First Monday

First Monday is one of the first openly accessible, peer–reviewed journals solely devoted to resarch about the Internet. First Monday has published 1,872 papers in 279 issues, written by 2,615 different authors, over the past 23 years. No subscription fees, no submission fees, no advertisements, no fundraisers, no walls.

This month: August 2019
Across the great divide: How today’s college students engage with news
This paper reports results from a mixed-methods study about how college students engage with news when questions of credibility and “fake news” abound in the U.S. Findings are based on 5,844 online survey responses, one open-ended survey question (N=1,252), and 37 follow-up telephone interviews with students enrolled at 11 U.S. colleges and universities. More than two-thirds of respondents had received news from at least five pathways to news during the previous week; often their news came from discussions with peers, posts on social media platforms, online newspaper sites, discussions with professors, or news feeds. The classroom was an influential incubator for news habits; discussions of news provided relevant connections to curricular content as well as guidance for navigating a complex and crowded online media landscape. Respondents majoring in the arts and humanities, social sciences, and business administration were far more likely to get news from their professors than were students in computer science or engineering. The interplay between unmediated and mediated pathways to news underscored the value of the socialness of news; discussions with peers, parents, and professors helped students identify which stories they might follow and trust.
Also this month
Benford’s Law can detect malicious social bots
Social bots are a growing presence and problem on social media. There is a burgeoning body of work on bot detection, often based in machine learning with a variety of sophisticated features. This paper presents a simple technique to detect bots: adherence with Benford’s Law. Benford’s Law states that, in naturally occurring systems, the frequency of the first digits of numbers is not evenly distributed. Numbers beginning with a 1 occur roughly 30 percent of the time, and are six times more common than numbers beginning with a 9. This principle can be used to detect bots because they violate the expected distribution. In three studies — an analysis of a large Russian botnet we discovered, and studies of purchased retweets on Twitter and purchased likes on Facebook — bots’ social patterns consistently violate Benford’s Law while legitimate users follow it closely. These results offer a computationally efficient new tool for bot detection and there are broader implications for understanding fraudulent online behavior.



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