Maximilian Mozes

516 total citations
10 papers, 72 citations indexed

About

Maximilian Mozes is a scholar working on Artificial Intelligence, Communication and Sociology and Political Science. According to data from OpenAlex, Maximilian Mozes has authored 10 papers receiving a total of 72 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Communication and 3 papers in Sociology and Political Science. Recurrent topics in Maximilian Mozes's work include Topic Modeling (4 papers), Social Media and Politics (3 papers) and Hate Speech and Cyberbullying Detection (3 papers). Maximilian Mozes is often cited by papers focused on Topic Modeling (4 papers), Social Media and Politics (3 papers) and Hate Speech and Cyberbullying Detection (3 papers). Maximilian Mozes collaborates with scholars based in United Kingdom, Netherlands and Germany. Maximilian Mozes's co-authors include Bennett Kleinberg, Arnoud Arntz, Bruno Verschuère, Isabelle van der Vegt, Paul Gill, Max Bartolo, Pontus Stenetorp, Mykola Makhortykh, Lewis D. Griffin and Ann Yuan and has published in prestigious journals such as Journal of Forensic Sciences, Empirical Methods in Natural Language Processing and arXiv (Cornell University).

In The Last Decade

Maximilian Mozes

9 papers receiving 70 citations

Peers

Maximilian Mozes

AI

SPS

IS

SP

COMMU

Rui Sousa‐Silva Portugal

Zeerak Talat Canada

Gautam Kishore Shahi Germany

Marco Antonio Stranisci Italy

Silvia Montero Martínez Spain

Gregory James United States

Andreas van Cranenburgh Netherlands

Keita Kurita United States

Matthew S. Nurse Australia

Simon Forest France

Rui Sousa‐Silva Portugal

Maximilian Mozes

55 ×1.1

AI

39 ×1.2

SPS

15 ×0.8

IS

14 ×0.8

SP

13 ×1.1

COMMU

Citations per year, relative to Maximilian Mozes Maximilian Mozes (= 1×) peers Rui Sousa‐Silva

Countries citing papers authored by Maximilian Mozes

Since Specialization

Citations

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

Fields of papers citing papers by Maximilian Mozes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maximilian Mozes

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

All Works

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10 of 10 papers shown

1.

Griffin, Lewis D., et al.. (2023). Large Language Models respond to Influence like Humans. 15–24. 4 indexed citations

2.

Mozes, Maximilian, Jessica D. Hoffmann, Katrin Tomanek, et al.. (2023). Towards Agile Text Classifiers for Everyone. 400–414. 2 indexed citations

3.

Mozes, Maximilian, Bennett Kleinberg, & Lewis D. Griffin. (2022). Identifying Human Strategies for Generating Word-Level Adversarial Examples. 6118–6126.

4.

Mozes, Maximilian, Max Bartolo, Pontus Stenetorp, Bennett Kleinberg, & Lewis D. Griffin. (2021). Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 8258–8270. 4 indexed citations

5.

Vegt, Isabelle van der, Maximilian Mozes, Paul Gill, & Bennett Kleinberg. (2020). Online influence, offline violence: Language Use on YouTube surrounding the 'Unite the Right' rally. RePEc: Research Papers in Economics. 16 indexed citations

6.

Vegt, Isabelle van der, Maximilian Mozes, Paul Gill, & Bennett Kleinberg. (2019). Online influence, offline violence: Linguistic responses to the 'Unite the Right' rally. arXiv (Cornell University). 1 indexed citations

7.

Makhortykh, Mykola, et al.. (2019). Uphill from here: Sentiment patterns in videos from left- and right-wing. 84–93. 4 indexed citations

8.

Kleinberg, Bennett, Maximilian Mozes, & Isabelle van der Vegt. (2018). Identifying the narrative styles of YouTube's vloggers.. Empirical Methods in Natural Language Processing. 3581–3590. 1 indexed citations

9.

Kleinberg, Bennett, Maximilian Mozes, Arnoud Arntz, & Bruno Verschuère. (2017). Using Named Entities for Computer‐Automated Verbal Deception Detection. Journal of Forensic Sciences. 63(3). 714–723. 38 indexed citations

10.

Kleinberg, Bennett & Maximilian Mozes. (2017). Web-based text anonymization with Node.js: Introducing NETANOS (Named entity-based Text Anonymization for Open Science). The Journal of Open Source Software. 2(14). 293–293. 2 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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