A friendly reminder that this is happening in 5 min. :-)

On Mon, Nov 16, 2015 at 3:37 PM, Sarah Rodlund <[email protected]>
wrote:

> Hi Everyone,
>
> The next Research Showcase will be live-streamed this Wednesday, November
> 18, 2015 at 11:30 (PST).
>
> YouTube stream: http://www.youtube.com/watch?v=kXCI6whgdUA
>
> As usual, you can join the conversation on IRC at #wikimedia-research.
> And, you can watch our past research showcases here
> <https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#Archive>.
>
> We look forward to seeing you!
>
> Kind regards,
>
> Sarah R. Rodlund
> Project Coordinator-Engineering, Wikimedia Foundation
> [email protected]
>
> This month:
>
> *Impact, Characteristics, and Detection of Wikipedia Hoaxes*
>
> By Srijan Kumar
>
> False information on Wikipedia raises concerns about its credibility. One
> way in which false information may be presented on Wikipedia is in the form
> of hoax articles, i.e. articles containing fabricated facts about
> nonexistent entities or events. In this talk, we study false information on
> Wikipedia by focusing on the hoax articles that have been created
> throughout its history. First, we assess the real-world impact of hoax
> articles by measuring how long they survive before being debunked, how many
> pageviews they receive, and how heavily they are referred to by documents
> on the Web. We find that, while most hoaxes are detected quickly and have
> little impact on Wikipedia, a small number of hoaxes survive long and are
> well cited across the Web. Second, we characterize the nature of successful
> hoaxes by comparing them to legitimate articles and to failed hoaxes that
> were discovered shortly after being created. We find characteristic
> differences in terms of article structure and content, embeddedness into
> the rest of Wikipedia, and features of the editor who created the hoax.
> Third, we successfully apply our findings to address a series of
> classification tasks, most notably to determine whether a given article is
> a hoax. And finally, we describe and evaluate a task involving humans
> distinguishing hoaxes from non-hoaxes. We find that humans are not
> particularly good at the task and that our automated classifier outperforms
> them by a big margin.
>
>
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>
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