epitweetr
Early Detection of Public Health Threats from 'Twitter' Data
It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information is available in the 'epitweetr' peer-review publication (doi:10.2807/1560-7917.ES.2022.27.39.2200177).
- Version2.2.16
- R versionunknown
- LicenseEUPL
- Needs compilation?No
- Last release11/15/2023
Documentation
Team
Laura Espinosa
Esther Kissling
Show author detailsRolesContributorFrancisco Orchard
Ariana Wijermans
Show author detailsRolesContributorThomas Mollet
Show author detailsRolesContributor, fndAdrian Prodan
Show author detailsRolesContributorThomas Czernichow
Show author detailsRolesContributorMaria Prieto Gonzalez
Show author detailsRolesContributorMichael Höhle
Show author detailsRolesContributor
Insights
Last 30 days
This package has been downloaded 363 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 10 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,976 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 40 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports34 packages
- Suggests1 package