Last year I wrote a book, aimed at a general audience, that explores how data-driven algorithms have impacted the news industry and our ability to separate fact from fiction. This article zeros in on, and amplifies, some of the more mathematical aspects of that story in what I hope will be both informative and engaging to a mathematical audience. As you’ll soon see, there are many fun ingredients at play here, ranging from elementary notions (fractions, linear functions, and weighted sums) to intermediate level concepts (eigenvalues and Shannon information) to sophisticated uses of probability theory, network analysis, and deep learning.
This topic of information and misinformation is complex, multifaceted, and interdisciplinary, and in my opinion more mathematicians should try to enter the public discussions surrounding it and undertake research related to it. I believe we, the math community, can do for misinformation what we have been doing for topics like gerrymandering, where mathematicians have assisted policy makers and helped shape the discourse while also discovering marvelous math topics to explore.
Read the rest of the article, by Noah Giansiracusa, here: https://www.ams.org/journals/notices/202210/noti2566/noti2566.html