This month: June 2021
The coming age of adversarial social bot detection
Social bots are automated accounts often involved in unethical or illegal activities. Academia has shown how these accounts evolve over time, becoming increasingly smart at hiding their true nature by disguising themselves as genuine accounts. If they evade, bots hunters adapt their solutions to find them: the cat and mouse game. Inspired by adversarial machine learning and computer security, this paper proposes an adversarial and proactive approach to social bot detection, and we call scholars to arms, to shed light on this open and intriguing field of study.
   
Also this month
Four years of fake news: A quantitative analysis of the scientific literature
Since 2016, “fake news” has been the main buzzword for online misinformation and disinformation. This term has been widely used and discussed by scholars, leading to hundreds of publications in a few years. This report provides a quantitative analysis of the scientific literature on this topic by using frequency analysis of metadata and automated lexical analysis of 2,368 scientific documents retrieved form Scopus, a large scientific database, mentioning “fake news” in the title or abstract. Findings show that until 2016 the number of documents mentioning the term was less than 10 per year, suddenly rising from 2017 and steadily increasing in the following years. Among the most prolific countries are the U.S. and European countries such as the U.K., but also many non-Western countries such as India and China. Computer science and social sciences are the disciplinary fields with the largest number of documents published. Three main thematic areas emerged: computational methodologies for fake news detection, the social and individual dimension of fake news, and fake news in the public and political sphere. There are 10 documents with more than 200 citations, and two papers with a record number of citations.