This Art Piece Transforms Crying Emojis From Twitter Into Beautiful Light Tears

Tears of Twitter is a real-time visualization of crying emojis posted on Twitter.


Created by French artist Parse/Error, Tears of Twitter is a real-time visualization of the use of 😢 (crying face) and 😭 (loudly crying face) emojis around the world. A connected artwork that responds to the emotions published on Twitter. A waterfall of light tears, fed by a global data stream.

 

Connected to the data stream from Twitter, Tears of Twitter is able to detect the presence of these two popular emojis in messages posted by users. It proposes to observe in real time the use of these two emojis, symbolizing tears and sadness, to transform it into a visual and luminous flow.

These 😢 😭 emojis posted on the famous social network are materialized in the form of drops of light, together forming an uninterrupted waterfall that flows tirelessly before our eyes. Thousands of tears published on Twitter come together to form a true torrent of information. Tears of Twitter is a contemplative experience inviting the viewer to observe the flow of these anonymous messages, as we lose ourselves in watching the drops run on a window on a rainy day.

Behind the real-time visualization of the use of these emojis, Tears of Twitter poses the question of our reaction to an information overdose, but also questions the limits of our empathy faced with the simultaneous expression of the emotions of thousands of people. How to apprehend these chains of characters inexorably falling into oblivion? How to feel an emotion when it is drowned in a river of data?

more info: http://parseerror.ufunk.net/portfolio/tears-of-twitter/


Like it? Share with your friends!

9.9k shares

0 Comments

Join the artFido Newsletter

artFido’s videos and content are viewed more than 2.5 billion times a month. This makes the network the seventh most viewed media company in the online sphere, behind the Walt Disney company in sixth place, and in front of US media giant Comcast in eighth place.*
* Statistics provided by research group Tubular Labs