This guide explains how to import geocoded Twitter data into a dataset that is located in your CARTO account. CARTO provides access to the Twitter Firehose, which enables users to visualize and analyze this data directly in CARTO.
Twitter is available from the "Connect Dataset" options when adding a new dataset, or creating a new map.
After the Twitter connector has been enabled, select Twitter from the Connect Dataset options. Before you connect your data, define the Twitter search categories and terms to be used to query Twitter's data feed.
The following options enable you to define the search terms for your Twitter query.
|Twitter Trend Options||Description|
|Category 1-4||Represents search terms for different hashtags or keywords separated by commas. You can enter up to four search terms using the Category fields.|
|From / to||Displays the from and to time range for your requested data. By default, it is set to search for the last 30 days. Depending on your account settings, you can click the calendar icon to open the calendar and select a different date range. Additionally, you can indicate the hour and minute for the selected date range,|
|Use||Displays the amount of Twitter credits allocated to your account. You can use the slider to increase or decrease the percentage of credits to use. Contact us to update your Twitter credits at any time.|
After connecting to a Twitter dataset, unique columns from your dataset contain important Twitter information. You may need this information in order to filter or plot Twitter data on your visualization.
For this guide, we searched the popular 'Hillary Clinton' election hashtags - 'hillaryclinton' and 'imwithher' for one-day (10/27/2016) to see trends over a single day.
The Data View of the selected map layer displays unique Twitter columns. Information ranges from the postedtime, the language to tweet text, the Twitter user, and so on.
You can visualize your Twitter data in many different ways using Builder. For example, to view your data over time and to visualize the tweets as they increase/decrease over the queried time-range, apply the ANIMATED aggregation style for the map layer.
By animating the tweets on Hillary Clinton over the hours in a day, you can effectively visualize the brackets where the tweets rise or diminish.
We would love to hear from you! Was it easy to understand? Do you need more information? Let us know.