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.
Running experiments on Amazon Mechanical Turk
20103.1k citationsGabriele Paolacci, Jesse Chandler et al.Judgment and Decision Makingprofile →
Get another label? improving data quality and data mining using multiple, noisy labelers
2008787 citationsVictor S. Sheng, Foster Provost et al.profile →
Quality management on Amazon Mechanical Turk
2010678 citationsPanagiotis G. Ipeirotis, Foster Provost et al.Rare & Special e-Zone (The Hong Kong University of Science and Technology)profile →
Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content
2012482 citationsAnindya Ghose, Panagiotis G. Ipeirotis et al.profile →
Demographics and Dynamics of Mechanical Turk Workers
2018341 citationsPanagiotis G. Ipeirotis et al.profile →
Countries citing papers authored by Panagiotis G. Ipeirotis
Since
Specialization
Citations
This map shows the geographic impact of Panagiotis G. Ipeirotis'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 Panagiotis G. Ipeirotis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Panagiotis G. Ipeirotis more than expected).
Fields of papers citing papers by Panagiotis G. Ipeirotis
This network shows the impact of papers produced by Panagiotis G. Ipeirotis. 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 Panagiotis G. Ipeirotis. The network helps show where Panagiotis G. Ipeirotis may publish in the future.
Co-authorship network of co-authors of Panagiotis G. Ipeirotis
This figure shows the co-authorship network connecting the top 25 collaborators of Panagiotis G. Ipeirotis.
A scholar is included among the top collaborators of Panagiotis G. Ipeirotis 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 Panagiotis G. Ipeirotis. Panagiotis G. Ipeirotis is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Gerber, Elizabeth M. & Panagiotis G. Ipeirotis. (2015). Proceedings, The Third AAAI Conference on Human Computation and Crowdsourcing.6 indexed citations
Kokkodis, Marios & Panagiotis G. Ipeirotis. (2014). The Utility of Skills in Online Labor Markets. Journal of the Association for Information Systems.16 indexed citations
6.
Wang, Jing, Panagiotis G. Ipeirotis, & Foster Provost. (2013). Quality-Based Pricing for Crowdsourced Workers. SSRN Electronic Journal.26 indexed citations
7.
Faltings, Boi, Kevin Leyton‐Brown, & Panagiotis G. Ipeirotis. (2012). Proceedings of the 13th ACM Conference on Electronic Commerce.16 indexed citations
8.
Wang, Jing, Anindya Ghose, & Panagiotis G. Ipeirotis. (2012). Bonus, Disclosure, and Choice: What Motivates the Creation of High-Quality Paid Reviews?. Rare & Special e-Zone (The Hong Kong University of Science and Technology).35 indexed citations
9.
Ghose, Anindya, Panagiotis G. Ipeirotis, & Beibei Li. (2012). Search Less, Find More? Examining Limited Consumer Search with Social Media and Product Search Engines. International Conference on Information Systems.7 indexed citations
10.
Aral, Sinan, Panagiotis G. Ipeirotis, & Sean J. Taylor. (2011). Content and Context: Identifying the Impact of Qualitative Information on Consumer Choice. Journal of the Association for Information Systems.6 indexed citations
11.
Paolacci, Gabriele, Jesse Chandler, & Panagiotis G. Ipeirotis. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making. 5(5). 411–419.1 indexed citations
12.
Paolacci, Gabriele, Jesse Chandler, & Panagiotis G. Ipeirotis. (2010). Running experiments on Amazon Mechanical Turk. Judgment and Decision Making. 5(5). 411–419.3051 indexed citations breakdown →
13.
Ghose, Anindya, Panagiotis G. Ipeirotis, & Beibei Li. (2010). DESIGNING RANKING SYSTEMS FOR HOTELS ON TRAVEL SEARCH ENGINES TO ENHANCE USER EXPERIENCE. International Conference on Information Systems. 113.2 indexed citations
14.
Bennett, Paul N., Raman Chandrasekar, Max Chickering, et al.. (2009). Proceedings of the ACM SIGKDD Workshop on Human Computation. Knowledge Discovery and Data Mining.1 indexed citations
15.
Sheng, Victor S., Foster Provost, & Panagiotis G. Ipeirotis. (2008). Get Another Label? Improving Data Quality and Data Mining Using Multiple, Noisy Labelers. The Faculty Digital Archive (New York University).58 indexed citations
16.
Li, Beibei, Anindya Ghose, & Panagiotis G. Ipeirotis. (2008). Stay Elsewhere? Improving Local Search for Hotels Using Econometric Modeling and Image Classification.6 indexed citations
17.
Ghose, Anindya, Panagiotis G. Ipeirotis, & Arun Sundararajan. (2007). Opinion Mining using Econometrics: A Case Study on Reputation Systems. Meeting of the Association for Computational Linguistics. 416–423.88 indexed citations
Agichtein, Eugene, Panagiotis G. Ipeirotis, & Luis Gravano. (2003). Modeling Query-Based Access to Text Databases.. 87–92.22 indexed citations
20.
Gravano, Luis, Panagiotis G. Ipeirotis, H. V. Jagadish, et al.. (2001). Using q-grams in a DBMS for Approximate String Processing.. IEEE Data(base) Engineering Bulletin. 24. 28–34.71 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.