3.1. Academic and professional Institutions fighting against fake news

University research teams are involved in fighting against fake news. Thus, a research team at the University of Michigan has created an algorithm for hunting fake news that has proven to do better than humans: it has managed to identify fake news with a success rate of 76%, compared to human hunters at 70%. Adam Conner-Simons (2018) MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), in collaboration with Qatar Computing Research Institute (QCRI), has approached the issue of detecting Fake news, by focusing on news sources. The system developed by the MIT researchers uses Machine Learning to determine the accuracy of an information source and identify whether it is politically biased or ideologized.

Another example of the detection of fake news through artificial intelligence is the system based on deep learning developed by the British startup Fabula. In this case, the hoax is not identified by analyzing the text, but by studying how the stories are shared, to recognize the patterns of diffusion that can only correspond to fake news.

Probably, the great unknown is not so much the capacity of the technology to disseminate disinformation, fake news, or hoaxes, but the lack of ethics in a network culture- or Cyberculture. A culture in which people cannot discern between what is credible, truthful information, and what is an attempt to manipulate behavior, opinion and even the will to act and decide.

With this panorama, we considered addressing some of the bots that have been developed as entrepreneurial initiatives to detect fake news, hoaxes, or disinformation. The following criteria of the selection of the bots analyzed is based on the most known and representative in each area, linked to the area of journalistic news.

3.2 Study and typology of information verification bots.

The following sections detail the references and characteristics of some of the bots selected for information verification.