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A Democratic World Wide Web: An Introduction and Implementation of the Plebeian Algorithm to Freely Combat Misinformation

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Benjamin Fedoruk
STEM Fellowship, Program Management Team Lead of Data Science Committee

Workshop Overview:

The Internet is filled with a vast array of information -- some is true, some is false. But, is there a programmatic method to effectively determine the verity of information, without the need for human intervention? This is where the Plebeian Algorithm shines, which you will learn about in this workshop. You will learn how to implement a Plebeian-esque algorithm into your personal projects.

Intended Audience:

This workshop is for you if: 

- you have an introductory level of data science skills in Python.

 - you are interested in natural language processing. 

- you want to learn applications of data science skills to real-world problems.

 - you want to find solutions to the spread of misinformation. 

- you are intrigued by problem solving approaches using data science. 

Topics covered in this workshop include:

 - Plebeian algorithms 

- democratic moderation of content 

- natural language processing, applied to a real-world issue 

- expansion on the basics of Python data science. 

Workshop Takeaways  

You will learn: 

- what a Plebeian Algorithm is. 

- how to implement a Plebeian

-esque algorithm into various projects. 

- how to analyze text in various ways using natural language processing (NLP). 

- the relationship between sentiment and verity. 

Participants should be able to access a Python environment. This can be tested by running "python3 --version" in a terminal (version should be >= 3.8). A participant should also have nltk, matplotlib, pandas, and numpy installed. Although it is not required, I will be using Jupyter Notebook; it may be optimal for participants to effectively follow along, to use Jupyter Notebook as well. However, all code works in any development environment (i.e. VSCode, Atom, IDLE, PyCharm, etc.)