucl ac ukconnector:rdr_ucl_ac_uk@1.0.0
concepts[field].local:field:life-sciencesmapping · rdr ucl ac ukconnector:rdr_ucl_ac_uk@1.0.0
concepts[field].local:field:life-sciencesmapping · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0
concepts[field].local:field:mathematics-statisticsmapping · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0
concepts[field].local:field:mathematics-statisticsmapping · rdr ucl ac ukconnector:rdr_ucl_ac_uk@1.0.0
concepts[field].local:field:medicine-healthmapping · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0
concepts[field].local:field:medicine-healthmapping · rdr ucl ac ukconnector:rdr_ucl_ac_uk@1.0.0
descriptionsource · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0/metadata/dc/description
licensesource · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0/metadata/dc/rights
publication_datesource · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0
titlesource · figshare dmu ac ukconnector:figshare_dmu_ac_uk@1.0.0/metadata/dc/title