Hi everyone,
The next Research showcase will be live-streamed this Wednesday (tomorrow), September 16 at 11.30 PST. The streaming link is:
http://wwwhttp://www.youtube.com/watch?v=eJk6mxJZhH8
As usual, you can join the conversation on IRC at #wikimedia-research.
We look forward to seeing you!
Leila
This month: https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#September_2015
Morten Warncke-Wang will talk about the misalignment between production and consumption of quality content on Wikipedia, and Besnik Fetahu proposes a news-article suggestion task to improve news coverage in Wikipedia.
*Fun or Functional? The Misalignment Between Content Quality and Popularity in Wikipedia*
By Morten Warncke-Wang
In peer production communities like Wikipedia, individual community members typically decide for themselves where to make contributions, often driven by factors such as “fun” or a belief that “information should be free”. However, the extent to which this bottom-up, interest-driven content production paradigm meets the need of consumers of this content is unclear. In this talk, I analyse four large Wikipedia language editions, finding extensive misalignment between production and consumption of quality content in all of them, and I show how this greatly impacts Wikipedia’s readers. I also examine misalignment in more detail by studying how it relates to specific topics, and to what extent high popularity is related to sudden changes in demand (i.e. “breaking news”). Finally, I discuss technologies and community practices that can help reduce misalignment in Wikipedia.
*Automated News Suggestions for Populating Wikipedia Entity Pages*
By Besnik Fetahu
Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20% of the references are from online news sources. However, many entity pages are incomplete even if relevant information is already available in existing news articles. Even for the already present references, there is often a delay between the news article publication time and the reference time. In this work, we therefore look at Wikipedia through the lens of news and propose a novel news-article suggestion task to improve news coverage in Wikipedia, and reduce the lag of newsworthy references. Our work finds direct application, as a precursor, to Wikipedia page generation and knowledge-base acceleration tasks that rely on relevant and high quality input sources. We propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, we suggest news articles to Wikipedia entities (article-entity placement) relying on a rich set of features which take into account the salience and relative authority of entities, and the novelty of news articles to entity pages. Second, we determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. We perform an extensive evaluation of our approach based on ground-truth data that is extracted from external references in Wikipedia. We achieve a high precision value of up to 93% in the article-entity suggestion stage and upto 84% for the article-section placement. Finally, we compare our approach against competitive baselines and show significant improvements.