Rapidly Verifying the Credibility of Information Sources on Twitter
One of the advantages of working at QCRI is that I’m regularly exposed to peer-reviewed papers presented at top computing conferences. This is how I came across an initiative called “Seriously Rapid Source Review” or SRSR. As many iRevolution readers know, I’m very interested in information forensics as applied to crisis situations. So SRSR certainly caught my attention.
The team behind SRSR took a human centered design approach in order to integrate journalistic practices within the platform. There are four features worth noting in this respect. The first feature to note in the figure below is the automated filter function, which allows one to view tweets generated by “Ordinary People,” “Journalists/Bloggers,” “Organizations,” “Eyewitnesses” and “Uncategorized.”
The second feature, Location, “shows a set of pie charts indica-ting the top three locations where the user’s Twitter contacts are located. This cue provides more location information and indicates whether the source has a ‘tie’ or other personal interest in the location of the event, an aspect of sourcing exposed through our preliminary interviews and suggested by related work.”
The third feature worth noting is the “Eyewitness” icon. The SRSR team developed the first ever automatic classifier to identify eyewitness reports shared on Twitter. My team and I at QCRI are developing a second one that focuses specifically on automatically classifying eyewitness reports during sudden-onset natural disasters. The fourth feature is ”Entities,” which displays the top five entities that the user has mentioned in their tweet history. These include references to organizations, people and places, which can reveal important patterns about the twitter user in question.
Journalists participating in this applied research found the “Location” feature particularly important when assess the credibility of users on Twitter. They noted that “sources that had friends in the location of the event were more believable, indicating that showing friends’ locations can be an indicator of credibility.” One journalist shared the following: “I think if it’s someone without any friends in the region that they’re tweeting about then that’s not nearly as authoritative, whereas if I find somebody who has 50% of friends are in [the disaster area], I would immediately look at that.”
In addition, the automatic identification of “eyewitnesses” was deemed essential by journalists who participated in the SRSR study. This should not be surprising since “news organizations often use eyewitnesses to add credibility to reports by virtue of the correspondent’s on-site proximity to the event.” Indeed, “Witness-ing and reporting on what the journalist had witnessed have long been seen as quintessential acts of journalism.” To this end, “social media provides a platform where once passive witnesses can become active and share their eyewitness testimony with the world, including with journalists who may choose to amplify their report.”
In sum, SRSR could be used to accelerate the verification of social media con-tent, i.e., go beyond source verification alone. For more on SRSR, please see this computing paper (PDF), which was authored by Nicholas Diakopoulos, Munmun De Choudhury and Mor Naaman.
Original source with two screen shots.
