Is the news media polarized? Or are we being conditioned to think it is?
- Room:
- Liffey Hall 2
- Start (Dublin time):
- Start (your time):
- Duration:
- 30 minutes
Abstract
In this talk, we aim to find if polarization is induced in a neural network by feeding it newspaper articles with manufactured sentiments according to the Allsides Media Bias chart for the level of faith people on various aisles of the political spectrum. This project consists of a set of experiments on similar data-sets from news agencies across the various subsets in the āmedia-biasā chart. News Media perceived bias is common across consumers that belong to various political affiliations. While anecdotal evidence of this exists and there exist annotated datasets that aim to annotate the āspinā a news agency puts on certain events and entities, whether this is a widespread problem and whether it can be detected by the neural network topically or temporally is a problem that needs to be explored. The news media bias analysis is modelled as a Natural Language Processing sentiment analysis task and a fake news binary classification task to deduce the level of polarization in a neural network by feeding it headlines embedded using pre-trained sentiment models from news publications across the political spectrum. When it came to fake news vulnerability, news from all kinds of perceived politically affiliated news media holds up well against a fake news dataset with a very good accuracy. None of the accuracies dropped below 95%. This is a significant result that sort of debunks the AllSlides categorization
TalkPyData: Deep Learning, NLP, CV
Description
This work is an example of an intersection of a non scientific field with computer science and mathematics, trying to quantify, measure and identify non mathematical phenomena in the language of mathematics. It is important because it could be the basis of the scientific approaches that the next generation policy makers, voters, non profit social organizations and governments could use to make life changing decisions for their citizens. 2 The questions that this study tries to answer is whether a neural network can learn biases from the news media based on perceived bias scores obtained from independent agencies. It also seeks to answer whether any of these political leanings of the news media affect the vulnerability of their consumer when it comes to fake news. The results of this experiment aim to show
Conclusions
- SVMs perform better clustering with respect to the categories than neural networks, however the maximum does not cross 67%
- The most significant conclusion from this work is that though there is a perceived bias when it comes to news agencies, when looked at from a neural networks standpoint, it is negligible. Mainstream news agencies are not able to polarize a neural network with inherent biases in their headlines.
- There may be topical biases that need to be examined by using an Entity linking and bias calculation approach
- Most mainstream news agencies do not make the consumer vulnerable to believing fake news. This study needs to be conducted with data from popular social media ānewsā groups or popular TV shows that masquerade as news but may technically not even be news channels.
- It is safe to conclude that the perceived bias that stems from social media polarization is being extended to news media when their contribution to the polarization may be negligible.