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.
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter
2016932 citationsDirk Hovy et al.Research at the University of Copenhagen (University of Copenhagen)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Dirk Hovy'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 Dirk Hovy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dirk Hovy more than expected).
This network shows the impact of papers produced by Dirk Hovy. 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 Dirk Hovy. The network helps show where Dirk Hovy may publish in the future.
Co-authorship network of co-authors of Dirk Hovy
This figure shows the co-authorship network connecting the top 25 collaborators of Dirk Hovy.
A scholar is included among the top collaborators of Dirk Hovy 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 Dirk Hovy. Dirk Hovy is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hovy, Dirk, et al.. (2016). Exploring Language Variation Across Europe - A Web-based Tool for Computational Sociolinguistics.. Language Resources and Evaluation. 2986–2989.4 indexed citations
12.
Hovy, Dirk, Barbara Plank, Hėctor Martínez Alonso, & Anders Søgaard. (2015). Mining for unambiguous instances to adapt POS taggers to new domains. Research at the University of Copenhagen (University of Copenhagen).2 indexed citations
13.
Plank, Barbara, Dirk Hovy, Ryan McDonald, & Anders Søgaard. (2014). Adapting taggers to Twitter with not-so-distant supervision. Research at the University of Copenhagen (University of Copenhagen). 1783–1792.22 indexed citations
14.
Søgaard, Anders, Barbara Plank, & Dirk Hovy. (2014). Selection Bias, Label Bias, and Bias in Ground Truth. International Conference on Computational Linguistics. 11–13.5 indexed citations
15.
Hovy, Dirk, Barbara Plank, & Anders Søgaard. (2014). When POS data sets don't add up: Combatting sample bias. Language Resources and Evaluation. 4472–4475.8 indexed citations
Hovy, Dirk, Taylor Berg-Kirkpatrick, Ashish Vaswani, & Eduard Hovy. (2013). Learning Whom to Trust with MACE. North American Chapter of the Association for Computational Linguistics. 1120–1130.162 indexed citations
18.
Hovy, Dirk, James Fan, Alfio Gliozzo, Siddharth Patwardhan, & Christopher Welty. (2012). When Did that Happen? — Linking Events and Relations to Timestamps. Conference of the European Chapter of the Association for Computational Linguistics. 185–193.14 indexed citations
19.
Hovy, Dirk, Ashish Vaswani, Stephen Tratz, David Chiang, & Eduard Hovy. (2011). Models and Training for Unsupervised Preposition Sense Disambiguation. Meeting of the Association for Computational Linguistics. 323–328.7 indexed citations
20.
Hovy, Dirk, Stephen Tratz, & Eduard Hovy. (2010). What's in a Preposition? Dimensions of Sense Disambiguation for an Interesting Word Class. International Conference on Computational Linguistics. 454–462.21 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.