Kate Starbird

Mass Convergence within Disaster and Humanitarian Response

The emCOMP Lab examines mass participation in the context of crisis (i.e. large-scale, natural, and man-made disasters). Disaster events have long been known to occasion convergence behavior—i.e. after disasters people converge physically onto the scene to— among other things—offer help. With the widespread adoption of social media and other online tools, we are now seeing digital convergence after disaster events, whereby people all over the world come together in virtual spaces, participating in a number of capacities, including volunteerism. Digital volunteerism has quickly become a recognized feature of the informational landscape after disaster events. The emCOMP Lab pursues empirical research and design opportunities in this space, seeking to find ways to support and leverage digital volunteerism and other forms of social media participation during crises, and to integrate the products of these activities into formal response efforts.

We believe that digital volunteerism offers an interesting perspective on crowdsourcing and crowdwork, one where participation is volunteer-based and motivations are largely altruistic, yet in many ways fluid (changing as an event progresses).

Examining the Flow of Misinformation after Disasters

A current focus of the emCOMP lab is the study of online rumoring during disaster events. We are approaching this problem from multiple perspectives. We have dual goals of 1) better understanding how and why misinformation spreads online after disaster events and 2) informing solutions for automatically detecting misinformation. Aligned with the lab focus on crowdwork, a significant component of this research looks specifically at how the crowd questions or challenges rumors.

In one study looking at Twitter data collected after the 2013 Boston Marathon Bombings, a collaborative effort with the Social Media (SoMe) Laboratory at UW, we draw parallels between qualitative and quantitative methods to identify multi-dimensional “signatures” or patterns of information propagation. These dimensions include a rumor’s origins, changes over time, relationships between rumor spreading and rumor challenging, and other salient features. Rumor signatures enhance our understanding of how different kinds of rumors propagate online during crisis events and, we propose, could help inform future techniques for automatically identifying and classifying rumors.

In more recent work, we use Twitter data related to alternative narratives or conspiracy theories of disaster events to reveal the underlying structure of (one subsection) of the alternative media ecosystem. We conducted content analysis of the domains that are cited in the production of alternative narratives—revealing intersections between conspiracy theories, political propaganda, and online disinformation. I wrote a blog post about this in March 2017.

Network Graph of Alternative Media Domains Cited in the Production (and Debunking) of Conspiracy Theories
Alternative Media = light blue; Mainstream Media = purple

Crowdsourcery: When Emergent Collaborations Work

One feature of crises is that people naturally come together to respond. Such emergent responses typically happen quickly and (just as the disaster itself) unexpectedly. This makes it challenging for researchers to observe and consequently understand how such collaborations form and what makes them work. Now, social media data can give us a window into the micro-interactions that take place to form and sustain emergent collaborations during crises. When combined with other data such as contextual interviews, we can model how the interactions we observe in social media fit into the larger context of a particular event. 

In research examining crisis events in rural communities (in the Catskill Mountains after 2011 Hurricane Irene and in Washington State after the 2013 Oso Landslide), we have been constructing an empirically-derived model of how information needs are met—with an eye towards intersections of offline and online work. We follow the flow of specific information needs, the platforms through which each need is mediated, and the work involved to address each specific need, including reaching out to specific audiences across gaps in the telecommunications infrastructure.

With this level of detail, we are able to 1) differentiate the many kinds of situated knowledge and expertise that came into play and observe the different kinds of micro-collaborations that formed in relation to meeting information needs.