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#WhatTheHash:
Twitter Hashtag Recommender
Emma Ideal
How can I find actively trending hashtags that are relevant for my tweet?
DATA MODEL
Train word2vec model
50 million recent English tweets
from the US/Canada
PCA
Warriors vs the Cavs!
vector mean
Warriors vs the Cavs!
Extract most recent 8 hours
of tweets with hashtags
Warriors vs
the Cavs!
User #nba #cavs #ilovecake .....
Extract most recent 8 hours
of tweets with hashtags
User #nba #cavs #ilovecake .....
Extract most recent 8 hours
of tweets with hashtags
similarity score
= cosine
similarity
0.865 0.795 0.144
50% of the time, the actual hashtag used is in
my top 10 recommendations
Emma Ideal
Hashtag Popularity
For each hashtag:
1) popularity = the number of tweets the hashtag has
appeared in
2) Take the log10 of the popularity
3) Squash range to [0.8, 1]
For the relative popularity displayed on webpage:
1) popularity = the number of tweets the hashtag has
appeared in
2)  Take the log10 of the popularity
3)  Normalize to [0, 1]
4)  Slice it so that:
1 star: < ~10 tweets
2 stars: < ~100 tweets
3 stars: < ~500 tweets
4 stars: < ~3000 tweets
5 stars: > ~3000 tweets
cosine similarity
x	
# user’s search words in tweets for hashtag
# words in user’s search
x	
Hashtag score =
hashtag popularity
Cavs
vs
User
#cavsWarriors
basketball
play

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What The Hash

  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7. How can I find actively trending hashtags that are relevant for my tweet?
  • 8. DATA MODEL Train word2vec model 50 million recent English tweets from the US/Canada PCA
  • 11. Extract most recent 8 hours of tweets with hashtags
  • 12. Warriors vs the Cavs! User #nba #cavs #ilovecake ..... Extract most recent 8 hours of tweets with hashtags
  • 13. User #nba #cavs #ilovecake ..... Extract most recent 8 hours of tweets with hashtags similarity score = cosine similarity 0.865 0.795 0.144
  • 14.
  • 15.
  • 16. 50% of the time, the actual hashtag used is in my top 10 recommendations
  • 18.
  • 19.
  • 20. Hashtag Popularity For each hashtag: 1) popularity = the number of tweets the hashtag has appeared in 2) Take the log10 of the popularity 3) Squash range to [0.8, 1] For the relative popularity displayed on webpage: 1) popularity = the number of tweets the hashtag has appeared in 2)  Take the log10 of the popularity 3)  Normalize to [0, 1] 4)  Slice it so that: 1 star: < ~10 tweets 2 stars: < ~100 tweets 3 stars: < ~500 tweets 4 stars: < ~3000 tweets 5 stars: > ~3000 tweets
  • 21. cosine similarity x # user’s search words in tweets for hashtag # words in user’s search x Hashtag score = hashtag popularity Cavs vs User #cavsWarriors basketball play