An Information Nutritional Label for Online Documents

Fuhr, Norbert; Giachanou, Anastasia; Grefenstette, Gregory; Gurevych, Iryna; Hanselowski, Andreas; Järvelin, Kalervo; Jones, Rosie; Liu, YiquN; Mothe, Josiane; Nejdl, Wolfgang; Peters, Isabella; Stein, Benno

doi:10.1145/3190580.3190588

Cited by 45 publications

(55 citation statements)

References 16 publications

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“…Recently, a Dagstuhl workshop on fake news proposed a number of axes of quantitative computer analysis, such as factuality, readability, and virality, could help users to make more informed judgments about the news items they read [184].…”

Section: Spin: the Vagueness Of Media Biasmentioning

confidence: 99%

Automated identification of media bias in news articles: an interdisciplinary literature review

2018

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Media bias, i.e., slanted news coverage, can strongly impact the public perception of the reported topics. In the social sciences, research over the past decades has developed comprehensive models to describe media bias and effective, yet often manual and thus cumbersome, methods for analysis. In contrast, in computer science fast, automated, and scalable methods are available, but few approaches systematically analyze media bias. The models used to analyze media bias in computer science tend to be simpler compared to models established in the social sciences, and do not necessarily address the most pressing substantial questions, despite technically superior approaches. Computer science research on media bias thus stands to profit from a closer integration of models for the study of media bias developed in the social sciences with automated methods from computer science. This article first establishes a shared conceptual understanding by mapping the state of the art from the social sciences to a framework, which can be targeted by approaches from computer science. Next, we investigate different forms of media bias and review how each form is analyzed in the social sciences. For each form, we then discuss methods from computer science suitable to (semi-)automate the corresponding analysis. Our review suggests that suitable, automated methods from computer science, primarily in the realm of natural language processing, are already available for each of the discussed forms of media bias, opening multiple directions for promising further research in computer science in this area.

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Section: Spin: the Vagueness Of Media Biasmentioning

confidence: 99%

Automated identification of media bias in news articles: an interdisciplinary literature review

2018

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“…news articles, blogs). Sentiment label can be considered as a feature in predicting relevance [Fuhr et al 2018].…”

Section: Decision-level Fusionmentioning

confidence: 99%

A Survey of Quantum Theory Inspired Approaches to Information Retrieval

2020

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Since 2004, researchers have been using the mathematical framework of Quantum Theory (QT) in Information Retrieval (IR). QT offers a generalized probability and logic framework. Such a framework has been shown capable of unifying the representation, ranking and user cognitive aspects of IR, and helpful in developing more dynamic, adaptive and context-aware IR systems. Although Quantum-inspired IR is still a growing area, a wide array of work in different aspects of IR has been done and produced promising results. This paper presents a survey of the research done in this area, aiming to show the landscape of the field and draw a road-map of future directions.

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“…Our approach to the hyperpartisan news task leverages lessons learned in prior work on fake news detection, and explores the extent to which that work is successful in a different but related task. Fake news detection has been widely studied (e.g., the survey paper by Fuhr et al (Fuhr et al, 2018)), and we base many of our classifier's features on previous studies of fake news.…”

Section: Previous Workmentioning

confidence: 99%

Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector

Amason¹,

Palanker²,

Shen³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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We use the 600 hand-labelled articles from Se-mEval Task 4 (Kiesel et al., 2019) to handtune a classifier with 3000 features for the Hyperpartisan News Detection task. Our final system uses features based on bag-of-words (BoW), analysis of the article title, language complexity, and simple sentiment analysis in a naive Bayes classifier. We trained our final system on the 600,000 articles labelled by publisher. Our final system has an accuracy of 0.653 on the hand-labeled test set. The most effective features are the Automated Readability Index and the presence of certain words in the title. This suggests that hyperpartisan writing uses a distinct writing style, especially in the title.

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An Information Nutritional Label for Online Documents

Cited by 45 publications

References 16 publications

Automated identification of media bias in news articles: an interdisciplinary literature review

Automated identification of media bias in news articles: an interdisciplinary literature review

A Survey of Quantum Theory Inspired Approaches to Information Retrieval

Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector

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