A Political Approach to Fake News

It is essential for people with opposing ideas to talk to each other without getting into a fight. Understanding each other is the best way to develop empathy on both sides.

One might be tempted to trust social media companies and the content posted by users on these sites, but we recommend never letting a third party act as a proxy for your mind process. Use transformer models to deconstruct messages but remain proactive!

A consensus on the previous topic (NFS Rivals) could be to agree on playing video games that suit your preferences. For example, one can choose either to play NFS Rivals on an older gaming console or try a more compatible alternative to the video game for the same overall racing experience!

Let’s move on to COVID-19 and social media posts.

COVID-19 and social media posts

No matter your political opinion, there is so much being said on X (formerly known as Twitter) by politicians that it would take a book in itself to analyze all of the information! This is a technical, not a political course, so we will analyze the posts scientifically.

We described an educational approach to fake news in the NFS Rivals section in the previous lesson. We do not need to go through the whole process again.

In this lesson, we will focus on the logic of fake news. We will run the BERT model on SRL and visualize the results on AllenNLP’s website.

Now, let’s go through some political posts on COVID-19.

Semantic role labeling (SRL)

SRL is an excellent educational tool for all of us. We tend just to read posts passively and listen to what others say about them. Breaking messages down with SRL is a good way to develop social media analytical skills to distinguish fake from accurate information.

We recommend using SRL transformers for educational purposes in class. A young student can enter a post and analyze each verb and its arguments. It could help younger generations become active readers on social media.

We will first analyze a relatively undivided post and then a controversial post:

Let’s analyze the following post. We removed identifiable information and paraphrased some of the text: