I'm excited to talk to you today about our recent research
at the intersection of misinformation, journalism,
and CSCW.
This is a teaser for what you can expect to see in our paper.
I first want to highlight this quote
from one of our participants, which I feel mirrors
the reason we've ventured into this area of research
in the first place.
They said, I feel like I'm coming with a knife
to a gunfight in terms of reporting on bad actors who
are trying to spread misinformation
and disinformation on social media.
So first, what is the misinformation be?
Though journalists have reported on disinformation efforts
for decades, a new beat is emerging
in national news outlets that not only talks
about the internet, but actually investigates
where the information on the internet
is coming from including organic or potentially
nefarious activity that shapes online conversation.
And in some cases, real world action.
Example of this include the 2016 election IRA effort,
the viral pandemic video, and many topics related to Cuban.
Journalists on this beat serve a critical role
in helping the public understand online activity,
and holding platforms accountable for hosting
harmful information.
Our labs own work investigating this content
showed us just how complicated and nuanced his reporting is,
which led us to question how are journalists doing this?
They have even tighter timelines and typically fewer
resources than us.
Also, in the era of fake news, journalists on the speak
can not afford to make mistakes at risk of being
called fake news themselves.
How do these journals investigate and report
on online misinformation, like what tools?
If any, are they using?
Also, how can CSCW and HCI researchers better
meet their needs.
We conducted 12 interviews, and analyze the data
using a grounded inductive approach.
No surprise, we found that there are unique
methodological ethical and technical challenges
to reporting on this beat.
Journalists overcome some of these challenges
by using unique processes and tools in their investigations,
and by seeking help from experts.
We dive into these elements in depth in our paper.
I'm going to spend the rest of this time introducing
new to our discussion topics.
To be honest, I expect to hear about the third party
tools or software that these journalists use
to identify trends.
This is a data heavy beat after all.
We did hear a little of this.
But mainly, we heard about how these journalists actually
become part of online environments to do this work.
They scroll through their Twitter feed,
join private Facebook, groups secretly lurk in Discord chat.
They actually have to be there, reading the content
to do this work.
In this way, they adapt their traditional investigative
journalistic skills to the online environment.
Third party tools and trend trackers
can miss subtleties of suspicious information
in these spaces.
Highlighting the need for a trained human eye to be here.
Based on this, we discussed suggestions for HCI researchers
like designing tools that keep journals
close to the data, prioritizing words and context
over numbers and trends.
I do acknowledge that there are often
data elements of the actual investigation.
Journalists on this beat struggled
to establish the credibility of the data they're using,
and analogous ways to how they would typically establish
credibility of a human source.
We also dive into the imbalance of power
between journalists and the platforms.
Social media platforms hold the keys
to much of our collective activity as a society.
The journalists we spoke to feel like platforms
are unwilling to share information with them, even
when they are working on a story that doesn't take aim
at the company.
For these journalists, gathering data from these sites
is necessary.
They need to understand the magnitude
and reach of the information to make an ethical decision
to publish or not.
Those some platforms are starting
to share data like Facebook's CrowdTangle
and Twitter's election integrity data set.
The relationship between journals and platforms
remains inherently unbalanced.
In many cases, platforms are selectively granting access
to this data, creating a data divide and a barrier
to entry for new and local journalists on this beat.
Lastly, we challenge our community
to think about solutions for those on this beat
beyond computational tools.
Academic studying misinformation, disinformation,
and conspiracy theories are doing important work.
But sometimes, the academic timelines
hinder real world impact.
Our own lab has struggled with this.
Lives have been lost over waiting for peer review.
In the paper, we discussed timeline, ethics,
and motivational mismatches that have troubled these kinds
of collaborations in the past.
And next, we plan to try something new,
and study how the academia journalism collaboration
on this beat might be improved.
That's all we have time for today,
but I'm happy to answer any questions today or offline
as you read the paper.
Thanks.