Hello my name is mohsen mosleh
and I'm going to talk about a joint work
together with Cameron Martel
Dean Eckles and Dave Rand on perverse
downstream consequences of debunking
One way to fight misinformation online
is through social corrections
that is where users correct one another
Do social corrections work?
Survey experiments indicate that corrections
typically make the recipients believe
more accurate about specific information
Survey experiments also suggest that
corrections from other users that is
social corrections are as effective as
corrections provided by social media
platforms
and improving beliefs about specific
information
what about the downstream consequences
of direct corrections on social media
well one possibility is that
corrections have an added benefit of
also functioning as accuracy nudges
by pointing out that the past pose was
inaccurate
this may make the concept of accuracy
top of mind and thereby
improve the user's subsequent sharing
but it could also be that direct
corrections are less effective or even
backfire
this could happen in two fundamentally
different ways
one is by making individuals more
defensive
while psychological reactance
for instance which in turn makes people
care less about accuracy this account
hypothesizes that
corrections could alter individuals
preferences about accuracy
another one is by drawing individuals
attentions away from accuracy
and directing attention instead towards
other possible considerations
this account hypothesizes that
corrections
could shift individuals attention away
from accuracy
we developed a field experiment to
investigate the impact of direct
correction
on user subsequent sharing behavior we
retrieved a list of recent stories found
to be false by the fact-checking website
we then identified more than
2000 users who shared those contents on
twitter
we publicly corrected users by replying
to their false
tweets including a link to the fact-checking
website
we quantified quality of content
using a list of 60 domains for whom we
had fact checkers trustworthiness
writing
as part of a previous study. We also
quantified personal standard of treats
using political alignment of domains
shared by the users.
Finally, we measured the language
toxicity using the Google Jigsaw
Perspective API. We use a step-wedge
design to examine the causal effects of
public correction on users's subsequent
sharing.
We randomly assigned users into various
treatment days in which they received
the correction message.
Within 24 hours after receiving the
corrections,
we then compared users subsequent
sharing behavior
between those who received the message
and those who did not yet receive the
message.
We found social correction can adversely
affect
users subsequent sharing. In particular
we found direct
direct correction increases subsequent
sharing of low quality partisan and
toxic content
and this only happens to retweets but
not to primary tweets
this effect is robust controlling for a
variety of users characteristics
including partisanship, number of
followers,
and activity level on the platform
overall we found that direct
public corrections can negatively impact
users subsequent sharing.
Our observation that this only happens
to retweets suggests that the
effect is operating through the channel
of attention which is particularly
constrained
when making typically fast retweet
decisions
rather than by modifying one's actual
preferences
which are likely to be more strongly
reflected by primary tweets
composed by the users themselves. Rather
than
focusing attention on accuracy it
seems that being publicly corrected by
another user
directs attention away from accuracy
perhaps toward
the various social factors at play in
such a fundamentally
social interaction. Thank you very much
if you have any questions feel free to
get in touch