Modeling, Quantifying and Visualizing Media Bias on Twitter

News media garner a lot of attention regarding the subjectivity of their reporting. News media bias is of immense interest to various individuals, as the systematic preference of an entity can invoke its support and public actions. These inclinations, although apparent, hinder the true facts. The id...

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Autores principales:	Anam Zahid, Maham Nasir Khan, Ahmer Latif Khan, Faisal Kamiran, Bilal Nasir
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Bias analysis information retrieval media bias social media Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a6704601d0404d35b78d3cc4fd948a3a
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spelling	oai:doaj.org-article:a6704601d0404d35b78d3cc4fd948a3a2021-11-19T00:03:18ZModeling, Quantifying and Visualizing Media Bias on Twitter2169-353610.1109/ACCESS.2020.2990800https://doaj.org/article/a6704601d0404d35b78d3cc4fd948a3a2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9079528/https://doaj.org/toc/2169-3536News media garner a lot of attention regarding the subjectivity of their reporting. News media bias is of immense interest to various individuals, as the systematic preference of an entity can invoke its support and public actions. These inclinations, although apparent, hinder the true facts. The identification and quantification of media bias is one of the most important metrics in reference to bias assessment in media and general public. In this paper, we present a principled approach to quantify media bias along with insightful visualizations for popular media sources using their tweets. We use the concept of a mini-world of N <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> M matrix to model the sources and entities of interest, where the tweet counts and respective polarities over a specified time period are the values. Direct comparisons between these two are not as meaningful due to the neglection of inherent characteristics of sources and entities. Thus, we define coverage and statement scores as properly normalized measures of tweet counts and polarity rates. Furthermore, we present a statistically consistent model of neutral tweet counts and polarity rates, using which we define the absolute coverage and statement bias of each source-entity pair. We illustrate our approach on two data sets capturing tweets on 1) Prime minister candidates of top political parties of Pakistan in the 2018 general election 2) Paris and Beirut bombings in 2015 by different news sources. The results indicate that our model is generalizable i.e. it can be applied to different entities/sources and in consistent with previous studies.Anam ZahidMaham Nasir KhanAhmer Latif KhanFaisal KamiranBilal NasirIEEEarticleBias analysisinformation retrievalmedia biassocial mediaElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 81812-81821 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Bias analysis information retrieval media bias social media Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Bias analysis information retrieval media bias social media Electrical engineering. Electronics. Nuclear engineering TK1-9971 Anam Zahid Maham Nasir Khan Ahmer Latif Khan Faisal Kamiran Bilal Nasir Modeling, Quantifying and Visualizing Media Bias on Twitter
description	News media garner a lot of attention regarding the subjectivity of their reporting. News media bias is of immense interest to various individuals, as the systematic preference of an entity can invoke its support and public actions. These inclinations, although apparent, hinder the true facts. The identification and quantification of media bias is one of the most important metrics in reference to bias assessment in media and general public. In this paper, we present a principled approach to quantify media bias along with insightful visualizations for popular media sources using their tweets. We use the concept of a mini-world of N <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> M matrix to model the sources and entities of interest, where the tweet counts and respective polarities over a specified time period are the values. Direct comparisons between these two are not as meaningful due to the neglection of inherent characteristics of sources and entities. Thus, we define coverage and statement scores as properly normalized measures of tweet counts and polarity rates. Furthermore, we present a statistically consistent model of neutral tweet counts and polarity rates, using which we define the absolute coverage and statement bias of each source-entity pair. We illustrate our approach on two data sets capturing tweets on 1) Prime minister candidates of top political parties of Pakistan in the 2018 general election 2) Paris and Beirut bombings in 2015 by different news sources. The results indicate that our model is generalizable i.e. it can be applied to different entities/sources and in consistent with previous studies.
format	article
author	Anam Zahid Maham Nasir Khan Ahmer Latif Khan Faisal Kamiran Bilal Nasir
author_facet	Anam Zahid Maham Nasir Khan Ahmer Latif Khan Faisal Kamiran Bilal Nasir
author_sort	Anam Zahid
title	Modeling, Quantifying and Visualizing Media Bias on Twitter
title_short	Modeling, Quantifying and Visualizing Media Bias on Twitter
title_full	Modeling, Quantifying and Visualizing Media Bias on Twitter
title_fullStr	Modeling, Quantifying and Visualizing Media Bias on Twitter
title_full_unstemmed	Modeling, Quantifying and Visualizing Media Bias on Twitter
title_sort	modeling, quantifying and visualizing media bias on twitter
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/a6704601d0404d35b78d3cc4fd948a3a
work_keys_str_mv	AT anamzahid modelingquantifyingandvisualizingmediabiasontwitter AT mahamnasirkhan modelingquantifyingandvisualizingmediabiasontwitter AT ahmerlatifkhan modelingquantifyingandvisualizingmediabiasontwitter AT faisalkamiran modelingquantifyingandvisualizingmediabiasontwitter AT bilalnasir modelingquantifyingandvisualizingmediabiasontwitter
_version_	1718420686501838848

Modeling, Quantifying and Visualizing Media Bias on Twitter

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