Phocus: Picking Valuable Research from a Sea of Citations
This addresses the issue for academics by providing a more qualitative evaluation method, though it appears incremental as it builds on existing citation analysis techniques.
The paper tackles the problem of academic evaluation being hindered by quantity-focused metrics, proposing Phocus, a mechanism that analyzes citation sentiment and context to assess the influence of references and authors, resulting in a new evaluation system.
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.