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DevFest 2024

PARser

Track: Politics

Nicholas Assaderaghi | Brianna Barkema | Maximo Librandi | Rattandeep Singh

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Table of

Contents

Addressed Political Issues

PARser

Current Solutions

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What Issues it Addresses

Misinformation: continued spread of inaccurate information

Political Polarization: divide between political parties due to largely different views

Bias: articles tend to be biased towards one side of the political spectrum

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Current

Several digital NLP-based solutions try to detect bias in news articles [1, 2]; however, known barriers include:

lack of political knowledge
difficult to access for the general public

too many steps to configure
too much data to manually enter
too much effort

Solutions

That’s where PARser comes in

1. Nadeem, M. and Raza, S. Detecting Bias in News Articles using NLP Models. Stanford University. Accessed February 4, 2024. https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1224/reports/custom_116661041.pdf

2. Najkov, D. Detecting political bias in online articles using NLP and classification models. Published July 19, 2022. Accessed February 4, 2024. https://medium.com/@danilo.najkov/detecting-political-bias-in-online-articles-using-nlp-and-classification-models-c1a40ec3989b

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Introducing

Clearer Perspectives, Cleaner News:

PARser Unveils the Truth!

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How it works

Highlight the article that you’re reading and click on the Chrome extension icon.

View bias and misinformation results!

LLM Analysis