Timo Spinde

Orcid: 0000-0003-3471-4127

Affiliations:
  • University of Konstanz, Department of Computer and Information Science, Germany


According to our database1, Timo Spinde authored at least 24 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
News Ninja: Gamified Annotation of Linguistic Bias in Online News.
Proc. ACM Hum. Comput. Interact., 2024

NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback.
CoRR, 2024

MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions.
CoRR, 2024

MAGPIE: Multi-Task Analysis of Media-Bias Generalization with Pre-Trained Identification of Expressions.
Proceedings of the 2024 Joint International Conference on Computational Linguistics, 2024

2023
What do Twitter comments tell about news article bias? Assessing the impact of news article bias on its perception on Twitter.
Online Soc. Networks Media, September, 2023

The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias.
CoRR, 2023

Introducing MBIB - The First Media Bias Identification Benchmark Task and Dataset Collection.
Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023

A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents.
Proceedings of the Information for a Better World: Normality, Virtuality, Physicality, Inclusivity, 2023

2022
A domain-adaptive pre-training approach for language bias detection in news.
Proceedings of the JCDL '22: The ACM/IEEE Joint Conference on Digital Libraries in 2022, Cologne, Germany, June 20, 2022

Exploiting Transformer-Based Multitask Learning for the Detection of Media Bias in News Articles.
Proceedings of the Information for a Better World: Shaping the Global Future - 17th International Conference, iConference 2022, Virtual Event, February 28, 2022

2021
Automated identification of bias inducing words in news articles using linguistic and context-oriented features.
Inf. Process. Manag., 2021

How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption?
CoRR, 2021

MBIC - A Media Bias Annotation Dataset Including Annotator Characteristics.
CoRR, 2021

How to Effectively Identify and Communicate Person-Targeting Media Bias in Daily News Consumption? (short paper).
Proceedings of the 9th International Workshop on News Recommendation and Analytics (INRA 2021) co-located with 15th ACM Conference on Recommender Systems (RecSys 2021), 2021

TASSY - A Text Annotation Survey System.
Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, 2021

Towards A Reliable Ground-Truth For Biased Language Detection.
Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, 2021

Do You Think It's Biased? How To Ask For The Perception Of Media Bias.
Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, 2021

Omission of Information: Identifying Political Slant via an Analysis of Co-occurring Entities.
Proceedings of the Information between Data and Knowledge: Information Science and its Neighbors from Data Science to Digital Humanities, 2021

An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles.
Proceedings of the 2021 International Conference on Data Mining, 2021

Identification of Biased Terms in News Articles by Comparison of Outlet-Specific Word Embeddings.
Proceedings of the Diversity, Divergence, Dialogue, 2021

Neural Media Bias Detection Using Distant Supervision With BABE - Bias Annotations By Experts.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

2020
Media Bias in German News Articles: A Combined Approach.
Proceedings of the ECML PKDD 2020 Workshops, 2020

An Integrated Approach to Detect Media Bias in German News Articles.
Proceedings of the JCDL '20: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, 2020

Enabling News Consumers to View and Understand Biased News Coverage: A Study on the Perception and Visualization of Media Bias.
Proceedings of the JCDL '20: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020, 2020


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