Going Viral
Going Viral is an interactive artwork that invites people to intervene in the spreading of misinformation by sharing informational videos about COVID-19 that feature algorithmically generated celebrities, social media influencers, and politicians that have made or shared claims about the coronavirus that are counter to the official consensus of healthcare professionals and were categorized as misinformation. In the videos, algorithmically-generated speakers deliver public service announcements or present news stories that counter the misinformation they had previously promoted on social media. The sharable YouTube videos are made using a conditional generative adversarial network (cGAN) that is trained on sets of two images where one image becomes a map to produce a second image, resulting in a glitchy reconstruction of the speaker. The recognizable, but clearly digitally-produced aesthetic prevents the videos from being classified as “deepfakes” and removed by online platforms, while inviting viewers to reflect on the constructed nature of celebrity, and question the authority of celebrities on issues of public health and the validity of information shared on social media. Celebrities and social media influencers are now entangled in the discourse on public health, and are sometimes given more authority than scientists or public health officials. Like the rumors they spread, the online popularity of social media influencers and celebrities is amplified through neural network-based content recommendation algorithms used by online platforms. 
The videos in Going Viral are produced by a Pix2Pix conditional generative adversarial network (cGAN). In a cGAN, a neural network is trained on sets of two images where one image becomes a map to produce a second image. In Going Viral, the two images are a frame from a video and the facial recognition landmarks from that video frame. Once the model is trained, it can be used to generate an image of a face based only on the facial landmarks from the first image. The process starts by extracting the facial landmarks of an influencer, celebrity, or politician from frames of a video. A model that maps the landmarks to an image of the influencer is then trained. Next, the facial landmarks of an expert speaking on a topic are extracted and used to generate new video frames. The new frames are combined to make a video with the audio track of the expert or journalist to produce a public service announcement that counters the misinformation spread by the celebrity, influencer, or politician. Finally, these videos are posted to YouTube and are shareable on social media via goingviral.art.
About The Artists
 Derek Curry (US) is an artist-researcher whose work critiques and addresses spaces for intervention in automated decision-making systems. His work has addressed automated stock trading systems, Open Source Intelligence gathering (OSINT), and algorithmic classification systems. His artworks have replicated aspects of social media surveillance systems and communicated with algorithmic trading bots. Derek earned his MFA in New Genres from UCLA's Department of Art in 2010 and his PhD in Media Study from the State University of New York at Buffalo in 2018. He is currently an Assistant Professor at Northeastern University in Boston.
Jennifer Gradecki (US) is an artist-theorist who investigates secretive and specialized socio-technical systems. Her artistic research has focused on social science techniques, financial instruments, technologies of mass surveillance, intelligence analysis, artificial intelligence, and social media misinformation. She received her MFA in New Genres from UCLA in 2010 and her PhD in Visual Studies from SUNY Buffalo in 2019. She is currently an Assistant Professor at Northeastern University in Boston.
Curry and Gradecki have presented and exhibited at venues including Ars Electronica (Linz), Media Art History (Krems), NeMe Arts Center (Cypress), Art Machines (Hong Kong), ISEA (Vancouver), ADAF (Athens), and the Centro Cultural de España (México). Their research has been published in Big Data & Society, Visual Resources, Leonardo, and Leuven University Press. Their artwork has been funded by Science Gallery Dublin, Science Gallery Detroit, the Puffin Foundation, and the NEoN Digital Arts Festival.