Category Archives: mdc

Truthy tool identifies smear tactics on Twitter

Astroturfers, Twitter-bombers and smear campaigners need beware this election season as a group of leading Indiana University information and computer scientists today unleashed Truthy.indiana.edu, a sophisticated new Twitter-based research tool that combines data mining, social network analysis and crowdsourcing to uncover deceptive tactics and misinformation leading up to the Nov. 2 elections. Combing through thousands of tweets per hour in search of political keywords, the team based out of IU’s School of Informatics and Computing will isolate patterns of interest and then insert those memes (ideas or patterns passed by imitation) into Twitter’s application programming interface (API) to obtain more information about the meme’s history.

In the run-up to the mid-term elections, Truthy uncovered a number of abuses such as robot-driven traffic to politician websites and networks of bot accounts controlled by individuals to promote fake news. These findings have been widely covered in the press, with mentions in The Atlantic, MIT Technology Review, PC World, New Scientist, NPR, Ars Technica, Fast Company, The Chronicle of Higher Education, The New York Times Magazine, and many other media. Read more here and here.

NaN Abstract for Michael Conover’s April 21st Talk

“The problem with Wikipedia is that it only works in practice. In theory, it can never work.”  — Zeroeth Law of Wikipedia

One of the most important social and intellectual phenomena of the 21st century, the collaboratively-edited online encyclopedia Wikipedia is vexing in its ability to produce informative articles on a multitude of subjects.  Leveraging graph theoretic techniques to measure the degree to which latent connections between articles are present in the Wikipedia corpus we demonstrate that the collaborative editing process produces, over time, an increasingly logically-connected information artifact. Moreover, using the public-domain 1911 Encyclopedia Britannica as a benchmark corpus for the single-author-article paradigm, we demonstrate that Wikipedia contains a growing core of mature articles which exhibit a degree of logical connectedness significantly surpassing that found in the Encyclopedia Britannica. Taken in conjunction with an understanding of Wikipedia’s accuracy and topical coverage, this conclusion paints a rich portrait of the strengths and weaknesses of both collaboratively- and single-author-edited encyclopedias.