Aroma Rodrigues

Aroma Rodrigues is a master's student at UMass Amherst. She believes that Automation is the path to Inclusion. In 2016, a teammate of her "Shoes for the Visually Impaired" project presented it at the FOSSASIA. She reads, writes and enjoys walking to explore places. She presently works in a financial services firm and believes that solving problems that she has would solve problems for a large chunk of the world. An ML enthusiast she has about 20+ Coursera Certifications with the respective project work to support her learning in that field. She presented a talk on “De-mystifying Terms and Conditions using NLP” at PyCon 2018 and a talk called “Propaganda Detection in Fake News using Natural Language Processing” at PyCon ZA 2019 in Johannesburg. She spoke on detecting gender roles based biases in school textbooks at PyOhio 2020.


Is the news media polarized? Or are we conditioned to think it is?
Aroma Rodrigues

Live stream:

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 ”media-bias” 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 ”spin”
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
Media categorization - if taken as simplistically as it is presented. These experiments can be extended to include entity based topical
studies in the future and to also educate the populace about their perceived biases.

Data Science, AI, and Machine Learning