Dean was certainly not the first to have this idea. He quickly learned from others who joined the effort to organize the FNC that much fundamental research in AI, ML and NLP has been happening in recent years. The convergence of this groundbreaking research and the widespread recognition that fake news is an important real-world problem resulted in an explosion of interest in our efforts by volunteers, teams and the technology press . The FNC has grown dramatically since that initial bet between friends, to the point where it now includes over 100 volunteers and 72 teams from around the world. While the details of the challenge have evolved from that initial (rather naive) wager, the goal has always remained the same - foster the use of AI, machine learning and natural language processing to help solve the fake news problem.
Marcia Angell’s comments were directed largely against conflicts of interest and the biases introduced by the influence of drug companies on researchers and universities. Richard Horton’s statement was part of his comments on a recent symposium on reliability and reproducibility of research in the biomedical sciences and addresses a broader area of concern. Some of the problems he identified are seen in the veterinary literature. They include inadequate number of subjects in the study, poor study design, and potential conflicts of interest. He notes that the quest for journal impact factor is fuelling competition for publication in a few high reputation journals. He warns that “our love of ‘significance’ pollutes the literature with many a statistical fairy-tale” and he remarks that journal editors, reviewers, and granting bodies all stress original studies to the extent that “we reject important confirmations.”