A Causal Inference Approach for Quantifying Research Impact

• URL: http://arxiv.org/abs/2503.13485v1
• Date: Fri, 07 Mar 2025 10:06:42 GMT
• Title: A Causal Inference Approach for Quantifying Research Impact
• Authors: Keiichi Ochiai, Yutaka Matsuo,
• Abstract summary: The number of citations and impact factor can be used to measure the impact for individual research.<n>Deep learning significantly affects computer vision and natural language processing.<n>Our method revealed that the impact of deep learning was 3.1 times of the impact of interpretability for ML models.
• Score: 23.828907866209494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has had a great impact on various fields of computer science by enabling data-driven representation learning in a decade. Because science and technology policy decisions for a nation can be made on the impact of each technology, quantifying research impact is an important task. The number of citations and impact factor can be used to measure the impact for individual research. What would have happened without the research, however, is fundamentally a counterfactual phenomenon. Thus, we propose an approach based on causal inference to quantify the research impact of a specific technical topic. We leverage difference-in-difference to quantify the research impact by applying to bibliometric data. First, we identify papers of a specific technical topic using keywords or category tags from Microsoft Academic Graph, which is one of the largest academic publication dataset. Next, we build a paper citation network between each technical field. Then, we aggregate the cross-field citation count for each research field. Finally, the impact of a specific technical topic for each research field is estimated by applying difference-in-difference. Evaluation results show that deep learning significantly affects computer vision and natural language processing. Besides, deep learning significantly affects cross-field citation especially for speech recognition to computer vision and natural language processing to computer vision. Moreover, our method revealed that the impact of deep learning was 3.1 times of the impact of interpretability for ML models.

Related papers

• Comprehensive Manuscript Assessment with Text Summarization Using 69707 articles [10.943765373420135]
  We harness Scopus to curate a significantly comprehensive and large-scale dataset of information from 69707 scientific articles. We propose a deep learning methodology for the impact-based classification tasks, which leverages semantic features extracted from the manuscripts and paper metadata.
  arXiv  Detail & Related papers  (2025-03-26T07:56:15Z)
• Diversity of Expertise is Key to Scientific Impact: a Large-Scale Analysis in the Field of Computer Science [1.8794304012790348]
  We analysed how the diversity of research fields within a research team relates to the number of citations their papers received in the upcoming 5 years. This suggests that, at least in Computer Science, diversity of expertise is key to scientific impact.
  arXiv  Detail & Related papers  (2023-06-27T09:56:44Z)
• Expanding the Role of Affective Phenomena in Multimodal Interaction Research [57.069159905961214]
  We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing. We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers. We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
  arXiv  Detail & Related papers  (2023-05-18T09:08:39Z)
• The Ways of Words: The Impact of Word Choice on Information Engagement and Decision Making [3.09766013093045]
  This study explores the impact of phrasing, specifically word choice, on information engagement (IE) and decision making. The findings suggest that phrasing can have a significant effect on the interpretation of and interaction with digital information.
  arXiv  Detail & Related papers  (2023-05-16T20:46:36Z)
• Citation Trajectory Prediction via Publication Influence Representation Using Temporal Knowledge Graph [52.07771598974385]
  Existing approaches mainly rely on mining temporal and graph data from academic articles. Our framework is composed of three modules: difference-preserved graph embedding, fine-grained influence representation, and learning-based trajectory calculation. Experiments are conducted on both the APS academic dataset and our contributed AIPatent dataset.
  arXiv  Detail & Related papers  (2022-10-02T07:43:26Z)
• Panning for gold: Lessons learned from the platform-agnostic automated detection of political content in textual data [48.7576911714538]
  We discuss how these techniques can be used to detect political content across different platforms. We compare the performance of three groups of detection techniques relying on dictionaries, supervised machine learning, or neural networks. Our results show the limited impact of preprocessing on model performance, with the best results for less noisy data being achieved by neural network- and machine-learning-based models.
  arXiv  Detail & Related papers  (2022-07-01T15:23:23Z)
• SciTweets -- A Dataset and Annotation Framework for Detecting Scientific Online Discourse [2.3371548697609303]
  Scientific topics, claims and resources are increasingly debated as part of online discourse. This has led to both significant societal impact and increased interest in scientific online discourse from various disciplines. Research across disciplines currently suffers from a lack of robust definitions of the various forms of science-relatedness.
  arXiv  Detail & Related papers  (2022-06-15T08:14:55Z)
• Semantic Analysis for Automated Evaluation of the Potential Impact of Research Articles [62.997667081978825]
  This paper presents a novel method for vector representation of text meaning based on information theory. We show how this informational semantics is used for text classification on the basis of the Leicester Scientific Corpus. We show that an informational approach to representing the meaning of a text has offered a way to effectively predict the scientific impact of research papers.
  arXiv  Detail & Related papers  (2021-04-26T20:37:13Z)
• Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework [83.21732533130846]
  The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2. It presents the design of two classes of deep neural networks trained with these databases. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
  arXiv  Detail & Related papers  (2021-03-29T17:36:20Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)
• Machine Identification of High Impact Research through Text and Image Analysis [0.4737991126491218]
  We present a system to automatically separate papers with a high from those with a low likelihood of gaining citations. Our system uses both a visual classifier, useful for surmising a document's overall appearance, and a text classifier, for making content-informed decisions.
  arXiv  Detail & Related papers  (2020-05-20T19:12:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.