Phocus: Picking Valuable Research from a Sea of Citations

TL;DR

Phocus introduces a novel citation analysis method that evaluates the influence of references by analyzing citation contexts and sentiment, aiming to improve academic evaluation beyond quantity-based metrics.

Contribution

The paper presents Phocus, a new mechanism that assesses citation influence through sentiment analysis and ranking, offering a more nuanced evaluation of academic impact.

Findings

Effective citation ranking based on sentiment analysis.

Improved assessment of individual author influence.

Potential to shift focus from quantity to quality in academic metrics.

Abstract

The deluge of new papers has significantly blocked the development of academics, which is mainly caused by author-level and publication-level evaluation metrics that only focus on quantity. Those metrics have resulted in several severe problems that trouble scholars focusing on the important research direction for a long time and even promote an impetuous academic atmosphere. To solve those problems, we propose Phocus, a novel academic evaluation mechanism for authors and papers. Phocus analyzes the sentence containing a citation and its contexts to predict the sentiment towards the corresponding reference. Combining others factors, Phocus classifies citations coarsely, ranks all references within a paper, and utilizes the results of the classifier and the ranking model to get the local influential factor of a reference to the citing paper. The global influential factor of the reference to the citing paper is the product of the local influential factor and the total influential factor of the citing paper. Consequently, an author's academic influential factor is the sum of his contributions to each paper he co-authors.