This project aims at predicting the virality of tweets and hashtags on Twitter based on a regression model and a classifier trained by a machine learning algorithms.

• Python
• MongoDB

The data used is composed of random English tweets crawled from the Twitter API during 3 days.

• Install MongoDB: sudo apt-get install mongodb
• Install the Python libs: sudo pip install -r requirements.txt If you get trouble installing the module h5py, try installing the corresponding package manually first, for example with sudo apt-get install libhdf5-dev on Ubuntu.

1. Fill your Twitter API key in authExample.py and rename the file auth.py
2. Execute python stream.py to get as many tweets as you want (at least 3 days if you can)
3. Remove the duplicates and add an index on the tweets ID
   • Execute the shell command mongo
   • In the mongo shell, execute db.Tweets.createIndex({id: 1}, {unique: true, dropDups: true})
4. Execute python retweetUpdater.py a few days later to update the number of retweets of each tweet. This timeframe will be the one of your model when predicting the virality of a tweet or hashtag
5. Predict the virality of previously retrieved hashtags: python viralityPrediction.py. This step can be replaced by the following ones:
   • Extract the features from the tweets stored in database: python featuresExtractor.py
   • Train your regression model or classifier to predict the number of retweets or the tweet virality class based on the features: python regression.py
   • Build an inverted index, giving a list of tweets for each hashtag: python hashtagIndex.py
   • Predict the virality of hashtags: python viralityPrediction.py
6. Predict the virality of new hashtags using current tweets: use python twitterSearch.py to retrieve tweets IDs from the Twitter API for a given hashtag. When you have retrieved the features for each tweet, you can feed those features to viralityPrediction.py in order to output a retweet count or virality prediction