Joseph D. Prusa

1.2k total citations · 1 hit paper
28 papers, 772 citations indexed

About

Joseph D. Prusa is a scholar working on Artificial Intelligence, Information Systems and Sociology and Political Science. According to data from OpenAlex, Joseph D. Prusa has authored 28 papers receiving a total of 772 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 11 papers in Information Systems and 4 papers in Sociology and Political Science. Recurrent topics in Joseph D. Prusa's work include Sentiment Analysis and Opinion Mining (17 papers), Spam and Phishing Detection (10 papers) and Text and Document Classification Technologies (9 papers). Joseph D. Prusa is often cited by papers focused on Sentiment Analysis and Opinion Mining (17 papers), Spam and Phishing Detection (10 papers) and Text and Document Classification Technologies (9 papers). Joseph D. Prusa collaborates with scholars based in United States and United Kingdom. Joseph D. Prusa's co-authors include Taghi M. Khoshgoftaar, Mike Crawford, Aaron N. Richter, David J. Dittman, Amri Napolitano, Naeem Seliya, Paul Morris, Michael R. Lowe and Joffrey L. Leevy and has published in prestigious journals such as Information Systems Frontiers, Journal Of Big Data and Social Network Analysis and Mining.

In The Last Decade

Joseph D. Prusa

27 papers receiving 730 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Survey of review spam detection using machine learning techniques

2015 295 citations Mike Crawford, Taghi M. Khoshgoftaar et al. Journal Of Big Data profile →

Peers

Joseph D. Prusa

AI

IS

SPS

CNC

SP

Gianluca Lax Italy

P Deepak India

Murat Can Ganiz Türkiye

Diana Palsetia United States

Guoyong Cai China

Yi Fang United States

P. M. Rubesh Anand India

Jyun‐Yu Jiang United States

Engin Demir Türkiye

Gianluca Lax Italy

Joseph D. Prusa

255 ×0.5

AI

289 ×0.8

IS

133 ×0.8

SPS

195 ×1.8

CNC

61 ×0.8

SP

Citations per year, relative to Joseph D. Prusa Joseph D. Prusa (= 1×) peers Gianluca Lax

Countries citing papers authored by Joseph D. Prusa

Since Specialization

Citations

This map shows the geographic impact of Joseph D. Prusa's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Joseph D. Prusa with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joseph D. Prusa more than expected).

Fields of papers citing papers by Joseph D. Prusa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joseph D. Prusa. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Joseph D. Prusa. The network helps show where Joseph D. Prusa may publish in the future.

Co-authorship network of co-authors of Joseph D. Prusa

This figure shows the co-authorship network connecting the top 25 collaborators of Joseph D. Prusa. A scholar is included among the top collaborators of Joseph D. Prusa based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Joseph D. Prusa. Joseph D. Prusa is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Prusa, Joseph D., et al.. (2024). Low-shot learning and class imbalance: a survey. Journal Of Big Data. 11(1). 14 indexed citations

2.

Lowe, Michael R., Joseph D. Prusa, Joffrey L. Leevy, & Taghi M. Khoshgoftaar. (2024). Advancing machine learning with OCR2SEQ: an innovative approach to multi-modal data augmentation. Journal Of Big Data. 11(1).

3.

Morris, Paul, et al.. (2019). Investigation of Maxout Activations on Convolutional Neural Networks for Big Data Text Sentiment Analysis.. The Florida AI Research Society. 250–256. 2 indexed citations

4.

Prusa, Joseph D., et al.. (2018). Location-Based Twitter Sentiment Analysis for Predicting the U.S. 2016 Presidential Election.. The Florida AI Research Society. 265–270. 6 indexed citations

5.

Prusa, Joseph D. & Taghi M. Khoshgoftaar. (2017). Deep Neural Network Architecture for Character-Level Learning on Short Text.. The Florida AI Research Society. 353–358. 3 indexed citations

6.

Prusa, Joseph D., et al.. (2017). Exploring the Effectiveness of Twitter at Polling the United States 2016 Presidential Election. 283–290. 15 indexed citations

7.

Khoshgoftaar, Taghi M., et al.. (2017). Improving detection of untrustworthy online reviews using ensemble learners combined with feature selection. Social Network Analysis and Mining. 7(1). 14 indexed citations

8.

Prusa, Joseph D. & Taghi M. Khoshgoftaar. (2017). Training Convolutional Networks on Truncated Text. 2. 330–335. 1 indexed citations

9.

Prusa, Joseph D., Taghi M. Khoshgoftaar, & Amri Napolitano. (2016). Necessity of Feature Selection when Augmenting Tweet Sentiment Feature Spaces with Emoticons. The Florida AI Research Society. 614–620. 1 indexed citations

10.

Prusa, Joseph D., Taghi M. Khoshgoftaar, & Naeem Seliya. (2016). Enhancing Ensemble Learners with Data Sampling on High-Dimensional Imbalanced Tweet Sentiment Data. The Florida AI Research Society. 322–328. 10 indexed citations

11.

Prusa, Joseph D. & Taghi M. Khoshgoftaar. (2016). Comparing Approaches for Combining Data Sampling and Feature Selection to Address Key Data Quality Issues in Tweet Sentiment Analysis.. The Florida AI Research Society. 608–613. 3 indexed citations

12.

Crawford, Mike, Taghi M. Khoshgoftaar, & Joseph D. Prusa. (2016). Reducing Feature Set Explosion to Facilitate Real-World Review Spam Detection. The Florida AI Research Society. 304–309. 19 indexed citations

13.

Khoshgoftaar, Taghi M., et al.. (2016). Integrating Multiple Data Sources to Enhance Sentiment Prediction. 285–291. 6 indexed citations

14.

Khoshgoftaar, Taghi M., et al.. (2016). An Investigation of Ensemble Techniques for Detection of Spam Reviews. 127–133. 8 indexed citations

15.

Khoshgoftaar, Taghi M., et al.. (2016). Cross-Domain Sentiment Analysis: An Empirical Investigation. 160–165. 28 indexed citations

16.

Prusa, Joseph D., Taghi M. Khoshgoftaar, & David J. Dittman. (2015). Impact of Feature Selection Techniques for Tweet Sentiment Classification.. The Florida AI Research Society. 299–304. 38 indexed citations

17.

Crawford, Mike, et al.. (2015). Survey of review spam detection using machine learning techniques. Journal Of Big Data. 2(1). 295 indexed citations breakdown →

18.

Prusa, Joseph D., Taghi M. Khoshgoftaar, & Amri Napolitano. (2015). Utilizing Ensemble, Data Sampling and Feature Selection Techniques for Improving Classification Performance on Tweet Sentiment Data. 535–542. 6 indexed citations

19.

Prusa, Joseph D., Taghi M. Khoshgoftaar, David J. Dittman, & Amri Napolitano. (2015). Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data. 197–202. 104 indexed citations

20.

Prusa, Joseph D., et al.. (2015). Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data. 252–257. 36 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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