Nadia Conroy

1.7k total citations · 2 hit papers
8 papers, 1.0k citations indexed

About

Nadia Conroy is a scholar working on Sociology and Political Science, Information Systems and Artificial Intelligence. According to data from OpenAlex, Nadia Conroy has authored 8 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Sociology and Political Science, 3 papers in Information Systems and 3 papers in Artificial Intelligence. Recurrent topics in Nadia Conroy's work include Spam and Phishing Detection (3 papers), Misinformation and Its Impacts (3 papers) and Online Learning and Analytics (2 papers). Nadia Conroy is often cited by papers focused on Spam and Phishing Detection (3 papers), Misinformation and Its Impacts (3 papers) and Online Learning and Analytics (2 papers). Nadia Conroy collaborates with scholars based in Canada, Nigeria and United States. Nadia Conroy's co-authors include Victoria L. Rubin, Yimin Chen, Anatoliy Gruzd, Lu Xiao, Sarah Cornwell and Toluwase Asubiaro and has published in prestigious journals such as Journal of the Association for Information Science and Technology, Proceedings of the Association for Information Science and Technology and Figshare.

In The Last Decade

Nadia Conroy

8 papers receiving 950 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.

Automatic deception detection: Methods for finding fake news

2015 592 citations Nadia Conroy, Victoria L. Rubin et al. Proceedings of the Association for Information Science and Technology profile →
Deception detection for news: Three types of fakes

2015 327 citations Victoria L. Rubin, Yimin Chen et al. Proceedings of the Association for Information Science and Technology profile →

Peers

Nadia Conroy

SPS

IS

AI

SP

COMMU

Jin Yea Jang United States

Yimin Chen Canada

Niall Conroy Canada

Júlio C. S. Reis Brazil

Johannes Kiesel Germany

Giovanni Luca Ciampaglia United States

Geraldine Wong Sak Hoi United Kingdom

Lisa Kaati Sweden

Hannah Rashkin United States

Yi-Fang Wu United States

Jin Yea Jang United States

Nadia Conroy

751 ×0.8

SPS

454 ×0.7

IS

530 ×1.2

AI

108 ×0.5

SP

112 ×0.7

COMMU

Citations per year, relative to Nadia Conroy Nadia Conroy (= 1×) peers Jin Yea Jang

Countries citing papers authored by Nadia Conroy

Since Specialization

Citations

This map shows the geographic impact of Nadia Conroy'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 Nadia Conroy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nadia Conroy more than expected).

Fields of papers citing papers by Nadia Conroy

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nadia Conroy. 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 Nadia Conroy. The network helps show where Nadia Conroy may publish in the future.

Co-authorship network of co-authors of Nadia Conroy

This figure shows the co-authorship network connecting the top 25 collaborators of Nadia Conroy. A scholar is included among the top collaborators of Nadia Conroy 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 Nadia Conroy. Nadia Conroy is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Gruzd, Anatoliy & Nadia Conroy. (2020). Learning Analytics Dashboard for Teaching with Twitter. Proceedings of the ... Annual Hawaii International Conference on System Sciences. 7 indexed citations

2.

Rubin, Victoria L., et al.. (2019). litrl/litrl_code: Litrl Browser Experimental 0.14.0.0 Public. Figshare. 1 indexed citations

3.

Rubin, Victoria L., et al.. (2019). A News Verification Browser for the Detection of Clickbait, Satire, and Falsified News. The Journal of Open Source Software. 4(35). 1208–1208. 6 indexed citations

4.

Gruzd, Anatoliy & Nadia Conroy. (2018). Designing a learning analytics dashboard for twitter-facilitated teaching. Figshare. 1–4. 2 indexed citations

5.

Xiao, Lu & Nadia Conroy. (2017). Discourse relations in rationale‐containing text‐segments. Journal of the Association for Information Science and Technology. 68(12). 2783–2794. 1 indexed citations

6.

Conroy, Nadia, Victoria L. Rubin, & Yimin Chen. (2015). Automatic deception detection: Methods for finding fake news. Proceedings of the Association for Information Science and Technology. 52(1). 1–4. 592 indexed citations breakdown →

7.

Rubin, Victoria L., Yimin Chen, & Nadia Conroy. (2015). Deception detection for news: Three types of fakes. Proceedings of the Association for Information Science and Technology. 52(1). 1–4. 327 indexed citations breakdown →

8.

Chen, Yimin, Nadia Conroy, & Victoria L. Rubin. (2015). News in an online world: The need for an “automatic crap detector”. Proceedings of the Association for Information Science and Technology. 52(1). 1–4. 93 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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