acm-header
Sign In

Communications of the ACM

ACM TechNews

Science Makes First Study of Know-It-All Internet Commenters


View as: Print Mobile App Share:
The study found some of the most intransigent commenters express their convictions across less likely subjects, and appear to be just bad-tempered.

Researchers at Stanford University and Microsoft have conducted the first study of intransigent commenters in social networks, whose contributions are written as statements of fact rather than contributions to a discourse.

Credit: MontrealGazette.com

The first study of intransigent commenters in social networks has been conducted via a method developed by researchers at Stanford University and Microsoft.

Their analysis system was trained on 5,000 annotated posts from Reddit communities, but the extracted principles were applied to millions of other comments on the site.

Among the knowledge tasks set up by Stanford's Ethan Fast and Microsoft's Eric Horvitz was determining whether dogmatic commenters are uniformly stubborn across a range of subjects, and for this purpose they produced 10 million analyzed posts from 2007-2015 across 1,000 users.

The study found some of the most intransigent commenters express their convictions across less-likely subjects, and appear to be just bad-tempered. "For example, among the users who are dogmatic on politics, they are also disproportionately dogmatic on unrelated subreddits such as science, technology, IAmA, and AskReddit," the researchers say.

The study indicates dogmatic commenters post often and in favorite communities, but they also appear to most value placing a first or at least early comment, as they are "not as inclined to engage with discussion, once it has begun."

Fast and Horvitz suggest the results of their computational model could help users engage in more pro-social behavior in online communities.

From The Stack (UK)
View Full Article

 

Abstracts Copyright © 2016 Information Inc., Bethesda, Maryland, USA


 

No entries found

Sign In for Full Access
» Forgot Password? » Create an ACM Web Account