• Overview

Full papers submitted to HCOMP 2017 will be evaluated according to the following review criteria. Our intent in posting review criteria online is to further improve transparency of the conference's peer-review process and to provide additional guidance to authors in preparing their submissions (especially for young researchers, as well as researchers from diverse disciplines).

Feedback or suggestions for further improving these review criteria? Tweet us @hcomp_conf or email us at hcompconference@gmail.com.

• Relevance

• How relevant is the work to HCOMP? Are many conference attendees likely to be interested?
• From the Call for Papers
  • "To ensure relevance, submissions are encouraged to include research questions and contributions of broad interest to crowdsourcing and human computation, as well as discuss relevant open problems and prior work in the field."
  • "When evaluation is conducted entirely within a specific domain, authors are encouraged to discuss how findings might generalize to other communities and application areas using crowdsourcing and human computation."

• Presentation & Writing

• Is the English writing correct and comprehensible?
• Are the ideas, methods, results and discussions well-presented?
• Are research questions and major findings clearly articulated?
• Is any domain-specific terminology and methodology explained for a diverse HCOMP audience?
• HCOMP-specific Note: Is any language use criticizing the crowd balanced with praise when appropriate? Do the authors either avoid inflammatory language in characterizing crowd contributors (e.g., incompetent, cheaters, spammers, etc.) or provide rigorous evidence justifying use of such terms?

• Citations & Prior Work

• Do authors cite prior work most relevant to their own study?
• Is the review of prior work correct and sufficiently detailed?
• Do authors discuss all prior work needed to interpret and assess this paper?

• Novelty

• Does the paper establish a new problem?
• Are new methods or theorems proposed?
• Is evaluation conducted on novel data?
• Are new evaluation procedures described for establishing validity?
• Is the problem or approach so novel that it may be difficult for the authors to rigorously evaluate or for the reviewers to assess?

• Significance & Impact

• How significantly will this work change future research and practice in the field?
• Where is it on the spectrum from incremental to transformative?
• Does it pose important new problems, challenge accepted knowledge and practice, or otherwise prompt or enable new avenues of research?
• How likely is this paper to be cited?

• Data, Code, & Resources

• Do the authors commit to sharing any new datasets, sourcecode, and/or other resources for others to use?
• If so, are many people likely to use these new resources?
• How greatly would these new resources impact future research and practice?

• Soundness & Validity

• Are research methods appropriate, sufficient, and correctly employed?
• Is the experimental design sound, analyses thorough, proofs valid, and findings supported by evidence?
• Is the work brought to an appropriate state of completion?
• HCOMP-specific Note: Do the authors consider the impact of their own task design in any evaluation of crowd reliability & quality?

• Reproducibility

• Are method descriptions sufficiently detailed and clear?
• Are the resources used in the paper (e.g., data, code, computing infrastructure) already publicly available, committed to be shared by the authors, or easily substitutable with similar resources?
• Could someone else conduct similar experiments to verify the results?

• Thank Your Crowd!

When writing acknowledgments in the final, camera-ready versions of accepted papers, we encourage authors to thank their crowd contributors. After all, we couldn't conduct our research or run our crowd-powered systems without the many individual contributors who choose to participate!