Demo Data from SIGIR 2026 / reviewers in the wild / expert
Matthew Can
dblp:68/9596
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 50% Design research and methods · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Collaborative and social computing
crowdsourcing |
0.1 | 1 | 2012 | Collaboratively crowdsourcing workflows with turkomatic · CSCW 2012 |
Design research and methods
workflow design |
0.1 | 1 | 2012 | Collaboratively crowdsourcing workflows with turkomatic · CSCW 2012 |
Methods — techniques the papers use, named apart from their topics
iterative design · 0.1evaluation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Same Referent, Different Words: Unsupervised Mining of Opaque Coreferent Mentions
Marta Recasens, Matthew Can, Daniel Jurafsky |
HLT-NAACL | 2 |
| 2012 | Collaboratively crowdsourcing workflows with turkomaticabstractPreparing complex jobs for crowdsourcing marketplaces requires careful attention to workflow design, the process of decomposing jobs into multiple tasks, which are solved by multiple workers. Can the crowd help design such workflows? This paper presents Turkomatic, a tool that recruits crowd workers to aid requesters in planning and solving complex jobs. While workers decompose and solve tasks, requesters can view the status of worker-designed workflows in real time; intervene to change tasks and solutions; and request new solutions to subtasks from the crowd. These features lower the threshold for crowd employers to request complex work. During two evaluations, we found that allowing the crowd to plan without requester supervision is partially successful, but that requester intervention during workflow planning and execution improves quality substantially. We argue that Turkomatic's collaborative approach can be more successful than the conventional workflow design process and discuss implications for the design of collaborative crowd planning systems. Anand Pramod Kulkarni, Matthew Can, Björn Hartmann |
CSCW | 2 |