James Hollenczer
Biographic Data
| ID | 4430286 |
|---|---|
| NAME | James Hollenczer |
| GIVEN NAMES | James |
| FAMILY NAME | Hollenczer |
| SIGNATURE | HOLLENCZER J |
| AFFILIATIONS | University of Oklahoma |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 2 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 1 |
Efficient Duplicate Comment Detection for Rulemaking Agencies With Unsupervised Deep Learning
Government agencies tasked with soliciting comments from the American public in response to changes in rulemaking have long been interested in finding effective techniques to automatically process spam comments, including flagging and filtering duplicate comments. Duplicate submissions are problematic because they obscure genuine public input and may amplify fringe opinions nonrepresentative of the majority of the American public. Given that dupl…
Managing Opinion Spamming with AI in Regulatory Public Engagement
From Pre-Science to Paradigm Shift
This article reviews the evolution of public relations from its pre-science period to the present-day using Thomas Kuhn’s theory of scientific development. The review discusses how public relations scholarship has grown and reached scientific maturity, in parallel with the institutionalization of public relations’ professional practice and higher education. This article highlights the evolving nature of the discipline, including the paradigmatic …
From Pre-Science to Paradigm Shift
This article reviews the evolution of public relations from its pre-science period to the present-day using Thomas Kuhn’s theory of scientific development. The review discusses how public relations scholarship has grown and reached scientific maturity, in parallel with the institutionalization of public relations’ professional practice and higher education. This article highlights the evolving nature of the discipline, including the paradigmatic …
Efficient Duplicate Comment Detection for Rulemaking Agencies With Unsupervised Deep Learning
Government agencies tasked with soliciting comments from the American public in response to changes in rulemaking have long been interested in finding effective techniques to automatically process spam comments, including flagging and filtering duplicate comments. Duplicate submissions are problematic because they obscure genuine public input and may amplify fringe opinions nonrepresentative of the majority of the American public. Given that dupl…
Managing Opinion Spamming with AI in Regulatory Public Engagement
Law (3 works) · Political science (3 works) · Computer Science (2 works) · Hate Speech and Cyberbullying Detection (2 works) · Artificial Intelligence (1 work) · Business (1 work) · Communication in Education and Healthcare (1 work) · Data science (1 work) · Deep learning (1 work) · Epistemology (1 work)