KDD is a premier conference that brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. These slides give some statistics about the KDD program and present data science view of the paper review process: 1100 submissions, 3000 reviews, and 150 accepted papers.

1. Data Science of the
KDD ‘14 Review Process
Jure Leskovec (Stanford) and
Wei Wang (UCLA)
Joint work with
Jason Hirshman and David Zeng (Stanford)

2. KDD 2014 Research Track
Statistics

3. KDD 2014 Program
Largest KDD program ever:
• 151 research papers (20% growth over KDD’13)
• 43 industry & govt. papers (30% growth)
• 26 workshops (75% growth)
• 11 tutorials (83% growth)
Program highlights:
• Paper spotlights early morning (8:15am)
• Oral presentations (Mon-Wed)
• Posters at the reception (Tue night)

4. KDD 2014 Research Track
• 1036 submissions from 2600 authors
– 42% increase over KDD ’13
• 151 papers:
– Acceptance rate
14.6%
0
200
400
600
800
1000
1200
2000 2005 2010 2015
KDD year
Numberofsubmissions

5. KDD Reviewing Process
46 Senior PC members + 340 PC members
• 2971 reviews in total
(Rough) Acceptance rule:
• Raw review score AND Standardized review score AND Raw
meta-review AND Standardized meta-review score ≥ Weak
Accept
• 110 papers matched (immediate accepts)
• Remaining papers were discussed with meta-reviewers and
final decisions were made

6. Submissions per Country

7. Acceptance Rate per Country

8. Acceptance by Subject Area

9. Predicting Paper Acceptance
Features Used Accuracy
Random Guessing 0.50
Paper Abstract 0.57
Author Status (Past paper counts) 0.64
Author Status (DBLP graph connectivity) 0.61
Author Status (Counts + Graph) 0.65
Reviewer (Similarity, Graph distance to authors) 0.60
All (Abstract, Author Status, and Reviewer) 0.65

10. Predicting Paper Acceptance
from the Review Text
Features Used
Paper:
Accepted?
Review:
Score > 0?
Random Guessing 0.50 0.50
Review Text 0.68 0.72
Review Text + Numeric Score
(Novelty, Presentation)
0.77 0.77
Human Reading of Review Text 0.88 0.73

11. I’m submitting a paper:
What correlates with acceptance?

12. Academia + Industry Papers do Better

13. Submissions per Author: 5 is best!

14. No benefit in submitting >5 papers!

15. Having more authors (seems to) help

16. It is the most experienced author
that matters!

17. What insights can we gain on
the review process?

18. Most reviews are Weak Rejects

19. More granularity is needed at the
Weak Reject / Weak Accept level
Reviewagreeswiththefinaloutcome

20. Review length is a good determinant
of a review’s influence/quality
Reviewagreeswiththefinaloutcome

21. Shorter reviews are used for
clear accepts and rejects

22. Never review co-author’s papers

23. The Curse of the Review
Submission Deadline

24. Over 50% reviews submitted in the last 5 days
Over 20% reviews submitted in the last 24 hours
10% of reviews
submitted late

25. Ratings increase near the deadline
Weak Rejects
increase while
Rejects decrease

26. Reviews submitted late are less likely
to agree with final outcome

27. Late reviews are shorter

28. Review quality drops: Accuracy of
predicting score from review text

29. Conclusions
• To get your papers accepted to KDD:
– Collaborate in multidisciplinary teams
– Have a senior author on board
– Do not submit more than 5 papers
• To improve KDD community standards:
– Avoid Weak Reject/Weak Accept scores
– Write longer and clearer reviews
– Submit reviews early!