1. PSA: Never work with Rate My Professor
Use Koofers instead.

2. Application to Find Like-Minded Twitter Users
Austin herrera

3. Overview Background Related Works Methodology Review
Questions and Comments

4. Background

5. Friend Recommendation
There are many complex algorithms already.
Collaborative Filtering
Past Behaviors and preferences
Content-based Filtering
Characteristics of people you like
Why mine?

6. Why Like-Minded Users? Who should be using this?
Create a support group
Questions
Advice
Meaningful Conversations
Enthusiasm
“To give everyone the power to create a support system using social media as a tool.”

7. Emma Lindström
“This is my artwork. Although, I would not call it work. This is what I want to do, what I have to do in life.”

9. Related Works

11. Create an account Customize a Profile Set policies Bio Location
Preferred categories
Connect to other social Media pages
Set policies
Who can follow you
Which users will find you

12. How does Twellow work? They categorize users
Data entered on their Twitter Bio
Analysis of Tweets and keywords
Twellow Profile Details

13. TweetStork Tool that finds users who want to… Three Search Techniques
Read
Share
Three Search Techniques
Find Related Users
Find List Owners
Find Re-Tweeters
Unfollow Users

14. How does TweetStork Find Related Users?
They find a popular person within your search.
Look Through that user’s followers list
Filters the followers based on the your preferences

15. Find List Owners Finds popular accounts similar to yours
Finds if they under lists of other users
Shows you those users

16. Find Retweeters Finds popular users that are similar to you
Looks at people who retweets them
Shows you those users

17. Unfollow Users TweetStock looks through the your 'following' list
It lists the users who aren't following you back and allows you to unfollow them.

18. Methodology

19. Methodology Likes and Retweets Key Word Analysis – Tweets
Key Word Analysis - Bios
Find out who Favorites and Retweets the same things as you.
Look through people’s Tweets
Look through people’s Profile Bios

20. Likes and Retweets Take User’s liked tweets
Take User’s retweeted tweets
Add more weight to retweeted tweets, because on average most tweets are favorited 5x more than retweeted.
Issues
“Tom” who likes and retweets everything he sees
New Users

21. Finding Keywords Go through tweets over the last year
Create a Matrix with the words
Column Word
Row is Tweet Message
Normalize values within Matrix
Divide number of keywords by total words

22. Normalization
X ij is the occurrence of the word j in the user’s tweet i.
We normalize the value in order to normalize the matrix I create

23. Review

24. Background. Why. Who Related Works. Twellow. TweetStork Methodology
Background Why Who Related Works Twellow TweetStork Methodology Favorites and RTs Keywords

25. Questions or Comments
Are you guys aware of what problem I want to solve, and why it is important?
Do you understand how I plan to solve the problem?
How can I better improve my results better than others?
Can you offer me tips or ideas for the application?

26. References
Bushell, Annie. Creating Your Successful Future. 2010, eBookwholesaler. 
Soufiene Jaffali, Salma Jamoussi, Abdelmajid Ben Hamadou. “Clustering and Classification of Like-Minded People from Their Tweets”, University of Sfax, Tunisia
Twellow Website. 
TweetStork Website.