Johannes Kiesel

1.8k total citations · 1 hit paper
36 papers, 821 citations indexed

About

Johannes Kiesel is a scholar working on Artificial Intelligence, Information Systems and Sociology and Political Science. According to data from OpenAlex, Johannes Kiesel has authored 36 papers receiving a total of 821 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 14 papers in Information Systems and 3 papers in Sociology and Political Science. Recurrent topics in Johannes Kiesel's work include Topic Modeling (14 papers), Natural Language Processing Techniques (11 papers) and Web Data Mining and Analysis (7 papers). Johannes Kiesel is often cited by papers focused on Topic Modeling (14 papers), Natural Language Processing Techniques (11 papers) and Web Data Mining and Analysis (7 papers). Johannes Kiesel collaborates with scholars based in Germany, Australia and Netherlands. Johannes Kiesel's co-authors include Benno Stein, Martin Potthast, Janek Bevendorff, Henning Wachsmuth, Matthias Hagen, Emmanuel Vincent, Rishabh Shukla, David Corney, Khalid Al‐Khatib and Yamen Ajjour and has published in prestigious journals such as ACM Transactions on Information Systems, ACM SIGIR Forum and Journal of Data and Information Quality.

In The Last Decade

Johannes Kiesel

32 papers receiving 781 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

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.

A Stylometric Inquiry into Hyperpartisan and Fake News

2018 417 citations Martin Potthast, Johannes Kiesel et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Johannes Kiesel Germany	11	572	456	379	80	62	36	821
Hannah Rashkin United States	11	1.0k 1.8×	475 1.0×	322 0.8×	70 0.9×	46 0.7×	16	1.3k
Júlio C. S. Reis Brazil	8	298 0.5×	460 1.0×	354 0.9×	131 1.6×	105 1.7×	47	638
Geraldine Wong Sak Hoi United Kingdom	3	352 0.6×	550 1.2×	248 0.7×	47 0.6×	92 1.5×	3	661
Nadia Conroy Canada	5	454 0.8×	902 2.0×	660 1.7×	231 2.9×	150 2.4×	8	1.0k
Noura Farra United States	12	1.2k 2.1×	119 0.3×	230 0.6×	45 0.6×	104 1.7×	15	1.3k
Parinaz Sobhani Canada	8	898 1.6×	284 0.6×	183 0.5×	26 0.3×	79 1.3×	10	1.1k
Sérgio Nunes Portugal	9	713 1.2×	109 0.2×	212 0.6×	88 1.1×	177 2.9×	40	849
David Vilares Spain	14	579 1.0×	195 0.4×	154 0.4×	23 0.3×	44 0.7×	39	713
Niall Conroy Canada	5	360 0.6×	627 1.4×	424 1.1×	133 1.7×	97 1.6×	7	731
Bhargavi Paranjape United States	9	284 0.5×	247 0.5×	155 0.4×	45 0.6×	44 0.7×	12	503

Countries citing papers authored by Johannes Kiesel

Since Specialization

Citations

This map shows the geographic impact of Johannes Kiesel'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 Johannes Kiesel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johannes Kiesel more than expected).

Fields of papers citing papers by Johannes Kiesel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Johannes Kiesel. 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 Johannes Kiesel. The network helps show where Johannes Kiesel may publish in the future.

Co-authorship network of co-authors of Johannes Kiesel

This figure shows the co-authorship network connecting the top 25 collaborators of Johannes Kiesel. A scholar is included among the top collaborators of Johannes Kiesel 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 Johannes Kiesel. Johannes Kiesel is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Hagen, Matthias, et al.. (2025). Segmentation of Argumentative Texts by Key Statements for Argument Mining from the Web. 228–242.

2.

Hagen, Matthias, et al.. (2025). Reproducing the Argument Quality Prediction of Project Debater. 181–188.

3.

Scells, Harrisen, Niklas Deckers, Janek Bevendorff, et al.. (2024). Evaluating Generative Ad Hoc Information Retrieval. 1916–1929. 7 indexed citations

4.

Fröbe, Maik, et al.. (2024). The Eighth Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI’24). University of Twente Research Information. 433–435. 1 indexed citations

5.

Fröbe, Maik, et al.. (2024). Report on the 8th Workshop on Search-Oriented Conversational Artificial Intelligence (SCAI 2024) at CHIIR 2024. ACM SIGIR Forum. 58(1). 1–12. 3 indexed citations

6.

Kiesel, Johannes, et al.. (2023). On Stance Detection in Image Retrieval for Argumentation. 2562–2571. 1 indexed citations

7.

Kiesel, Johannes, et al.. (2023). Unveiling the Power of Argument Arrangement in Online Persuasive Discussions. University of Groningen research database (University of Groningen / Centre for Information Technology). 15659–15671.

8.

Bevendorff, Janek, et al.. (2023). An Empirical Comparison of Web Content Extraction Algorithms. 2594–2603. 3 indexed citations

9.

Ajjour, Yamen, Johannes Kiesel, Benno Stein, & Martin Potthast. (2023). Topic Ontologies for Arguments. 1411–1427.

10.

Kiesel, Johannes, et al.. (2023). Guiding Oral Conversations: How to Nudge Users Towards Asking Questions?. 34–42. 3 indexed citations

11.

Kiesel, Johannes, et al.. (2023). SemEval-2023 Task 4: ValueEval: Identification of Human Values Behind Arguments. 2287–2303. 12 indexed citations

12.

Kiesel, Johannes, et al.. (2022). Identifying the Human Values behind Arguments. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 23 indexed citations

13.

Kiesel, Johannes, et al.. (2021). Meta-Information in Conversational Search. ACM Transactions on Information Systems. 39(4). 1–44. 7 indexed citations

14.

Kiesel, Johannes, Rishabh Shukla, Emmanuel Vincent, et al.. (2019). SemEval-2019 Task 4: Hyperpartisan News Detection. 829–839. 131 indexed citations

15.

Kiesel, Johannes, et al.. (2019). A Dataset for Content Error Detection in Web Archives. 349–350. 2 indexed citations

16.

Kiesel, Johannes, et al.. (2019). Clarifying False Memories in Voice-based Search. 331–335. 3 indexed citations

17.

Potthast, Martin, et al.. (2018). A Stylometric Inquiry into Hyperpartisan and Fake News. 231–240. 417 indexed citations breakdown →

18.

Hagen, Matthias, et al.. (2016). Webis at TREC 2016: Tasks, Total Recall, and Open Search Tracks.. Text REtrieval Conference. 1 indexed citations

19.

Al‐Khatib, Khalid, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen, & Benno Stein. (2016). A News Editorial Corpus for Mining Argumentation Strategies. Data Archiving and Networked Services (DANS). 3433–3443. 51 indexed citations

20.

Wachsmuth, Henning, Johannes Kiesel, & Benno Stein. (2015). Sentiment Flow - A General Model of Web Review Argumentation. 601–611. 18 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.

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