This document discusses the automated identification of discussion topics within Twitter communities using machine learning and semantic feature extraction. It details the analysis of over four million tweets from Trump supporters and three million tweets related to Brexit, demonstrating how ideas and conversations can be visualized and tracked over time. The approach allows for easy visualization of community topics and their evolution.

1. Social Media Meets Artificial Intelligence
Automated Identification Of Discussion Topics In Twitter Communities

2. How to extract valuable insights from a
noisy medium such as Twitter?

3. Our approach
Gather tons of tweets
(4M+ should do it)
Extract semantic features Use machine learning to analyze
the data
Using state-of-the-art neural models to tackle the problem

4. Trump

5. We gathered four million tweets from hard-core
Trump supporters

6. Then, we trained a shallow neural network to map the tweet’s
content into a semantic space

7. Mexico is ‘bringing drugs, crime and rapists’ to the U.S.

8. Trump folks think of foreigners as invaders…

9. …invaders scare the hell out of them

10. Mexicans are taking our jobs and killing us

11. How to automatically identify topics in
Twitter?

12. Brexit

13. Our new approach

14. We gathered three million tweets using the
#Brexit hashtag

16. Hate speech Campaigns Economy

17. Take-away points
1. We can visualize what ideas a Twitter community associate
to a specific term in an easy to read map or tree.
2. We can identify what the main topics are inside the
community’s conversation.
3. We can automatically track he evolution of those topics.