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hashtagSeq.py
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hashtagSeq.py
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import pandas as pd
import numpy as np
import networkx as nx
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.metrics.pairwise import cosine_similarity
# Data assumptions:
# - 2 Pandas dataframes
# - control: control dataset -> includes only columns ['retweeted_status', 'user', 'in_reply_to_status_id', 'full_text', 'id']
# - treated: information Operation dataset -> includes only columns ['retweet_tweetid', 'userid', 'in_reply_to_tweetid', 'quoted_tweet_tweetid', 'tweet_text', 'tweetid']
# minHashtags: minimum number of hashtags inside an hashtag sequence
def hashSeq(control, treated, minHashtags = 5):
control.replace(np.NaN, None, inplace=True)
control['engagementParentId'] = control['in_reply_to_status_id']
retweet_id = []
names = []
eng = []
for row in control[['retweeted_status', 'user', 'in_reply_to_status_id']].values:
if row[0] != None:
u = dict(row[0])
retweet_id.append(u['id'])
eng.append('retweet')
elif row[2] != None:
retweet_id.append(row[2])
eng.append('reply')
else:
retweet_id.append(None)
eng.append('tweet')
u = dict(row[1])
names.append(u['id'])
control['twitterAuthorScreenname'] = names
control['retweet_ordinalId'] = retweet_id
control['engagementType'] = eng
control['engagementParentId'].fillna(control['retweet_ordinalId'], inplace=True)
control_filt = control[['twitterAuthorScreenname', 'engagementType', 'engagementParentId']]
control_filt['contentText'] = control['full_text']
control_filt['tweetId'] = control['id'].astype(int)
control_filt['tweet_timestamp'] = control_filt['tweetId'].apply(lambda x: get_tweet_timestamp(x))
del control
treated.replace(np.NaN, None, inplace=True)
retweet_id = []
names = []
eng = []
for row in treated[['retweet_tweetid', 'userid', 'in_reply_to_tweetid', 'quoted_tweet_tweetid']].values:
if row[0] != None:
retweet_id.append(row[0])
eng.append('retweet')
elif row[2] != None:
retweet_id.append(row[2])
eng.append('reply')
elif row[3] != None:
retweet_id.append(row[3])
eng.append('quote tweet')
else:
retweet_id.append(None)
eng.append('tweet')
names.append(row[1])
treated['twitterAuthorScreenname'] = names
treated['engagementType'] = eng
treated['engagementParentId'] = retweet_id
treated_filt = treated[['twitterAuthorScreenname', 'engagementType', 'engagementParentId']]
treated_filt['contentText'] = treated['tweet_text']
treated_filt['tweetId'] = treated['tweetid'].astype(int)
treated_filt['tweet_timestamp'] = treated_filt['tweetId'].apply(lambda x: get_tweet_timestamp(x))
del treated
cum = pd.concat([control_filt, treated_filt])
del control_filt, treated_filt
cum = cum.loc[cum['engagementType'] != 'retweet']
cum['hashtag_seq'] = ['__'.join([tag.strip("#") for tag in tweet.split() if tag.startswith("#")]) for tweet in cum['contentText'].values.astype(str)]
cum.drop('contentText', axis=1, inplace=True)
cum = cum[['twitterAuthorScreenname', 'hashtag_seq']].loc[cum['hashtag_seq'].apply(lambda x: len(x.split('__'))) >= i]
cum.drop_duplicates(inplace=True)
temp = cum.groupby('hashtag_seq', as_index=False).count()
cum = cum.loc[cum['hashtag_seq'].isin(temp.loc[temp['twitterAuthorScreenname']>1]['hashtag_seq'].to_list())]
cum['value'] = 1
hashs = dict(zip(list(cum.hashtag_seq.unique()), list(range(cum.hashtag_seq.unique().shape[0]))))
cum['hashtag_seq'] = cum['hashtag_seq'].apply(lambda x: hashs[x]).astype(int)
del hashs
userid = dict(zip(list(cum.twitterAuthorScreenname.astype(str).unique()), list(range(cum.twitterAuthorScreenname.unique().shape[0]))))
cum['twitterAuthorScreenname'] = cum['twitterAuthorScreenname'].astype(str).apply(lambda x: userid[x]).astype(int)
person_c = CategoricalDtype(sorted(cum.twitterAuthorScreenname.unique()), ordered=True)
thing_c = CategoricalDtype(sorted(cum.hashtag_seq.unique()), ordered=True)
row = cum.twitterAuthorScreenname.astype(person_c).cat.codes
col = cum.hashtag_seq.astype(thing_c).cat.codes
sparse_matrix = csr_matrix((cum["value"], (row, col)), shape=(person_c.categories.size, thing_c.categories.size))
del row, col, person_c, thing_c
#cum = pd.pivot_table(cum,'value', 'userid', 'urls', aggfunc='max')
#cum.fillna(0, inplace = True)
vectorizer = TfidfTransformer()
tfidf_matrix = vectorizer.fit_transform(sparse_matrix)
similarities = cosine_similarity(tfidf_matrix, dense_output=False)
df_adj = pd.DataFrame(similarities.toarray())
del similarities
df_adj.index = userid.keys()
df_adj.columns = userid.keys()
G = nx.from_pandas_adjacency(df_adj)
del df_adj
G.remove_nodes_from(list(nx.isolates(G)))
return G