The next Research showcase will be live-streamed this Wednesday (tomorrow),
September 16 at 11.30 PST. The streaming link is:
As usual, you can join the conversation on IRC at #wikimedia-research.
We look forward to seeing you!
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
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
*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.
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