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Predicting Academic Citations: A Novel Approach using Ordered Probit Model

This paper, presented by Claudia Perlich, Foster Provost, and Sofus Macskassy at the KDD Cup, explores the challenge of predicting academic paper citations based on various features. Key methods include feature construction analyzing citation counts' temporal shape, author and paper reputation, and publication seasonality. The study addresses difficulties such as the asymmetric distribution of citations and the significance of normalization in training data. The final model employs an Ordered Probit technique and demonstrates effective prediction with a focus on citation distribution management.

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Predicting Academic Citations: A Novel Approach using Ordered Probit Model

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  1. Task1: Predicting Citations Claudia Perlich, Foster Provost & Sofus Macskassy New York University KDD Cup, August 2003

  2. Citations

  3. Step 1: Feature Construction • Temporal “shape” of citation counts: + counts for last 6 quarters and number of missing • Seasonality of Publishing (conferences): + quarterly dummies • Age of paper: + publication quarter • Author reputation: + number of papers, total number of citations + median change at same paper “age” • Paper reputation: + total and average number of citations per quarter

  4. Step 2: First Impression • Very hard problem • Across quarters it is very hard to beat consistently a constant (Median -2 scores 1403) • Distribution of target is not symmetric • L1 vs. L2 matters • Strong but instable seasonality • The median delta varies • Scaling differences across papers • Max citations: 2400, Average: 15 • Only 10% of papers ever had more than 5 in a quarter

  5. Step 3: Training Considerations • Normalization • Large variation in average citation counts → Normalize by average of the last 3 quarterly counts • Selection of Training Period • Potentially non-stationary due to conference schedules, age of database, ... → Keep entire dataset • Selection of Training Observations • Evaluation only on papers with at least 6 citations → Only use observation where the last citation count was at least 6

  6. Step 4: Model Class Selection

  7. Ordered Probit Model Y* 4 3 2 1 0 x X

  8. Results and Comparison • Final Model: • 10 categories: -7, ..., +2 • Ordered Probit with special prediction • Curtail predictions between -4 and 0 • Results: • Winning Participant: 1329 • Best constant (-2): 1403 • Curtailing Predictions between -4 and 0: 1360

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