Matthew L. Jockers
- General Social Sciences top 0.1%
- Computational and Text Analysis Methods 2
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- Digital Humanities and Scholarship 2
- Artificial Intelligence top 5%
- Authorship Attribution and Profiling 6
- Natural Language Processing Techniques 4
- Hate Speech and Cyberbullying Detection 2
- Artificial Intelligence Applications 1
- Conservation top 5%
- Communication top 10%
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- Names, Identity, and Discrimination Research 2
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- Language and cultural evolution 1
- Co-authors
- Daniela WittenDavid MimnoCraig S. CriddleMichael WitmoreFranco MorettiLei LeiTimothy R. TangherliniJianbo Gao
- Partner nations
- United StatesChinaUnited Kingdom
In The Last Decade
Matthew L. Jockers
19 papers receiving 626 citations
Hit Papers
Peers
Comparison fields: 5 of 90
- General Social Sciences 146
- Literature and Literary Theory 206
- Artificial Intelligence 381
- Conservation 26
- Communication 44
Countries citing papers authored by Matthew L. Jockers
This map shows the geographic impact of Matthew L. Jockers'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 Matthew L. Jockers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew L. Jockers more than expected).
Fields of papers citing papers by Matthew L. Jockers
This network shows the impact of papers produced by Matthew L. Jockers. 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 Matthew L. Jockers. The network helps show where Matthew L. Jockers may publish in the future.
Co-authorship network
The 15 scholars most cited alongside Matthew L. Jockers, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 20 | |
| 2 | Extracts Sentiment and Sentiment-Derived Plot Arcs from Text [R package syuzhet version 1.0.6] | 2020 | 8 |
| 3 | 2019 | 2 | |
| 4 | 2017 | 0 | |
| 5 | 2016 | 12 | |
| 6 | 2016 | 0 | |
| 7 | The Bestseller Code: Anatomy of the Blockbuster Novel | 2016 | 26 |
| 8 | 2016 | 16 | |
| 9 | 2014 | 66 | |
| 10 | Macroanalysisbreakdown → | 2013 | 233 |
| 11 | 2013 | 112 | |
| 12 | Computing and Visualizing the 19th-Century Literary Genome. | 2012 | 3 |
| 13 | 2012 | 1 | |
| 14 | 2012 | 10 | |
| 15 | 2012 | 2 | |
| 16 | Detecting and Characterizing National Style in the 19th Century Novel. | 2011 | 2 |
| 17 | 2011 | 44 | |
| 18 | 2010 | 108 | |
| 19 | 2008 | 47 | |
| 20 | 2004 | 0 |
About Matthew L. Jockers
Matthew L. Jockers is a scholar working on General Social Sciences, Artificial Intelligence and Literature and Literary Theory, having authored 25 papers that have together received 755 indexed citations. Recurring topics across this work include Authorship Attribution and Profiling (6 papers), Natural Language Processing Techniques (4 papers), Computational and Text Analysis Methods (2 papers), Digital Humanities and Scholarship (2 papers), Names, Identity, and Discrimination Research (2 papers), Hate Speech and Cyberbullying Detection (2 papers), Artificial Intelligence Applications (1 paper) and Language and cultural evolution (1 paper). The work is most often cited by research in General Social Sciences (146 citations), Literature and Literary Theory (206 citations) and Artificial Intelligence (381 citations). Matthew L. Jockers has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Daniela Witten, David Mimno, Craig S. Criddle, Michael Witmore, Franco Moretti, Lei Lei, Timothy R. Tangherlini, Jianbo Gao, Sophia Ananiadou and Ben Lambert. Their work appears in journals such as Digital Scholarship in the Humanities, Poetics, Nature, Nature Human Behaviour and Journal of Quantitative Linguistics.
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.