Search Results for author: Timo Spinde

Found 14 papers, 5 papers with code

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

1 code implementation26 Dec 2023 Timo Spinde, Smi Hinterreiter, Fabian Haak, Terry Ruas, Helge Giese, Norman Meuschke, Bela Gipp

However, we have identified a lack of interdisciplinarity in existing projects, and a need for more awareness of the various types of media bias to support methodologically thorough performance evaluations of media bias detection systems.

Bias Detection

Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection

1 code implementation25 Apr 2023 Martin Wessel, Tomáš Horych, Terry Ruas, Akiko Aizawa, Bela Gipp, Timo Spinde

A unified benchmark encourages the development of more robust systems and shifts the current paradigm in media bias detection evaluation towards solutions that tackle not one but multiple media bias types simultaneously.

Bias Detection

A Benchmark of PDF Information Extraction Tools using a Multi-Task and Multi-Domain Evaluation Framework for Academic Documents

no code implementations17 Mar 2023 Norman Meuschke, Apurva Jagdale, Timo Spinde, Jelena Mitrović, Bela Gipp

Using the new framework, we benchmark ten freely available tools in extracting document metadata, bibliographic references, tables, and other content elements from academic PDF documents.

Retrieval Table Extraction

Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts

1 code implementation29 Sep 2022 Timo Spinde, Manuel Plank, Jan-David Krieger, Terry Ruas, Bela Gipp, Akiko Aizawa

Fine-tuning and evaluating the model on our proposed supervised data set, we achieve a macro F1-score of 0. 804, outperforming existing methods.

Bias Detection Sentence

A Domain-adaptive Pre-training Approach for Language Bias Detection in News

1 code implementation22 May 2022 Jan-David Krieger, Timo Spinde, Terry Ruas, Juhi Kulshrestha, Bela Gipp

We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0. 814.

Bias Detection Decision Making +1

An Interdisciplinary Approach for the Automated Detection and Visualization of Media Bias in News Articles

no code implementations26 Dec 2021 Timo Spinde

My vision is to devise a system that helps news readers become aware of media coverage differences caused by bias.

Bias Detection

Towards A Reliable Ground-Truth For Biased Language Detection

no code implementations14 Dec 2021 Timo Spinde, David Krieger, Manuel Plank, Bela Gipp

Our results demonstrate the existing crowdsourcing approaches' lack of data quality, underlining the need for a trained expert framework to gather a more reliable dataset.

Bias Detection

Identification of Biased Terms in News Articles by Comparison of Outlet-specific Word Embeddings

no code implementations14 Dec 2021 Timo Spinde, Lada Rudnitckaia, Felix Hamborg, Bela Gipp

The underlying idea is that the context of biased words in different news outlets varies more strongly than the one of non-biased words, since the perception of a word as being biased differs depending on its context.

Word Embeddings

Do You Think It's Biased? How To Ask For The Perception Of Media Bias

no code implementations14 Dec 2021 Timo Spinde, Christina Kreuter, Wolfgang Gaissmaier, Felix Hamborg, Bela Gipp, Helge Giese

To name an example: Intending to measure bias in a news article, should we ask, "How biased is the article?"

TASSY -- A Text Annotation Survey System

no code implementations14 Dec 2021 Timo Spinde, Kanishka Sinha, Norman Meuschke, Bela Gipp

We present a free and open-source tool for creating web-based surveys that include text annotation tasks.

text annotation

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

no code implementations18 Oct 2021 Felix Hamborg, Timo Spinde, Kim Heinser, Karsten Donnay, Bela Gipp

We present an in-progress system for news recommendation that is the first to automate the manual procedure of content analysis to reveal person-targeting biases in news articles reporting on policy issues.

News Recommendation

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