I follow too many people on twitter. It makes my stream a bit of a mismatch of things and I often end up missing tweets from accounts I’m really interested in. I’m not too sure how to solve this problem but I am sure that it must start with my understanding my network. In this post I’ll show a simple python script that grabs my twitter network (disclaimer it takes time) and then some simple graph theoretic analysis of my network.

Getting the network

To get my network I use tweepy , a Python library that taps in to twitter’s API. There are two things to remember:

You need to get an app authentication (this is very simple to do). You need time. Twitter limits the amount of requests you can put to its API. Thankfully tweepy can automatically limit how often you ask a request so as long as you have a machine that can be left running this isn’t unsurmountable.

Once you’ve done 1, I suggest putting the authentication details in a twittersecrets.py file. Something like:

consumer_key = "XXXX" consumer_secret = "XXXX" token = "XXXX" token_secret = "XXXX"

Don’t share those. If you use version control, make sure you don’t commit those files.

Once you’ve done that here is a script that will go through the people you follow and will write to file (in case something crashes):

The id number of the user;

The date of their last tweet;

The id number of the people they follow.

Note that in my script I didn’t grab the screen name to file, you should do that, I show you how later. It’s 1 extra line, I don’t know why I didn’t do that.

import tweepy import csv from tqdm import tqdm # An awesome progress bar from twittersecrets import * # This grabs the authentication secrets import os # Authenticating auth = tweepy . OAuthHandler ( consumer_key , consumer_secret ) auth . set_access_token ( token , token_secret ) api = tweepy . API ( auth , wait_on_rate_limit = True , wait_on_rate_limit_notify = False ) def analyse_user ( user_id = None ): """Return the list of followers and last tweet date and text""" try : friends = api . friends_ids ( user_id = user_id ) try : last_tweet = api . user_timeline ( user_id = user_id , count = 1 )[ 0 ] return friends , last_tweet . created_at except IndexError : # No tweets return friends , False except tweepy . error . TweepError : # No access to user return [], False class Friend ( object ): """Holds data""" def __init__ ( self , user_id , friends , last_tweet_date ): self . user_id = user_id self . friends = friends self . last_tweet_date = last_tweet_date def write_to_csv ( self , file = "friends.csv" ): with open ( 'friends.csv' , 'a' ) as f : writer = csv . writer ( f ) row = [ self . user_id , self . last_tweet_date , len ( self . friends )] + self . friends writer . writerow ( row ) if __name__ == "__main__" : try : # This is just to get rid of the file so I didn't add users twice as I # was debugging all this. os . remove ( "friends.csv" ) except FileNotFoundError : pass # Analyse myself: me = Friend ( 0 , * analyse_user ()) me . write_to_csv () # Go through all my friends and write their information to file. for user_id in tqdm ( me . friends ): f = Friend ( user_id , * analyse_user ( user_id )) f . write_to_csv ()

That can take quite a while, it took just under twenty hours for me.

This creates quite a mess of a csv file but it’s not unbearable to deal with (there were perhaps other ways of doing this).

Reading in the data

First all here are the libraries I used:

import datetime # Deal with dates import networkx as nx # Network import matplotlib.pyplot as plt # Graph stuff import seaborn # Graph stuff pretty import tweepy # To analyse further from twittersecrets import * # For authenticating % matplotlib inline # I did this in a notebook so ths magic makes the graphs appear

Here’s a class to hold the data:

class Friend ( object ): def __init__ ( self , user_id , last_tweet_date , friend_ids ): self . user_id = user_id try : self . last_tweet_date = datetime . datetime . strptime ( last_tweet_date , ' % Y- % m- % d % H: % M: % S' ) except ValueError : self . last_tweet_date = False self . friend_ids = friend_ids def friend_ids_in_network ( self , list_of_ids ): return [ n for n in self . friend_ids if n in list_of_ids ] def __str__ ( self ): return self . user_id def __repr__ ( self ): return str ( self )

and here is me reading in the data:

import csv with open ( "friends.csv" , "r" ) as f : # Removing myself from the network network_nodes = [ Friend ( row [ 0 ], row [ 1 ], row [ 2 :]) for row in csv . reader ( f )][ 1 :] network_ids = [ n . user_id for n in network_nodes ] # Ids for all the network

A quick look at the distribution of “friends” (this is what people you follow are referred to as):

plt . hist ([ len ( f . friend_ids ) for f in network_nodes ], bins = 40 ) plt . title ( "Friend numbers of people I follow" );

You can see the peak at 5000 (I think that’s the upper limit of what twitter allows).

Some of those are outside of the network of people I follow, so let me “cut of the outside” of my own network:

for friend in network_nodes : friend . friend_ids_in_network = friend . friend_ids_in_network ( network_ids ) plt . hist ([ len ( f . friend_ids_in_network ) for f in network_nodes ], bins = 40 ) plt . title ( "Friend numbers of people I follow in network of people I follow" );

I have probably followed people who don’t really use twitter anymore. Let me remove them from my network:

>>> # Consider a 1 year limit >>> limit = datetime . datetime . now () - datetime . timedelta ( days = 365 ) >>> active_network_nodes = [] >>> for f in network_nodes : ... if f . last_tweet_date and f . last_tweet_date >= limit : ... active_network_nodes . append ( f )

This is how many people haven’t tweeted for a year (for me: 95).

>>> inactive_users = [ n for n in network_nodes if n not in active_network_nodes ] >>> len ( inactive_users ) 95

Once I’ve done that, I can go through and find out the user names of them (and even use the api to unfollow them if I cared to):

>>> auth = tweepy . OAuthHandler ( consumer_key , consumer_secret ) >>> auth . set_access_token ( token , token_secret ) >>> api = tweepy . API ( auth , wait_on_rate_limit = True , ... wait_on_rate_limit_notify = False ) >>> users = api . lookup_users ( user_ids = inactive_users ) >>> for u in users : ... print ( u . screen_name )

I probably should have collected the screen name in my data collection script. Not sure why I didn’t do that…

Let me look at the active user friend numbers:

>>> plt . hist ([ len ( f . friend_ids_in_network ) for f in active_network_nodes ], bins = 40 ) >>> plt . title ( "Friend numbers of active people I follow in network of people I follow" );

These two graphs look quite similar for me (I’m removing a very biased part of my network).

Let’s build the network

To do this I’m going to use networkx , a great library for manipulating and building networks in Python.

First let me build a dictionary mapping nodes to nodes:

>>> network_map = { f . user_id : f . friend_ids_in_network for f in active_network_nodes }

Before I go any further, let me draw my network. I won’t actually draw the whole network: it’s big and messy but I’ll draw a sample of it:

>>> import random >>> random . seed ( 0 ) # So that this is reproducible >>> number_of_nodes = 400 >>> sampled_nodes = random . sample ( network_map . keys (), number_of_nodes ) >>> sampled_sub_network_map = { key : [ node for node in network_map [ key ] if node in sampled_nodes ] ... for key in sampled_nodes } >>> G = nx . Graph ( sampled_sub_network_map )

Writing the network to a dot file:

>>> from networkx.drawing.nx_agraph import write_dot >>> write_dot ( G , 'net.dot' )

Using graphviz and imagemagick to turn this in to a png:

neato -Tps -Goverlap = scale net.dot -o net.ps ; convert net.ps net.png

This gives, it’s a big file…: net.png.

If you look closely at that image you’ll see that it’s not a completely connected network, this extend to the entire graph:

>>> G = nx . Graph ( network_map ) # Graph for the whole network >>> components = list ( nx . connected_components ( G )) >>> len ( components ) 8

I have 8 components in my graph. Most of these have just 1 user in them:

>>> [ len ( c ) for c in components ] [ 1124 , 1 , 1 , 1 , 1 , 1 , 1 , 1 ]

It’s then easy enough to look at the components that have just 1 user and decide what I want to do with them.

Now for the interesting stuff: let me create a network from the active users in the connected component:

>>> G = nx . Graph ({ key : value for key , value in network_map . items () if key in components [ 0 ]})

Now that I’ve done that I can start looking at all sorts of thing, including the center of the network:

>>> nx . center ( G ) [ '11348282' ]

This center is apparently a single node, and represents the node that has lowest greatest distance to all other nodes. We can use the twitter API to find out who this account is:

>>> api . lookup_users ( center )[ 0 ] . screen_name 'NASA'

Given my interests that’s actually quite neat and not so unsuspected. One in particular is apply a page rank calculation:

>>> pr = nx . pagerank ( G ) >>> sorted_nodes = sorted ([( node , pagerank ) for node , pagerank in pr . items ()], key = lambda x : pr [ x [ 0 ]])

This will then identify the 10 nodes with the highest page rank:

>>> users = api . lookup_users ( user_ids = [ pair [ 0 ] for pair in sorted_nodes [: 10 ]]) >>> for u in users : ... print ( u . screen_name )

I am not sure if a high page rank is actually a good or a bad thing when it comes to my twitter network. I need to think about this a bit more, but one thing that is easy to do is build what is called a maximal independent set.

>>> mis = nx . maximal_independent_set ( G ) # This is in fact an approximative algorithm >>> len ( mis ) 300

So this seems to indicate that there is a set of just 300 accounts that would perhaps be sufficient to follow. This would assume that tweets flow through the network which is of course not quite true.

I’m not going to rush to unfollow anyone but having this data and all the networkx algorithms at my finger tips is awesome.