A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it

Rodrigo-Ginés, Francisco-Javier; Carrillo-de-Albornoz, Jorge; Plaza, Laura

doi:10.1016/j.eswa.2023.121641

Cited by 10 publications

(3 citation statements)

References 61 publications

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“… 1 Whether through headlines, articles, or broadcasted narratives, the deliberate selection of words by news outlets can evoke specific emotions, highlight particular aspects of a story, and even shape the overall narrative direction, which then shapes audience opinions, attitudes, and perception of the reported information. 50 , 51 On the subject of GMOs, the Ghanaian media frames the subject as a food security issue.…”

Section: Discussionmentioning

confidence: 99%

The evolution of media reportage on GMOs in Ghana following approval of first GM crop

Gakpo,

Baffour - Awuah

2024

GM Crops & Food

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Section: Discussionmentioning

confidence: 99%

The evolution of media reportage on GMOs in Ghana following approval of first GM crop

Gakpo,

Baffour - Awuah

2024

GM Crops & Food

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“…However, it is crucial to note that media biases exist. News outlets play a significant role in shaping public opinion by setting the information agenda and emphasizing certain topics (Mutz, 1997;Rodrigo-Ginés et al, 2024). They adhere to specific writing protocols, such as the inverted pyramid structure, to convey information and influence public perception (Grunwald, 2005).…”

Section: Introductionmentioning

confidence: 99%

Corpus-assisted CDA of George Floyd’s Murder Reports on CNN and Aljazeera

Putri,

Mardiah

2024

Arbitrer

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In 2020, one news that shook the world was the murder of an Afro-American named George Floyd by Minneapolis police officers. This study focuses on the media coverage of George Floyd’s murder case by two news outlets, CNN and Aljazeera. We used a Corpus-assisted Critical Discourse Analysis (CACDA) technique to compare how each news outlet portrayed the social actors involved in the incident and to identify if any social actors were excluded from the reporting, eventually revealing which side each media was inclined to. The analysis was based on Theo Van Leeuwen’s theory and utilized the Antconc application. The data used in this study consists of ten news articles, five from CNN and Aljazeera, published between May and July 2020. A literature study was conducted to collect the data. The Antconc corpus-processing application was then used to process the large amount of data collected, enabling quick, extensive, and comprehensive analysis. We uploaded all ten articles to the Antconc application and used the frequency feature to identify the five most used words in the ten news articles. The top five words were further analyzed using van Leeuwen’s critical discourse analysis, excluding irrelevant particles. The result shows that CNN aligned with the police and Aljazeera aligned with Floyd. By the ideological stances of CNN and Aljazeera, readers can become more aware of potential biases in news articles concerning George Floyd’s murder.

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“…The literature contains a variety of survey papers (Hamborg et al, 2019;Medeiros and Braga, 2020;Kapantai et al, 2021;Rodrigo-Ginés et al, 2024) on the detection of fake news and related tasks. Nonetheless, any kind of survey has never been written about hyperpartisan detection.…”

Section: Introductionmentioning

confidence: 99%

A systematic review of Hyperpartisan News detection: A Comprehensive Framework for Definition, Detection, and Evaluation

Maggini,

Bassi,

Otero

et al. 2024

Preprint

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Hyperpartisan news detection employs advanced technological methods to identify news articles characterized by extreme bias, reporting information in a highly polarized manner. The spread of such news could lead to detrimental effects in terms of social cohesion and facts-perception. This systematic review delves into the uncharted territory of hyperpartisan news detection, where a conspicuous absence of comprehensive studies has left a void in scholarly discourse. Following the PRISMA methodology, we selected 80 pertinent articles. The paper commences by addressing the inherent challenges arising from disparate and ambiguous definitions of hyperpartisan found in existing literature. Through a meticulous analysis, we propose a refined and comprehensive definition of hyperpartisan news, serving as a foundational framework for subsequent exploration. The review systematically evaluates Non-Deep Learning, Deep Learning (i.e. non-Transformer and Transformer-based methods), and mixed approaches employed in the identification of hyperpartisan content across various studies. By scrutinizing methodologies, algorithms, and key features, we categorize and compare their performances, shedding light on their efficacy and limitations. Furthermore, we conducted an examination of 36 datasets utilized in diverse studies, offering insights into the commonalities and variations in data sources. This inclusive dataset analysis contributes to a nuanced understanding of the challenges faced in the development and validation of hyperpartisan news detection models. This paper not only pioneers the systematic review of hyperpartisan news detection, but also establishes a robust foundation for future research endeavors in this critical domain. We emphasize the necessity of standardized definitions, rigorous evaluation metrics, and shared datasets to advance the field.

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A systematic review on media bias detection: What is media bias, how it is expressed, and how to detect it

Cited by 10 publications

References 61 publications

The evolution of media reportage on GMOs in Ghana following approval of first GM crop

The evolution of media reportage on GMOs in Ghana following approval of first GM crop

Corpus-assisted CDA of George Floyd’s Murder Reports on CNN and Aljazeera

A systematic review of Hyperpartisan News detection: A Comprehensive Framework for Definition, Detection, and Evaluation

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