Investigating bias in traditional media through social media

Younus, Arjumand; Qureshi, Muhammad Atif; Kingrani, Suneel Kumar; Saeed, Muhammad; Touheed, Nasir; O’Riordan, Colm; Pasi, Gabriella

doi:10.1145/2187980.2188168

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…Despite having high popularity among its viewership, mainstream media often fails to meet the standards of journalism ethics, and inadvertently patronises certain ideas [5]. Furthermore, news sources are also known to inject political bias while reporting [9], [17], [18], potentially affecting the political beliefs of the audience, which might result in altering voting behaviour [18]. Recently it was shown that Fox News had been misrepresenting facts in an effort to appeal to conservative viewers [6].…”

Section: Related Workmentioning

confidence: 99%

Exploring Media Bias and Toxicity in South Asian Political Discourse

Qayyum

Gilani

Latif

et al. 2018

2018 12th International Conference on Open Source Systems and Technologies (ICOSST)

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Media outlets and political campaigners recognise social media as a means for widely disseminating news and opinions. In particular, Twitter is used by political groups all over the world to spread political messages, engage their supporters, drive election campaigns, and challenge their critics. Further, news agencies, many of which aim to give an impression of balance, are often of a particular political persuasion which is reflected in the content they produce. Driven by the potential for political and media organisations to influence public opinion, our aim is to quantify the nature of political discourse by these organisations through their use of social media. In this study, we analyse the sentiments, toxicity, and bias exhibited by the most prominent Pakistani and Indian political parties and media houses, and the pattern by which these political parties utilise Twitter. We found that media bias and toxicity exist in the political discourse of these two developing nations.

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Section: Related Workmentioning

confidence: 99%

Exploring Media Bias and Toxicity in South Asian Political Discourse

Qayyum

Gilani

Latif

et al. 2018

2018 12th International Conference on Open Source Systems and Technologies (ICOSST)

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“…Diehl et al, (2013) suggested bias between social media and online data and explored how interaction between journalists and people on Twitter affects perceptions of bias. Younus et al (2012) researched how social media can identify bias in traditional media, introducing the concept of citizen journalism and creating a model using latent Dirichlet allocation and Jaccard similarity to measure bias. Thomsen (2018) used sentiment analysis to study media bias in newspaper tweets and discovered that while some media companies showed bias, most did not.…”

Section: Biasmentioning

confidence: 99%

Uncovering Media Bias in Eviction Reporting: A Comprehensive Analysis Utilising Sentiment Analysis Framework and Social Media Data

Koena,

Primus,

Mahlobo

2024

JDHASA

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This study investigates the prevalence of evictions in South Africa and examines potential disparities between traditional media reporting and social media discourse. Employing a sentiment analysis framework, we extend its application to compare the reporting of evictions in newspaper articles (i.e. conventional media) and Twitter data (i.e. social media). Statistical machine-learning methods are utilized to predict sentiment scores for both types of content, and a chi-square test is employed to evaluate bias between news articles and tweets. The test results reveal a significant bias in the sentiment distribution, suggesting that the dissimilarities observed between articles and tweets are not merely coincidental.

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“…Morgan et al [43] found that Twitter users share news in similar ways regardless of outlet or perceived outlet ideology, and that as users share more news content, they tend to quickly include outlets with opposing views. Younes et al [62] looked at how traditional media outlets and social media differ in coverage of an event, and focused on coverage patterns of NYTimes articles and tweets during the Egyptian uprising in 2011. To discover such patterns, they proposed a simple media bias measurement model for day-to-day news items built on top of topic models.…”

Section: Twitter For Crisis and Controversy Understandingmentioning

confidence: 99%

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

Ali

Stratmann

Park

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.

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Investigating bias in traditional media through social media

Cited by 4 publications

References 3 publications

Exploring Media Bias and Toxicity in South Asian Political Discourse

Exploring Media Bias and Toxicity in South Asian Political Discourse

Uncovering Media Bias in Eviction Reporting: A Comprehensive Analysis Utilising Sentiment Analysis Framework and Social Media Data

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

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