Emotional contagion in online social networks has been of great interest over the past years. Previous studies have mainly focused on finding evidence of affection contagion in homophilic atmospheres. However, these studies have overlooked users' awareness of the sentiments they share and consume online. In this work, we present an experiment with Twitter users that aims to help them better understand which emotions they experience on this social network. We introduce Affective Network (Aff-Net), a Google Chrome extension that enables Twitter users to filter and make explicit (through colored visual marks) the emotional content in their news feed.
The extension is powered by machine learning algorithms that classify tweets into different sentiment categories: positive posts tend to use happy or surprising language; negative posts tend to use sad, angry, or disgusting language; and posts without strong emotional language are classified as neutral.
Affective Network aims to help social media users better understand which emotions they tend to consume on social media, and how these emotions can spread through their social networks. It was built by researchers at the Laboratory for Social Machines and the Affective Computing group at the MIT Media Lab.
Note that Affective Network does not necessarily reflect the official position of the MIT Media Lab regarding the benefits and drawbacks of filtering out specific emotional content.