Stephen Wan

1.6k total citations · 1 hit paper
61 papers, 917 citations indexed

About

Stephen Wan is a scholar working on Artificial Intelligence, Information Systems and Sociology and Political Science. According to data from OpenAlex, Stephen Wan has authored 61 papers receiving a total of 917 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Artificial Intelligence, 7 papers in Information Systems and 6 papers in Sociology and Political Science. Recurrent topics in Stephen Wan's work include Topic Modeling (27 papers), Natural Language Processing Techniques (26 papers) and Advanced Text Analysis Techniques (13 papers). Stephen Wan is often cited by papers focused on Topic Modeling (27 papers), Natural Language Processing Techniques (26 papers) and Advanced Text Analysis Techniques (13 papers). Stephen Wan collaborates with scholars based in Australia, United States and United Kingdom. Stephen Wan's co-authors include Cécile Paris, Robert Dale, Karin Verspoor, Bridianne O’Dea, Philip J. Batterham, Alison L. Calear, Helen Christensen, Mark Dras, Kathy McKeown and Katja Filippova and has published in prestigious journals such as Information Processing & Management, Foods and Teaching in Higher Education.

In The Last Decade

Stephen Wan

55 papers receiving 822 citations

Hit Papers

Detecting suicidality on Twitter 2015 2026 2018 2022 2015 50 100 150 200 250

Peers

Stephen Wan
Manas Gaur United States
Bart Desmet Belgium
Doğan Can United States
Cristina Robles United States
Marilyn Walker United States
Eduardo Blanco United States
Karthik Dinakar United States
Viet-An Nguyen United States
Zhiyuan Lin United States
Manas Gaur United States
Stephen Wan
Citations per year, relative to Stephen Wan Stephen Wan (= 1×) peers Manas Gaur

Countries citing papers authored by Stephen Wan

Since Specialization
Citations

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

Fields of papers citing papers by Stephen Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Stephen Wan

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

All Works

20 of 20 papers shown
1.
Pennells, Jordan, et al.. (2025). An Evaluation of Large Language Models for Supplementing a Food Extrusion Dataset. Foods. 14(8). 1355–1355.
2.
Dai, Xiang, et al.. (2024). An adaptive approach to noisy annotations in scientific information extraction. Information Processing & Management. 61(6). 103857–103857. 1 indexed citations
4.
Wan, Stephen & Julian Lowe. (2024). Market entry strategy into China by entrepreneurial new venture firms. Swinburne Research Bank (Swinburne University of Technology). 660.
7.
Wan, Stephen, Sarvnaz Karimi, Cécile Paris, et al.. (2023). SciHarvester: Searching Scientific Documents for Numerical Values. 3135–3139.
8.
Joshi, Aditya, et al.. (2019). Red-faced ROUGE: Examining the Suitability of ROUGE for Opinion Summary Evaluation.. Figshare. 52–60. 6 indexed citations
9.
Nguyen, Vincent, et al.. (2017). CSIRO at 2017 TREC Precision Medicine Track.. Text REtrieval Conference. 2 indexed citations
10.
Xu, Qiongkai, et al.. (2017). Demographic Inference on Twitter using Recursive Neural Networks. 471–477. 23 indexed citations
11.
Robinson, Bella, et al.. (2016). CSIRO Data61 at the WNUT Geo Shared Task. International Conference on Computational Linguistics. 218–226. 12 indexed citations
12.
Wan, Stephen. (2015). CLAS at the MediaEval 2015 C@merata Task. MediaEval.
13.
Wan, Stephen. (2014). The CLAS System at the MediaEval 2017 C@merata Task.. MediaEval. 1 indexed citations
14.
Wan, Stephen, Cécile Paris, & Robert Dale. (2009). Whetting the appetite of scientists. 59–68. 13 indexed citations
15.
Wan, Stephen & Cécile Paris. (2008). Experimenting with Clause Segmentation for Text Summarization.. Theory and applications of categories. 4 indexed citations
16.
Wan, Stephen, et al.. (2008). From aggravated to aggregated search: Improving utility through coherent organisations of an answer space. 4 indexed citations
17.
Colineau, Nathalie, Cécile Paris, Stephen Wan, & Robert Dale. (2006). Proceedings of the Fourth International Natural Language Generation Conference. 8 indexed citations
18.
Wan, Stephen, Robert Dale, & Mark Dras. (2005). Searching for Grammaticality: Propagating Dependencies in the Viterbi Algorithm.. 7 indexed citations
19.
Wan, Stephen & Karin Verspoor. (1998). Automatic English-Chinese name transliteration for development of multilingual resources. 2. 1352–1356. 77 indexed citations
20.
Wan, Stephen & Karin Verspoor. (1998). Automatic English-Chinese name transliteration for development of multilingual resources. 2. 1352–1356. 24 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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