Michael Schlichtkrull
- Artificial Intelligence top 5%
- Topic Modeling 6
- Natural Language Processing Techniques 5
- Sentiment Analysis and Opinion Mining 3
- Authorship Attribution and Profiling 1
- Advanced Text Analysis Techniques 1
- Text Readability and Simplification 1
- Information Systems top 10%
- General Social Sciences top 10%
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- Misinformation and Its Impacts 3
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- Multimodal Machine Learning Applications 2
- Co-authors
- Andreas VlachosZhijiang GuoOana CocarascuArpit MittalXilun ChenDmytro OkhonkoSonal GuptaVladimir Karpukhin
- Journals
- Transactions of the Association for Computational Linguistics (1 paper)ORCA Online Research @Cardiff (Cardiff University) (1 paper)Queen Mary Research Online (Queen Mary University of London) (2 papers)
- Partner nations
- United KingdomNetherlandsDenmark
In The Last Decade
Michael Schlichtkrull
10 papers receiving 287 citations
Hit Papers
Peers
Comparison fields: 5 of 45
- Artificial Intelligence 241
- Health Informatics 6
- Information Systems 80
- General Social Sciences 10
- Sociology and Political Science 104
Countries citing papers authored by Michael Schlichtkrull
This map shows the geographic impact of Michael Schlichtkrull'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 Michael Schlichtkrull with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Schlichtkrull more than expected).
Fields of papers citing papers by Michael Schlichtkrull
This network shows the impact of papers produced by Michael Schlichtkrull. 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 Michael Schlichtkrull. The network helps show where Michael Schlichtkrull may publish in the future.
Co-authorship network
The 20 scholars most cited alongside Michael Schlichtkrull, 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 | 2024 | 0 | |
| 2 | 2024 | 6 | |
| 3 | 2024 | 1 | |
| 4 | 2023 | 2 | |
| 5 | 2023 | 5 | |
| 6 | 2022 | 45 | |
| 7 | A Survey on Automated Fact-Checkingbreakdown → | 2022 | 167 |
| 8 | 2021 | 42 | |
| 9 | 2017 | 10 | |
| 10 | 2016 | 7 | |
| 11 | 2015 | 10 |
About Michael Schlichtkrull
Michael Schlichtkrull is a scholar working on Artificial Intelligence, Human-Computer Interaction, Communication, Management Information Systems and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 295 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (5 papers), Misinformation and Its Impacts (3 papers), Sentiment Analysis and Opinion Mining (3 papers), Multimodal Machine Learning Applications (2 papers), Authorship Attribution and Profiling (1 paper), Advanced Text Analysis Techniques (1 paper) and Text Readability and Simplification (1 paper). The work is most often cited by research in Artificial Intelligence (241 citations), Health Informatics (6 citations), Information Systems (80 citations), General Social Sciences (10 citations) and Sociology and Political Science (104 citations). Michael Schlichtkrull has collaborated with scholars based in United Kingdom, Netherlands and Denmark. Frequent co-authors include Andreas Vlachos, Zhijiang Guo, Oana Cocarascu, Arpit Mittal, Xilun Chen, Dmytro Okhonko, Sonal Gupta, Vladimir Karpukhin, Christos Christodoulopoulos and Scott Yih. Their work appears in journals such as Transactions of the Association for Computational Linguistics, ORCA Online Research @Cardiff (Cardiff University) and Queen Mary Research Online (Queen Mary University of London).
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.