Background: Kevin Hu & Travis Rich built a site called GIFGIF, which aims to crowd tag animated gifs with various emotions. From GIFGIF’s website: “An animated gif is a magical thing. It contains the power to convey emotion, empathy, and context in a subtle way that text or emoticons simply can’t. GIFGIF is a project to capture that magic with quantitative methods. Our goal is to create a tool that lets people explore the world of gifs by the emotions they evoke, rather than by manually entered tags.” As we know, animated gifs are also a popular storytelling mechanism for social news and entertainment websites.
The cultural phenomenon of using animated gifs to express emotions has been the subject of numerous journalistic inquiries:
Fresh From the Internet’s Attic – NYTimes
Christina Hendricks on an Endless Loop: The Glorious GIF Renaissance – Slate.com
GIF hearts Tumblr: a fairytale for the Internet age – Wired.co.uk
Visualization project for this week: Kevin, Travis, and I built a map tool so people can explore GIFGIF’s current dataset to see which gifs are most representative of certain emotions across different countries. Out of 1.8 million votes, 1.4 million votes had IP data which links the votes to the location of the voter. GIFGIFmap can be found here.
In a future version, we would like to show the top gifs per emotion that countries have in common with each other, and what are unique top gifs for each country (along the lines of What We Watch). However, there are limitations to the GIFGIF data set in terms of global coverage. For example, the top 21 countries account for 92% of the votes. Additionally, we excluded countries that had less than 10,000 total votes across all categories, so as to avoid making generalizations based on limited data. We chose to include the number of votes per country (per emotion) to make the data set more transparent in terms of representation.
We think the tool we are building could complement existing stories about the phenomenon of using animated gifs to communicate (stories like the ones we linked to above).
These are some potential questions that we hope journalists could explore using a map interface to the GIFGIF dataset:
1) Do people from different countries interpret the emotional content of gifs differently?
2) If there are variances in interpretation, are there clusters of countries that have more similar interpretations? Do these match up with proximity, or immigration patterns?
3) What top gifs per emotion are unique to a given country?
Note: GIFGIF’s data will soon be made publicly available through an API.