The rise of artificial intelligence has recently led to bots writing real news stories about sports, finance and politics. As yet, bots have not turned their attention to science, and some people still mistakenly think science is too complex for bots to write about. In fact, a small number of insiders are now applying AI algorithms to summarise scientific research papers and automatically turn them into simple press releases and news stories. Could the science beat be next in line for automation, potentially making many science reporters --- and even editors --- superfluous to science
This article examines certain guiding tenets of science journalism in the era of big data by focusing on its engagement with citizen science. Having placed citizen science in historical context, it highlights early interventions intended to help establish the basis for an alternative epistemological ethos recognising the scientist as citizen and the citizen as scientist. Next, the article assesses further implications for science journalism by examining the challenges posed by big data in the realm of citizen science. Pertinent issues include potential risks associated with data quality
Thanks, on the one hand, to the extraordinary availability of colossal textual archives and, on the other hand, to advances in computational possibilities, today the social scientist has at their disposal an extraordinary laboratory, made of millions of interacting subjects and billions of texts. An unprecedented, yet challenging, opportunity for science. How to test, corroborate models? How to control, interpret and validate Big Data? What is the role of theory in the universe of patterns and statistical correlations? In this article, we will show some general characteristics of the use of