Hello all,
The next Wikimedia Research Showcase will be on Wednesday, November 17, at
17:30 UTC (9:30am PST/12:30pm EST/ 18:30 CET). The topic is content
moderation.
Livestream: https://www.youtube.com/watch?v=Rx3xesDkp2o
*Amy S. Bruckman (Georgia Institute of Technology, USA)Is Deplatforming
Censorship? What happened when controversial figures were deplatformed,
with philosophical musings on the nature of free speech*
Abstract: When a controversial figure is deplatformed, what happens to
their online influence? In this talk, first, I’ll present results from a
study of the deplatforming from Twitter of three figures who repeatedly
broke platform rules (Alex Jones, Milo Yiannopoulos, and Owen Benjamin).
Second, I’ll discuss what happened when this study was on the front page of
Reddit, and the range of angry reactions from people who say that they’re
in favor of “free speech.” I’ll explore the nature of free speech, and why
our current speech regulation framework is fundamentally broken. Finally,
I’ll conclude with thoughts on the strength of Wikipedia’s model in
contrast to other platforms, and highlight opportunities for improvement.
*Nathan TeBlunthuis (University of Washington / Northwestern University,
USA)Effects of Algorithmic Flagging on Fairness. Quasi-experimental
Evidence from Wikipedia*
Abstract: Online community moderators often rely on social signals such as
whether or not a user has an account or a profile page as clues that users
may cause problems. Reliance on these clues can lead to "overprofiling bias
when moderators focus on these signals but overlook the misbehavior of
others. We propose that algorithmic flagging systems deployed to improve
the efficiency of moderation work can also make moderation actions more
fair to these users by reducing reliance on social signals and making norm
violations by everyone else more visible. We analyze moderator behavior in
Wikipedia as mediated by RCFilters, a system which displays social signals
and algorithmic flags, and estimate the causal effect of being flagged on
moderator actions. We show that algorithmically flagged edits are reverted
more often, especially those by established editors with positive social
signals, and that flagging decreases the likelihood that moderation actions
will be undone. Our results suggest that algorithmic flagging systems can
lead to increased fairness in some contexts but that the relationship is
complex and contingent.
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
--
Janna Layton (she/her)
Administrative Associate - Product & Technology
Wikimedia Foundation <https://wikimediafoundation.org/>
The next Wikimedia Research Showcase will be on Wednesday, November 17, at
17:30 UTC (9:30am PST/12:30pm EST/ 18:30 CET). The topic is content
moderation.
Livestream: https://www.youtube.com/watch?v=Rx3xesDkp2o
*Amy S. Bruckman (Georgia Institute of Technology, USA)Is Deplatforming
Censorship? What happened when controversial figures were deplatformed,
with philosophical musings on the nature of free speech*
Abstract: When a controversial figure is deplatformed, what happens to
their online influence? In this talk, first, I’ll present results from a
study of the deplatforming from Twitter of three figures who repeatedly
broke platform rules (Alex Jones, Milo Yiannopoulos, and Owen Benjamin).
Second, I’ll discuss what happened when this study was on the front page of
Reddit, and the range of angry reactions from people who say that they’re
in favor of “free speech.” I’ll explore the nature of free speech, and why
our current speech regulation framework is fundamentally broken. Finally,
I’ll conclude with thoughts on the strength of Wikipedia’s model in
contrast to other platforms, and highlight opportunities for improvement.
*Nathan TeBlunthuis (University of Washington / Northwestern University,
USA)Effects of Algorithmic Flagging on Fairness. Quasi-experimental
Evidence from Wikipedia*
Abstract: Online community moderators often rely on social signals such as
whether or not a user has an account or a profile page as clues that users
may cause problems. Reliance on these clues can lead to "overprofiling bias
when moderators focus on these signals but overlook the misbehavior of
others. We propose that algorithmic flagging systems deployed to improve
the efficiency of moderation work can also make moderation actions more
fair to these users by reducing reliance on social signals and making norm
violations by everyone else more visible. We analyze moderator behavior in
Wikipedia as mediated by RCFilters, a system which displays social signals
and algorithmic flags, and estimate the causal effect of being flagged on
moderator actions. We show that algorithmically flagged edits are reverted
more often, especially those by established editors with positive social
signals, and that flagging decreases the likelihood that moderation actions
will be undone. Our results suggest that algorithmic flagging systems can
lead to increased fairness in some contexts but that the relationship is
complex and contingent.
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
--
Janna Layton (she/her)
Administrative Associate - Product & Technology
Wikimedia Foundation <https://wikimediafoundation.org/>