Anke Stoll

About Anke Stoll

Anke Stoll, With an exceptional h-index of 5 and a recent h-index of 5 (since 2020), a distinguished researcher at Heinrich-Heine-Universität Düsseldorf, specializes in the field of Machine Learning, NLP, Online Communication, Computational Communication Science.

His recent articles reflect a diverse array of research interests and contributions to the field:

Technology acceptance and transparency demands for toxic language classification–interviews with moderators of public online discussion fora

AQuA--Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs

Machine Learning for the Automated Content Analysis of Incivility in Online Discussions

Gender-related differences in online comment sections: Findings from a large-scale content analysis of commenting behavior

The Accuracy Trap or How to Build a Phony Classifier

Developing an incivility dictionary for German online discussions–a semi-automated approach combining human and artificial knowledge

Overview of the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments

Detecting Impoliteness and Incivility in Online Discussions: Classification Approaches for German User Comments

Anke Stoll Information

University

Position

___

Citations(all)

160

Citations(since 2020)

159

Cited By

28

hIndex(all)

5

hIndex(since 2020)

5

i10Index(all)

3

i10Index(since 2020)

3

Email

University Profile Page

Google Scholar

Anke Stoll Skills & Research Interests

Machine Learning

NLP

Online Communication

Computational Communication Science

Top articles of Anke Stoll

Technology acceptance and transparency demands for toxic language classification–interviews with moderators of public online discussion fora

Human–Computer Interaction

2024/2/2

Anke Stoll
Anke Stoll

H-Index: 3

Marc Ziegele
Marc Ziegele

H-Index: 19

AQuA--Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs

arXiv preprint arXiv:2404.02761

2024/4/3

Machine Learning for the Automated Content Analysis of Incivility in Online Discussions

2024/2/14

Anke Stoll
Anke Stoll

H-Index: 3

Gender-related differences in online comment sections: Findings from a large-scale content analysis of commenting behavior

Social Science Computer Review

2023/6

Anke Stoll
Anke Stoll

H-Index: 3

Marc Ziegele
Marc Ziegele

H-Index: 19

The Accuracy Trap or How to Build a Phony Classifier

2023/4/21

Anke Stoll
Anke Stoll

H-Index: 3

Developing an incivility dictionary for German online discussions–a semi-automated approach combining human and artificial knowledge

Communication Methods and Measures

2023/4/3

Anke Stoll
Anke Stoll

H-Index: 3

Marc Ziegele
Marc Ziegele

H-Index: 19

Overview of the GermEval 2021 shared task on the identification of toxic, engaging, and fact-claiming comments

Proceedings of the GermEval

2021

Anke Stoll
Anke Stoll

H-Index: 3

Michael Wiegand
Michael Wiegand

H-Index: 12

Detecting Impoliteness and Incivility in Online Discussions: Classification Approaches for German User Comments

Computational Communication Research

2020/2/3

Supervised Machine Learning mit Nutzergenerierten Inhalten: Oversampling für nicht balancierte Trainingsdaten

Publizistik

2020

Anke Stoll
Anke Stoll

H-Index: 3

See List of Professors in Anke Stoll University(Heinrich-Heine-Universität Düsseldorf)

Co-Authors

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