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 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
$\times $
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.