Reviewer Guidelines

All abstracts have to be treated confidentially and must not be used for purposes other than the abstract review process.

1. Log in to the BigSurv18 conference management system to access and review abstracts. Use the same login information (email and password) as when you submitted your abstract. 

2. Locate your assigned abstracts under the ‘Submit/edit reviews’ tab at the bottom of the page.

3. Read the abstract and provide a rating and an overall recommendation.

On a scale from 1 to 5, with 1 indicating “unacceptable” and 5 indicating “outstanding” please rate each abstract according to the following criteria. 

  • Readability: is the abstract well written and clearly organized?  (1-5)
  • Relevance: does this abstract describe research that either combines survey research and big data, emerging analysis techniques, or has the potential to extend to larger or complex data sets? (see also https://www.bigsurv18.org/abstracts) (1-5)
  • Technical quality: are the research methods sound, and do the results appropriately address the research questions? (1-5)
  • Novelty: is the research a novel addition to the community? (1-5)
  • Implications: will this research stimulate discussion at the conference?  Does it draw broader implications that would advance the literature? (1-5)
  • Overall: how would you rate the abstract submission overall? (1-5)

Remember that not all papers may fit directly with your area(s) of expertise. If you find that this is the case, your feedback is still valuable. These kinds of reviews may emphasize the clarity with which the author communicates to audiences outside of their sub-discipline, the quality of writing, and/or the general appeal of the contribution.

Please select one of the following recommendations: 

  • Accept presentation and recommend for publication in the preferred publication outlet
  • Accept presentation and instead recommend for publication in the other publication outlet
  • Accept presentation, but do not recommend for publication outlet
  • Accept as poster
  • Reject

* These recommendations are slightly adapted for the student paper competition.

When making your recommendation, please consider that

  • The special issue (published by Social Science Computer Review) will highlight the best empirical studies worthy of inclusion in a peer-reviewed journal and is intended for a wider audience of researchers interested in computational social science.  
  • The edited volume/monograph (published by John Wiley & Sons) is intended to serve as a quasi-textbook/reference on specific topic sections within Big Data + Survey Science, and may include literature reviews, frameworks, or empirical studies.

Please provide any additional feedback in the text box.

4. Once you select a recommendation, please confirm by clicking on the "Submit Review" button. 

5. Review the remaining abstracts following steps three to five.