Deepfakes on Twitter: Which Actors Control Their Spread?

Abstract

The term deepfake was first used in a Reddit post in 2017 to refer to videos manipulated using artificial intelligence techniques and since then it is becoming easier to create such fake videos. A recent investigation by the cybersecurity company Deeptrace in September 2019 indicated that the number of what is known as fake videos had doubled in the last nine months and that most were pornographic videos used as revenge to harm many women. The report also highlighted the potential of this technology to be used in political campaigns such as in Gabon and Malaysia. In this sense, the phenomenon of deepfake has become a concern for governments because it poses a short-term threat not only to politics, but also for fraud or cyberbullying. The starting point of this research was Twitter’s announcement of a change in its protocols to fight fake news and deepfakes. We have used the Social Network Analysis technique, with visualization as a key component, to analyze the conversation on Twitter about the deepfake phenomenon. NodeXL was used to identify main actors and the network of connections between all these accounts. In addition, the semantic networks of the tweets were analyzed to discover hidden patterns of meaning. The results show that half of the actors who function as bridges in the interactions that shape the network are journalists and media, which is a sign of the concern that this sophisticated form of manipulation generates in this collective.

Fake news on the internet: a literature review, synthesis and directions for future research

“…Considering the economics of emotions (Bakir and McStay, 2018), FNI leverages emotions to generate attention and produce a surge of clickbait to attract diverse audiences (Munger, 2020). As Internet users are more likely to trust images and audio, deepfakes have quickly become a popular form of fake news (Ahmed, 2021b;Dasilva et al, 2021;Vaccari and Chadwick, 2020;Wahl-Jorgensen and Carlson, 2021). Regarding other links, root tweets about fake news are mostly generated by ordinary accounts, but they often include a link to non-credible news websites .…”

“…, 2016). Deepfakes are an emerging form of disinformation involving doctored multimedia content (Ahmed, 2021c; Dasilva et al. , 2021; Vaccari and Chadwick, 2020; Wahl-Jorgensen and Carlson, 2021).…”

“…The Oxford English Dictionary defines "hoax" as a humorous or mischievous deception (Kumar et al, 2016). Deepfakes are an emerging form of disinformation involving doctored multimedia content (Ahmed, 2021c;Dasilva et al, 2021;Vaccari and Chadwick, 2020;Wahl-Jorgensen and Carlson, 2021).…”

Visualizing an image network without rendering files: A method to combine user hashtags with computer vision labels

“…Westerlund (2019) argues that celebrities are easy targets for fabricated content since they have an abundance of free video, image, and audio content circulating over the internet and that content availability facilitates deepfakes creation. Dasilva et al (2021) analyzed the debate around deepfakes on a Twitter network. They have shown that journalists and media are bridging information nodes and that celebrities figures are among the most referenced and virialized users in the networks.…”

«Deepfakes»: el pròxim repte en la detecció de notícies falses

“…Twitter lucha con ocho millones de cuentas a la semana que intentan difundir contenido manipulado. Deepfakes publicados y compartidos en Twitter acumulan millones de visualizaciones (Pérez et al, 2021).…”