# Measuring academic influence: Not all citations are equal

arXiv: https://arxiv.org/abs/1501.06587 · HTML: https://mlsanity.com/paper/1501.06587/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2015.

## Built on by (most cited first, 11 of 11)

- [From Insights to Actions: The Impact of Interpretability and Analysis Research on NLP](https://mlsanity.com/paper/2406.12618/) (2024)
- [Enhancing Identification of Structure Function of Academic Articles Using Contextual Information](https://mlsanity.com/paper/2111.14110/) (2021)
- [Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science](https://mlsanity.com/paper/2409.19508/) (2024)
- [The Noisy Path from Source to Citation: Measuring How Scholars Engage with Past Research](https://mlsanity.com/paper/2502.20581/) (2025)
- [In-depth Research Impact Summarization through Fine-Grained Temporal Citation Analysis](https://mlsanity.com/paper/2505.14838/) (2025)
- [Multitask Learning for Citation Purpose Classification](https://mlsanity.com/paper/2106.13275/) (2021)
- [IDRBench: Understanding the Capability of Large Language Models on Interdisciplinary Research](https://mlsanity.com/paper/2507.15736/) (2025)
- [Who should I Collaborate with? A Comparative Study of Academia and Industry Research Collaboration in NLP](https://mlsanity.com/paper/2308.04524/) (2023)
- [CausalCite: A Causal Formulation of Paper Citations](https://mlsanity.com/paper/2311.02790/) (2023)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)
- [Crystal: Characterizing Relative Impact of Scholarly Publications](https://mlsanity.com/paper/2603.26791/) (2026)

## Abstract (arXiv)

The importance of a research article is routinely measured by counting how many times it has been cited. However, treating all citations with equal weight ignores the wide variety of functions that citations perform. We want to automatically identify the subset of references in a bibliography that have a central academic influence on the citing paper. For this purpose, we examine the effectiveness of a variety of features for determining the academic influence of a citation. By asking authors to identify the key references in their own work, we created a data set in which citations were labeled according to their academic influence. Using automatic feature selection with supervised machine learning, we found a model for predicting academic influence that achieves good performance on this data set using only four features. The best features, among those we evaluated, were those based on the number of times a reference is mentioned in the body of a citing paper. The performance of these features inspired us to design an influence-primed h-index (the hip-index). Unlike the conventional h-index, it weights citations by how many times a reference is mentioned. According to our experiments, the hip-index is a better indicator of researcher performance than the conventional h-index.