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.
The general principle behind epitweetr is that it collects tweets and related metadata from the Twitter Standard Search API version 1.1 (https://developer.twitter.com/en/docs/twitter-api/v1/tweets/search/overview/standard) according to specified topics and stores these tweets in a compressed form on your computer. epitweetr geolocalises the tweets and collects information on key words within a tweet. Tweets are aggregated according to topic and geographical location. Next, a signal detection algorithm identifies the number of tweets (by topic and geographical location) that exceeds what is expected for a given day. Then, epitweetr sends out email alerts to notify those who need to further investigate these signals following the epidemic intelligence processes (filtering, validation, analysis and preliminary assessment).
The package includes an interactive web application (Shiny app) with five pages: the dashboard, where a user can visualise and explore tweets (Fig 1), the alerts page, where you can view the current alerts and associated information (Fig 2), the geotag evaluation page, where you can evaluate the geolocation algorithm in different tweet fields to manually choose the geolocation threshold (Fig 3), the configuration page, where you can change settings and check the status of the underlying processes (Fig 4), and the troubleshoot page, with automatic checks and hints for using epitweetr with all its functionalities (Fig 5). On the dashboard, users can view the aggregated number of tweets over time, the location of these tweets on a map and the words most frequently found in these tweets. These visualisations can be filtered by the topic, location and time period you are interested in. Other filters are available and include the possibility to adjust the time unit of the timeline, whether retweets/quotes should be included, what kind of geolocation types you are interested in, the sensitivity of the prediction interval for the signal detection, and the number of days used to calculate the threshold for signals. This information is also downloadable directly from this interface in the form of data, pictures, or reports.