Joseph Chee Chang

1.0k total citations
21 papers, 366 citations indexed

About

Joseph Chee Chang is a scholar working on Artificial Intelligence, Information Systems and Computer Science Applications. According to data from OpenAlex, Joseph Chee Chang has authored 21 papers receiving a total of 366 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 9 papers in Information Systems and 8 papers in Computer Science Applications. Recurrent topics in Joseph Chee Chang's work include Mobile Crowdsensing and Crowdsourcing (8 papers), Personal Information Management and User Behavior (5 papers) and Topic Modeling (5 papers). Joseph Chee Chang is often cited by papers focused on Mobile Crowdsensing and Crowdsourcing (8 papers), Personal Information Management and User Behavior (5 papers) and Topic Modeling (5 papers). Joseph Chee Chang collaborates with scholars based in United States, Canada and Israel. Joseph Chee Chang's co-authors include Aniket Kittur, Saleema Amershi, Ece Kamar, Nathan Hahn, Jieun Kim, Joel Chan, Dafna Shahaf, Tom Hope, Adam Perer and Amy X. Zhang and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, Proceedings of the ACM on Human-Computer Interaction and arXiv (Cornell University).

In The Last Decade

Joseph Chee Chang

19 papers receiving 359 citations

Peers

Joseph Chee Chang
David Piorkowski United States
Daniel J. Liebling United States
Vidya Rajaram United States
Jonathan Bragg United States
Daniel Alexander Smith United Kingdom
Thomas White United States
Julia Kiseleva Netherlands
David Piorkowski United States
Joseph Chee Chang
Citations per year, relative to Joseph Chee Chang Joseph Chee Chang (= 1×) peers David Piorkowski

Countries citing papers authored by Joseph Chee Chang

Since Specialization
Citations

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

Fields of papers citing papers by Joseph Chee Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Joseph Chee Chang. 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 Chee Chang. The network helps show where Joseph Chee Chang may publish in the future.

Co-authorship network of co-authors of Joseph Chee Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Joseph Chee Chang. A scholar is included among the top collaborators of Joseph Chee Chang 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 Chee Chang. Joseph Chee Chang 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
3.
Wang, Ruotong, et al.. (2025). Social-RAG: Retrieving from Group Interactions to Socially Ground AI Generation. 1–25. 2 indexed citations
4.
Malaviya, Chaitanya, Joseph Chee Chang, Dan Roth, et al.. (2025). Contextualized Evaluations: Judging Language Model Responses to Underspecified Queries. Transactions of the Association for Computational Linguistics. 13. 878–900.
5.
Naik, Aakanksha, Pao Siangliulue, Juho Kim, et al.. (2024). ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models. 9612–9631. 1 indexed citations
6.
Bragg, Jonathan, et al.. (2024). Mitigating Barriers to Public Social Interaction with Meronymous Communication. arXiv (Cornell University). 1–26. 2 indexed citations
7.
Guo, Yue, Joseph Chee Chang, Maria Antoniak, et al.. (2024). Personalized Jargon Identification for Enhanced Interdisciplinary Communication. PubMed. 2024. 4535–4550. 3 indexed citations
8.
Chang, Joseph Chee, et al.. (2024). Qlarify: Recursively Expandable Abstracts for Dynamic Information Retrieval over Scientific Papers. 1–21. 5 indexed citations
9.
Chang, Joseph Chee, et al.. (2021). When the Tab Comes Due:Challenges in the Cost Structure of Browser Tab Usage. 1–15. 7 indexed citations
10.
Chang, Joseph Chee, et al.. (2021). Tabs.do: Task-Centric Browser Tab Management. 663–676. 6 indexed citations
11.
Chang, Joseph Chee, Nathan Hahn, & Aniket Kittur. (2020). Mesh. 391–405. 19 indexed citations
12.
Chang, Joseph Chee, Nathan Hahn, Adam Perer, & Aniket Kittur. (2019). SearchLens. 498–509. 32 indexed citations
13.
Hahn, Nathan, Joseph Chee Chang, & Aniket Kittur. (2018). Bento Browser. 1–12. 12 indexed citations
14.
Chan, Joel, Joseph Chee Chang, Tom Hope, Dafna Shahaf, & Aniket Kittur. (2018). SOLVENT. Proceedings of the ACM on Human-Computer Interaction. 2(CSCW). 1–21. 40 indexed citations
15.
Huang, Ting-Hao, et al.. (2017). Evorus. 155–157. 4 indexed citations
16.
Chang, Joseph Chee, Saleema Amershi, & Ece Kamar. (2017). Revolt. 2334–2346. 142 indexed citations
17.
Chang, Joseph Chee, Nathan Hahn, & Aniket Kittur. (2016). Supporting Mobile Sensemaking Through Intentionally Uncertain Highlighting. 61–68. 13 indexed citations
18.
Chang, Joseph Chee, Aniket Kittur, & Nathan Hahn. (2016). Alloy. 3180–3191. 29 indexed citations
19.
Hahn, Nathan, Joseph Chee Chang, Jieun Kim, & Aniket Kittur. (2016). The Knowledge Accelerator. 2258–2270. 43 indexed citations
20.
Chang, Joseph Chee, et al.. (2012). Helping Our Own: NTHU NLPLAB System Description. 295–301. 1 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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