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.
This map shows the geographic impact of Mark Dredze'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 Mark Dredze with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Dredze more than expected).
This network shows the impact of papers produced by Mark Dredze. 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 Mark Dredze. The network helps show where Mark Dredze may publish in the future.
Co-authorship network of co-authors of Mark Dredze
This figure shows the co-authorship network connecting the top 25 collaborators of Mark Dredze.
A scholar is included among the top collaborators of Mark Dredze 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 Mark Dredze. Mark Dredze is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ayers, John W., Adam Poliak, Mark Dredze, et al.. (2023). Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Internal Medicine. 183(6). 589–589.1184 indexed citations breakdown →
Coppersmith, Glen, Mark Dredze, & Craig Harman. (2014). Quantifying Mental Health Signals in Twitter. 51–60.427 indexed citations breakdown →
14.
Joshi, Mahesh C., Mark Dredze, William W. Cohen, & Carolyn Penstein Rosé. (2013). What’s in a Domain? Multi-Domain Learning for Multi-Attribute Data. North American Chapter of the Association for Computational Linguistics. 29(171). 685–690.9 indexed citations
15.
Yeganova, Lana, et al.. (2012). Information retrieval and knowledge discovery in biomedical text : papers from the AAAI Fall Symposium.1 indexed citations
16.
Dredze, Mark, Bill N. Schilit, & Peter Norvig. (2009). Suggesting email view filters for triage and search. International Joint Conference on Artificial Intelligence. 1414–1419.7 indexed citations
17.
Crammer, Koby, Mark Dredze, & Fernando Pereira. (2008). Exact Convex Confidence-Weighted Learning. Neural Information Processing Systems. 21. 345–352.94 indexed citations
18.
Dredze, Mark, et al.. (2007). Learning Fast Classifiers for Image Spam..104 indexed citations
19.
Dredze, Mark, John Blitzer, & Fernando Pereira. (2006). "Sorry, I Forgot the Attachment:" Email Attachment Prediction.2 indexed citations
20.
Ando, Rie Kubota, Mark Dredze, & Tong Zhang. (2005). TREC 2005 genomics track experiments at IBM watson. Rare & Special e-Zone (The Hong Kong University of Science and Technology).16 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.