

In [11]:

    
%load_ext autoreload
%autoreload 2
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
from load_utils import *
from analysis_utils import compare_groups









    



The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload



In [12]:

    
df_diffs = load_diffs()



In [4]:

    
df_block_events, df_blocked_user_text = load_block_events_and_users()

How many user talk comments before first attacking comment?



In [20]:

    
t = 0.5

df_first_attack = df_diffs['2015'].query('pred_recipient_score>=%s' % t).sort('rev_timestamp')\
                    .assign(timestamp = lambda x: x.rev_timestamp)\
                    .groupby(['user_text'], as_index=False).first()[['user_text', 'timestamp']]
    

df_counts = df_diffs['2015'].merge(df_first_attack, how = 'inner', on = 'user_text')\
                .assign(delta = lambda x: (x['timestamp'] - x['rev_timestamp']).apply(lambda x: x.days) + 1)\
                .query('delta >=1')
        
def atleast(s):
    s = s.value_counts().value_counts().sort_index()
    n = s.sum()
    return 1 - s.cumsum()/n

s = atleast(df_counts['user_text'])
sr = atleast(df_counts.query('not author_anon')['user_text'])
sa = atleast(df_counts.query('author_anon')['user_text'])

#plt.plot(s.head(20), label = '')
plt.plot(sr.head(20), label = 'registered')
plt.plot(sa.head(20), label = 'anon')

plt.xlabel('n comments')
plt.ylabel('fraction of users who made > n comments')
plt.legend()









    Out[20]:





<matplotlib.legend.Legend at 0x12bd52f60>

Anons produce far fewer comments before their first attack than registered users. This could indicate that anons are more likely to by "drive-by" vandals. Or just be a product of anons not staying around very long in general.

How long are users around before their first block event? (Change for first attack and move)

TODO: make days active instead of days since registration



In [21]:

    
t = 0.5

d_first_post = df_diffs['2015'].sort('rev_timestamp', inplace= False)\
                    .groupby(['user_text', 'author_anon'], as_index=False).first()\
                    [['user_text', 'author_anon', 'rev_timestamp']]
        
df_first_attack = df_diffs['2015'].query('pred_recipient_score>=%s' % t).sort('rev_timestamp')\
                    .assign(timestamp = lambda x: x.rev_timestamp)\
                    .groupby(['user_text'], as_index=False).first()[['user_text', 'timestamp']]
    

dd = d_first_post.merge(df_first_block, how = 'inner', on = 'user_text')\
                .assign(delta = lambda x: (x['timestamp'] - x['rev_timestamp']).apply(lambda x: x.days) + 1)\
                .query('delta >=1')
        
def atleast(s):
    s = s.value_counts().sort_index()
    n = s.sum()
    return 1 - s.cumsum()/n

s = atleast(d['delta'])
sr = atleast(d.query('not author_anon')['delta'])
sa = atleast(d.query('author_anon')['delta'])

#plt.plot(s.head(20), label = '')
plt.plot(sr.head(20), label = 'registered')
plt.plot(sa.head(20), label = 'anon')
plt.legend()

plt.xlabel('n days')
plt.ylabel('fraction of users active for > n days before first attack')
plt.legend()









    Out[21]:





<matplotlib.legend.Legend at 0x1208f2940>

Similarly, anons are registered for fewer days than registered users before their first attack. Half of registered users where registered for over 14 days before attack.