def save_original_tweets_file():
dbm = DBManager('tweets')
query = {
'tweet_obj.retweeted_status': {
'$exists': 0
},
'sentimiento': {
'$exists': 1
},
}
s_objs = dbm.search(query)
with open('tweet_sentiments.csv', 'w', encoding='utf-8') as f_csv:
fieldnames = ['id', 'text', 'tone', 'score']
writer = csv.DictWriter(f_csv, fieldnames=fieldnames)
writer.writeheader()
for s_obj in s_objs:
tweet = s_obj['tweet_obj']
if 'full_text' in tweet.keys():
tweet_text = tweet['full_text']
else:
tweet_text = tweet['text']
tweet_text = clean_emojis(tweet_text)
tweet_text = tweet_text.replace('\r', '')
tweet_text = tweet_text.replace('\n', '')
tweet_text = tweet_text.replace(',', '')
tweet_dict = {
'id': tweet['id_str'],
'text': tweet_text,
'tone': s_obj['sentimiento']['tono'],
'score': s_obj['sentimiento']['score']
}
writer.writerow(tweet_dict)
def compute_bot_probability(self, users, source_users_collection = "", source_users_db = ""):
reusers_db = None
if source_users_db and source_users_collection:
reusers_db = DBManager(source_users_collection, source_users_db)
if not users:
# Get all users who don't have the analysis of bot in current user
users = self.__dbm_users.search({'bot_analysis': {'$exists': 0}})
tot_user = len(users) if type(users) == list else users.count()
idx_user = 1
for user in users:
logging.info('Remaining users: {0}'.format(tot_user-idx_user))
if reusers_db:
reuser_cursor = reusers_db.search({'screen_name': user['screen_name']})
if reuser_cursor.count() > 0:
logging.info('Reusing bot analysis from another DB for {0}'.format(user['screen_name']))
reuser = reuser_cursor[0]
bot_analysis = reuser['bot_analysis']
self.__save_user_pbb(reuser['screen_name'], bot_analysis['pbb'], bot_analysis['raw_score'],
bot_analysis['features'], bot_analysis['num_evaluated_heuristics'],
bot_analysis['sum_weights'], reuser['exists'])
continue
if type(users) == list:
user_screen_name = user
else:
user_screen_name = user['screen_name']
self.__compute_heuristics(user_screen_name)
idx_user += 1
def fix_tweets_with_empty_flags():
dbm = DBManager('tweets')
script_parent_dir = pathlib.Path(__file__).parents[1]
conf_file = script_parent_dir.joinpath('config.json')
configuration = get_config(conf_file)
keyword, k_metadata = parse_metadata(configuration['metadata'])
tweets_with_empty_flags = dbm.search({'flag.keyword': {'$size': 0}, 'relevante': 1})
for tweet in tweets_with_empty_flags:
logging.info('Updating flags of tweet {0}'.format(tweet['tweet_obj']['id_str']))
flag, headers = create_flag(k_metadata)
entities = get_entities_tweet(tweet['tweet_obj'])
flag = add_values_to_flags(flag, entities, k_metadata)
dbm.update_record({'tweet_obj.id_str': tweet['tweet_obj']['id_str']}, flag)
#if __name__ == '__main__':
# fix_tweets_with_empty_flags()
def compute_tweets_local_date(force_computation=False, include_hour=False):
dbm = DBManager('tweets')
if force_computation:
query = {}
else:
query = {'tweet_py_datetime': {'$exists': 0}}
s_objs = dbm.search(query, only_relevant_tws=False)
for s_obj in s_objs:
tweet = s_obj['tweet_obj']
py_pub_dt = get_py_date(tweet)
dict_to_update = {
'tweet_py_datetime': datetime.strftime(py_pub_dt,
'%m/%d/%y %H:%M:%S'),
'tweet_py_date': datetime.strftime(py_pub_dt, '%m/%d/%y')
}
if include_hour:
dict_to_update.update(
{'tweet_py_hour': datetime.strftime(py_pub_dt, '%H')})
dbm.update_record({'tweet_obj.id_str': tweet['id_str']},
dict_to_update)
return
class NetworkAnalyzer:
__dbm_tweets = None
__dbm_users = None
__dbm_networks = None
__network = None
__graph = None
__nodes = set()
__unknown_users = set()
__node_sizes = None
def __init__(self):
self.__dbm_tweets = DBManager('tweets')
self.__dbm_users = DBManager('users')
self.__dbm_networks = DBManager('networks')
self.__network = []
def __computer_ff_ratio(self, friends, followers):
if followers > 0 and friends > 0:
return friends / followers
else:
return 0
# Get interactions in of a given users
def get_in_interactions(self, user_screen_name):
# compute in interactions, meaning, interactions in which the user
# was mentioned, retweeted, quoted, replied
in_inter_query = {'interactions.' + user_screen_name: {'$exists': 1},
'screen_name': {'$ne': user_screen_name}}
n_users = self.__dbm_users.search(in_inter_query)
in_interactions_dict, in_rts, in_rps = {}, {}, {}
in_qts, in_mts = {}, {}
total_in_interactions = 0
total_in_retweets, total_in_replies = 0, 0
total_in_mentions, total_in_quotes = 0, 0
for n_user in n_users:
n_user_interactions = n_user['interactions']
for i_user, interactions in n_user_interactions.items():
if i_user == user_screen_name:
in_interactions_dict[n_user['screen_name']] = interactions['total']
total_in_interactions += interactions['total']
if 'retweets' in interactions.keys():
total_in_retweets += interactions['retweets']
in_rts[n_user['screen_name']] = interactions['retweets']
if 'replies' in interactions.keys():
total_in_replies += interactions['replies']
in_rps[n_user['screen_name']] = interactions['replies']
if 'mentions' in interactions.keys():
total_in_mentions += interactions['mentions']
in_mts[n_user['screen_name']] = interactions['mentions']
if 'quotes' in interactions.keys():
total_in_quotes += interactions['quotes']
in_qts[n_user['screen_name']] = interactions['quotes']
in_interactions_obj = {
'total': {
'count': total_in_interactions,
'details': in_interactions_dict
},
'replies': {
'count': total_in_replies,
'details': in_rps
},
'retweets': {
'count': total_in_retweets,
'details': in_rts
},
'mentions': {
'count': total_in_mentions,
'details': in_mts
},
'quotes': {
'count': total_in_quotes,
'details': in_qts
}
}
user_dict = {
'in_interactions': in_interactions_obj
}
return user_dict
# Get interactions out of a given users
def get_out_interactions(self, user_screen_name):
user = self.__dbm_users.search({'screen_name': user_screen_name})[0]
# compute out interactions, meaning, interactions originated by
# the user
user_interactions = user['interactions']
out_interactions_dict, out_rts = {}, {}
out_rps, out_qts, out_mts = {}, {}, {}
total_out_interactions, total_out_retweets = 0, 0
total_out_mentions, total_out_replies = 0, 0
total_out_quotes = 0
for recipient, interactions in user_interactions.items():
out_interactions_dict[recipient] = interactions['total']
total_out_interactions += interactions['total']
if 'retweets' in interactions:
total_out_retweets += interactions['retweets']
out_rts[recipient] = interactions['retweets']
if 'replies' in interactions:
total_out_replies += interactions['replies']
out_rps[recipient] = interactions['replies']
if 'mentions' in interactions:
total_out_mentions += interactions['mentions']
out_mts[recipient] = interactions['mentions']
if 'quotes' in interactions:
total_out_quotes += interactions['quotes']
out_qts[recipient] = interactions['quotes']
out_interactions_obj = {
'total': {
'count': total_out_interactions,
'details': out_interactions_dict
},
'replies': {
'count': total_out_replies,
'details': out_rps
},
'retweets': {
'count': total_out_retweets,
'details': out_rts
},
'mentions': {
'count': total_out_mentions,
'details': out_mts
},
'quotes': {
'count': total_out_quotes,
'details': out_qts
}
}
# compile all information in a dictionary
user_dict = {
'out_interactions': out_interactions_obj
}
return user_dict
def create_users_db(self, clear_collection=False):
logging.info('::. Network Analyzer: Creating database of users, it can take several minutes, please wait_')
if clear_collection:
self.__dbm_users.clear_collection()
users = self.__dbm_tweets.get_unique_users()
users_count = len(users)
logging.info('::. Network Analyzer: Extracted {0} unique users from the database...'.format(users_count))
progress = 1
for user in users:
db_user = {
'screen_name': user['screen_name'],
'friends': user['friends'],
'followers': user['followers'],
'ff_ratio': self.__computer_ff_ratio(user['friends'], user['followers']),
'interactions': user['interactions'],
'tweets': user['tweets_count'],
'original_tweets': user['original_count'],
'rts': user['retweets_count'],
'qts': user['quotes_count'],
'rps': user['replies_count'],
'verified': user['verified']
}
# Assign the party and movement to the party and movement that are more related to the user
# counting both Hashtags and Mentions by the user
user_parties = self.__dbm_tweets.get_party_user(user['screen_name'])
user_parties_count = len(user_parties) or 0
logging.debug('::. Network Analyzer: User {0} has {1} associated parties...'
.format(user['screen_name'],user_parties_count))
if user_parties_count > 0:
user_party = user_parties[0]
db_user.update({'most_interacted_party': user_party['partido']})
user_movements = self.__dbm_tweets.get_movement_user(user['screen_name'])
user_movements_count = len(user_movements) or 0
logging.debug('::. Network Analyzer: User {0} has {1} associated movements...'
.format(user['screen_name'], user_movements_count))
if user_movements_count > 0:
user_movement = user_movements[0]
db_user.update({'most_interacted_movement': user_movement['movimiento']})
else:
db_user.update({'most_interacted_movement': ''})
else:
db_user.update({'most_interacted_party': '', 'movement': ''})
# Assign the party and movement to the party and movement that are more related to the user
# counting both Hashtags and Mentions by the user
upp = UserPoliticalPreference()
user_party = upp.get_user_political_party(user['screen_name'])
user_movement = upp.get_user_political_movement(user['screen_name'])
db_user.update({'party': user_party, 'movement': user_movement})
filter_query = {'screen_name': user['screen_name']}
logging.debug('::. Network Analyzer: Updating/creating user {0} ({1}/{2})...'
.format(user['screen_name'], progress, users_count))
progress += 1
self.__dbm_users.update_record(filter_query, db_user, create_if_doesnt_exist=True)
def generate_network(self, subnet_query={}, depth=1, file_name='network', override_net=False):
net_query = subnet_query.copy()
net_query.update({'depth': depth})
ret_net = self.__dbm_networks.search(net_query)
# the net doesn't exist yet, let's create it
if ret_net.count() == 0 or override_net:
logging.info('Generating the network, it can take several minutes, please wait_')
users = self.__dbm_users.search(subnet_query)
# for each user generate his/her edges
for user in users:
if 'ff_ratio' in user.keys():
u_ff_ratio = user['ff_ratio']
else:
u_ff_ratio = self.__computer_ff_ratio(user['friends'], user['followers'])
pbb_score = user['bot_analysis']['pbb'] if 'bot_analysis' in user.keys() else ''
self.__nodes.add(tuple({'screen_name': user['screen_name'], 'party': user['party'],
'movement': user['movement'], 'ff_ratio': u_ff_ratio,
'pbb': pbb_score}.items()))
for interacted_user, interactions in user['interactions'].items():
iuser = self.__dbm_users.find_record({'screen_name': interacted_user})
if not iuser:
if depth > 1:
iuser_ffratio = self.__get_ffratio(interacted_user)
if not iuser_ffratio:
self.__unknown_users.add(interacted_user)
continue
else:
self.__unknown_users.add(interacted_user)
continue
else:
if 'ff_ratio' in iuser.keys():
i_ff_ratio = iuser['ff_ratio']
else:
i_ff_ratio = self.__computer_ff_ratio(iuser['friends'], iuser['followers'])
pbb_iuser_score = user['bot_analysis']['pbb'] if 'bot_analysis' in iuser.keys() else ''
self.__nodes.add(tuple({'screen_name': iuser['screen_name'], 'party': iuser['party'],
'movement': iuser['movement'], 'ff_ratio': i_ff_ratio,
'pbb': pbb_iuser_score}.items()))
edge = {
'nodeA': {'screen_name': user['screen_name'], 'ff_ratio': u_ff_ratio,
'party': user['party'], 'movement': user['movement'],
'pbb': pbb_score},
'nodeB': {'screen_name': interacted_user, 'ff_ratio': i_ff_ratio,
'party': iuser['party'], 'movement': iuser['movement'],
'pbb': pbb_iuser_score},
'weight': interactions['total']
}
self.__network.append(edge)
logging.info('Created a network of {0} nodes and {1} edges'.format(len(self.__nodes), len(self.__network)))
logging.info('Unknown users {0}'.format(len(self.__unknown_users)))
# save the net in a gefx file for posterior usage
f_name = self.save_network_in_gexf_format(file_name)
logging.info('Saved the network in the file {0}'.format(f_name))
db_net = {'file_name': str(f_name)}
db_net.update(net_query)
self.__dbm_networks.save_record(db_net)
else:
f_net = ret_net[0]
logging.info('The network was already generated, please find it at {0}'.format(f_net['file_name']))
def create_graph(self):
logging.info('Creating the graph, please wait_')
self.__graph = net.DiGraph()
ff_ratio = defaultdict(lambda: 0.0)
# create a directed graph from the edge data and populate a dictionary
# with the friends/followers ratio
for edge in self.__network:
user = edge['nodeA']['screen_name']
interacted_with = edge['nodeB']['screen_name']
num_interactions = edge['weight']
u_ff_ratio = edge['nodeA']['ff_ratio']
self.__graph.add_edge(user, interacted_with, weight=int(num_interactions))
ff_ratio[user] = float(u_ff_ratio)
# obtain central node
# degrees = net.degree(self.__graph)
# central_node, max_degree = sorted(degrees, key=itemgetter(1))[-1]
# center the graph around the central node
# ego_graph = net.DiGraph(net.ego_graph(self.__graph, central_node))
return
def get_graph_nodes(self):
return len(self.__nodes)
def get_graph_edges(self):
return len(self.__network)
def get_graph(self):
return self.__graph
def get_node_sizes(self):
return self.__node_sizes
def __get_ffratio(self, screen_name):
query = {
'$or': [
{'tweet_obj.user.screen_name': screen_name},
{'tweet_obj.retweeted_status.user.screen_name': screen_name},
{'tweet_obj.quoted_status.user.screen_name': screen_name}
]
}
tweet_obj = self.__dbm_tweets.find_record(query)
if tweet_obj:
tweet = tweet_obj['tweet_obj']
if 'retweeted_status' in tweet.keys():
return self.__computer_ff_ratio(tweet['retweeted_status']['user']['friends_count'],
tweet['retweeted_status']['user']['followers_count'])
elif 'quoted_status' in tweet.keys():
return self.__computer_ff_ratio(tweet['quoted_status']['user']['friends_count'],
tweet['quoted_status']['user']['followers_count'])
else:
return self.__computer_ff_ratio(tweet['user']['friends_count'],
tweet['user']['followers_count'])
else:
return None
def save_network_in_gexf_format(self, file_name):
today = datetime.strftime(datetime.now(), '%m/%d/%y')
f_name = pathlib.Path(__file__).parents[2].joinpath('sna', 'gefx', file_name+'.gexf')
with open(str(f_name), 'w', encoding='utf-8') as f:
f.write('<?xml version="1.0" encoding="UTF-8"?>\n')
f.write('<gexf xmlns="http://www.gexf.net/1.2draft" xmlns:viz="http://www.gexf.net/1.1draft/viz" '
'xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" '
'xsi:schemaLocation="http://www.gexf.net/1.2draft http://www.gexf.net/1.2draft/gexf.xsd" '
'version="1.2">\n')
f.write('<meta lastmodifieddate="{0}">\n'.format(today))
f.write('<creator>PoliticBots</creator>\n')
f.write('<description>{0}</description>\n'.format(file_name))
f.write('</meta>\n')
f.write('<graph mode="static" defaultedgetype="directed">\n')
# add data attributes
f.write('<attributes class="node">\n')
f.write('<attribute id="0" title="party" type="string"/>\n')
f.write('<attribute id="1" title="movement" type="string"/>\n')
f.write('<attribute id="2" title="ff_ratio" type="float"/>\n')
f.write('<attribute id="3" title="pbb" type="float"/>\n')
f.write('</attributes>\n')
# add nodes
f.write('<nodes>\n')
node_id = 0
list_nodes = []
for node_tup in self.__nodes:
node = dict(node_tup)
f.write('<node id="{0}" label="{1}">\n'.format(node_id, node['screen_name']))
f.write('<attvalues>\n')
f.write('<attvalue for="0" value="{0}"/>\n'.format(node['party']))
f.write('<attvalue for="1" value="{0}"/>\n'.format(node['movement']))
f.write('<attvalue for="2" value="{0}"/>\n'.format(node['ff_ratio']))
f.write('<attvalue for="3" value="{0}"/>\n'.format(node['pbb']))
f.write('</attvalues>\n')
#f.write('<viz:size value="{0}"/>\n'.format(node['ff_ratio']))
f.write('</node>\n')
node_id += 1
list_nodes.append(node['screen_name'])
f.write('</nodes>\n')
# add edges
f.write('<edges>\n')
edge_id = 0
for edge in list(self.__network):
id_vertexA = list_nodes.index(edge['nodeA']['screen_name'])
id_vertexB = list_nodes.index(edge['nodeB']['screen_name'])
weight = edge['weight']
f.write('<edge id="{0}" source="{1}" target="{2}" weight="{3}"/>\n'.format(edge_id, id_vertexA,
id_vertexB, weight))
edge_id += 1
f.write('</edges>\n')
f.write('</graph>\n')
f.write('</gexf>\n')
return f_name
class HashtagDiscoverer:
user_handlers, hashtags = [], []
__dbm = None
def __init__(self):
self.user_handlers, self.hashtags = get_user_handlers_and_hashtags()
self.__dbm = DBManager('tweets')
def discover_hashtags_by_text(self, tweet_text):
new_hashtags = set()
for token in tweet_text.split():
if u'\u2026' in token:
continue
if '#' in token:
if token.lower() not in self.hashtags:
new_hashtags.add(token)
return new_hashtags
def discover_hashtags_by_entities(self, tweet_hashtags):
new_hashtags = set()
for tweet_hashtag in tweet_hashtags:
tweet_hashtag_txt = '#' + tweet_hashtag['text'].lower()
if u'\u2026' in tweet_hashtag_txt:
continue
if tweet_hashtag_txt not in self.hashtags:
new_hashtags.add('#' + tweet_hashtag['text'])
return new_hashtags
def discover_new_hashtags(self, query={}, sorted_results=True):
tweet_regs = self.__dbm.search(query)
new_hashtags = defaultdict(int)
for tweet_reg in tweet_regs:
tweet = tweet_reg['tweet_obj']
if 'retweeted_status' in tweet.keys():
original_tweet = tweet['retweeted_status']
else:
original_tweet = tweet
if 'entities' in original_tweet.keys():
t_hashtags = original_tweet['entities']['hashtags']
discovered_hashtags = self.discover_hashtags_by_entities(
t_hashtags)
else:
if 'full_text' in original_tweet.keys():
tweet_text = tweet['full_text']
else:
tweet_text = tweet['text']
discovered_hashtags = self.discover_hashtags_by_text(
tweet_text)
for discovered_hashtag in discovered_hashtags:
new_hashtags[discovered_hashtag] += 1
if sorted_results:
return [(k, new_hashtags[k]) for k in sorted(
new_hashtags, key=new_hashtags.get, reverse=True)]
else:
return new_hashtags
def discover_coccurrence_hashtags_by_text(self, tweet_text):
known_hashtags = set()
for token in tweet_text.split():
if u'\u2026' in token:
continue
if '#' in token:
if token.lower() in self.hashtags:
known_hashtags.add(token)
if len(known_hashtags) > 0:
return ' '.join(known_hashtags)
else:
return None
def discover_coccurrence_hashtags_by_entities(self, tweet_hashtags):
known_hashtags = set()
for tweet_hashtag in tweet_hashtags:
tweet_hashtag_txt = '#' + tweet_hashtag['text'].lower()
if u'\u2026' in tweet_hashtag_txt:
continue
if tweet_hashtag_txt in self.hashtags:
known_hashtags.add('#' + tweet_hashtag['text'])
if len(known_hashtags) > 0:
return ' '.join(known_hashtags)
else:
return None
def coccurence_hashtags(self, query={}, sorted_results=True):
coccurence_hashtags_dict = defaultdict(int)
tweet_regs = self.__dbm.search(query)
for tweet_reg in tweet_regs:
tweet = tweet_reg['tweet_obj']
if 'retweeted_status' in tweet.keys():
original_tweet = tweet['retweeted_status']
else:
original_tweet = tweet
if 'entities' in original_tweet.keys():
t_hashtags = original_tweet['entities']['hashtags']
coccurrence_hashtag_str = self.discover_coccurrence_hashtags_by_entities(
t_hashtags)
else:
if 'full_text' in original_tweet.keys():
tweet_text = tweet['full_text']
else:
tweet_text = tweet['text']
coccurrence_hashtag_str = self.discover_coccurrence_hashtags_by_text(
tweet_text)
if coccurrence_hashtag_str:
coccurence_hashtags_dict[coccurrence_hashtag_str] += 1
if sorted_results:
return [(k, coccurence_hashtags_dict[k])
for k in sorted(coccurence_hashtags_dict,
key=coccurence_hashtags_dict.get,
reverse=True)]
else:
return coccurence_hashtags_dict
class TweetEvaluator:
special_chars = r'[=\+/&<>;:\'\"\?%$!¡\,\. \t\r\n]+'
hashtags, user_handlers = [], []
__dbm = None
BATCH_SIZE = 1000
def __init__(self):
self.user_handlers, self.hashtags = get_user_handlers_and_hashtags()
self.__dbm = DBManager('tweets')
def __is_relevant(self, users_counter, hashtags_counter):
# a tweet is considered relevant if fulfills one of two
# conditions; candidates are mentioned or if candidates are
# are not mentioned but there are at least more than one
# campaign hashtag
if users_counter > 0 or hashtags_counter > 1:
return True
else:
return False
def __assess_tweet_by_text(self, tweet_text):
tweet_text = re.sub(u'\u2026', '',
tweet_text) # remove ellipsis unicode char
users_counter, hashtags_counter = 0, 0
for token in tweet_text.split():
token = re.sub(self.special_chars, '',
token) # remove special chars
if token.lower() in self.user_handlers:
users_counter += 1
if token.lower() in self.hashtags:
hashtags_counter += 1
return self.__is_relevant(users_counter, hashtags_counter)
def __assess_tweet_by_entities(self, tweet_hashtags, tweet_mentions):
users_counter, hashtags_counter = 0, 0
for tweet_hashtag in tweet_hashtags:
tweet_hashtag_txt = '#' + tweet_hashtag['text'].lower()
if tweet_hashtag_txt in self.hashtags:
hashtags_counter += 1
for tweet_mention in tweet_mentions:
screen_name = '@' + tweet_mention['screen_name'].lower()
if screen_name in self.user_handlers:
users_counter += 1
return self.__is_relevant(users_counter, hashtags_counter)
def is_tweet_relevant(self, tweet):
tweet_author = tweet['user']['screen_name']
tweet_handler = '@{0}'.format(tweet_author.lower())
if tweet_handler in self.user_handlers:
return True
else:
if 'retweeted_status' in tweet.keys():
original_tweet = tweet['retweeted_status']
else:
original_tweet = tweet
if 'entities' in original_tweet.keys():
t_user_mentions = original_tweet['entities']['user_mentions']
t_hashtags = original_tweet['entities']['hashtags']
return self.__assess_tweet_by_entities(t_hashtags,
t_user_mentions)
else:
if 'full_text' in original_tweet.keys():
return self.__assess_tweet_by_text(tweet['full_text'])
else:
return self.__assess_tweet_by_text(tweet['text'])
def __mark_relevance_rt(self, tweet_reg):
logging.info('Marking RTS...')
query = {
'tweet_obj.retweeted_status': {
'$exists': 1
},
'tweet_obj.retweeted_status.id_str': {
'$eq': tweet_reg['tweet_obj']['id_str']
},
'relevante': {
'$ne': tweet_reg['relevante']
}
}
update = {'$set': {'relevante': tweet_reg['relevante']}}
update_res = self.__dbm.update_record_many(query, update)
logging.info('Marked {0} RTS...'.format(update_res.matched_count))
def identify_relevant_tweets(self):
# select only original tweets that are not marked as relevant
query = {
'relevante': {
'$exists': 0
},
'tweet_obj.retweeted_status': {
'$exists': 0
}
}
logging.info('Relevant Tweets: Running query to count...')
# processing by batch as workaround cursor not found error
total_tweets = self.__dbm.search(query,
only_relevant_tws=False).count()
total_batches = ceil(total_tweets / self.BATCH_SIZE)
batch = 1
moreToProcess = batch <= total_batches
while moreToProcess:
logging.info(
'Querying records in batches of {0} records...'.format(
self.BATCH_SIZE))
search_res = self.__dbm.search(
query, only_relevant_tws=False).limit(self.BATCH_SIZE)
logging.info('Loading batch {0}/{1} into memory...'.format(
batch, total_batches))
tweets = [doc for doc in search_res]
total_tweets_batch = self.BATCH_SIZE
if batch == total_batches:
total_tweets_batch = len(tweets)
logging.info(
'Identifying relevant tweets in batch {0}/{1} out of {2} tweets...'
.format(batch, total_batches, total_tweets_batch))
tweet_counter = 0
try:
for tweet_reg in tweets:
tweet_counter += 1
tweet = tweet_reg['tweet_obj']
if self.is_tweet_relevant(tweet):
tweet_reg['relevante'] = 1
logging.info(
'Identifying {0}/{1} tweets (relevant)'.format(
tweet_counter, total_tweets))
else:
tweet_reg['relevante'] = 0
logging.info(
'Identifying {0}/{1} tweets (irrelevant)'.format(
tweet_counter, total_tweets))
self.__dbm.update_record(
{'tweet_obj.id_str': tweet['id_str']}, tweet_reg)
# copy the relevance flag to rts
self.__mark_relevance_rt(tweet_reg)
logging.info(
'Finished identifying relevant tweets in batch {0}/{1} out of {2} tweets...'
.format(batch, total_batches, total_tweets_batch))
batch += 1
moreToProcess = batch <= total_batches
except Exception as e:
logging.info("Exception occurred...")
logging.info("Exception message {0}".format(e))
logging.info('Finished identifying relevant tweets...')
return True
# set to 'user' the type of tweets which keyword contains @
def fix_tweet_type(self):
query = {'type': 'hashtag', 'keyword': {'$regex': '@'}}
objs = self.__dbm.search(query)
num_fixed_tweets = objs.count()
for obj in objs:
obj['type'] = 'user'
self.__dbm.save_record(obj)
return num_fixed_tweets
def __get_hashtags(self, hashtags_list):
hts = []
for ht in hashtags_list:
hts.append(ht['text'])
return hts
def __get_screen_names(self, screen_names_list):
scs = []
for sc in screen_names_list:
scs.append('@' + sc['screen_name'])
return scs
# fix value of candidatura if hashtags related to a candidacy
# are present in the text of the tweet
def fix_value_of_candidatura(self):
script_parent_dir = pathlib.Path(__file__).parents[1]
config_fn = script_parent_dir.joinpath('config.json')
configuration = get_config(config_fn)
keyword, k_metadata = parse_metadata(configuration['metadata'])
interested_data = []
# keep metadata that refer to candidacies
for kword, kmetada in zip(keyword, k_metadata):
if kmetada['candidatura'] != '':
kmetada.update({'keyword': kword})
interested_data.append(kmetada)
query = {'candidatura': ''}
# select tweets without candidacy
s_objs = self.__dbm.search(query)
num_fixed_tweets = 0
# iterate over tweets without candidacy and fix those
# whose text mention a candidate or have hashtags
# related to a candidacy
for s_obj in s_objs:
party = s_obj['partido_politico']
movement = s_obj['movimiento']
tweet = s_obj['tweet_obj']
relevant_data = []
candidacy = ''
# keep metadata related to the political party
# (and movement) of the tweet (s_obj)
for ida in interested_data:
if ida['partido_politico'] == party:
if movement != '':
if ida['movimiento'] == movement:
relevant_data.append(ida)
else:
relevant_data.append(ida)
if len(relevant_data) > 0:
# extract relevant information of the tweet. hashtags and mentions if
# the tweet obj has these entities otherwise the text of the tweet
if 'retweeted_status' in tweet.keys():
original_tweet = tweet['retweeted_status']
else:
original_tweet = tweet
if 'entities' in original_tweet.keys():
t_user_mentions = self.__get_screen_names(
original_tweet['entities']['user_mentions'])
t_hashtags = self.__get_hashtags(
original_tweet['entities']['hashtags'])
# see if the interested keywords are part of the tweet hashtags or mentions
for rd in relevant_data:
if rd['keyword'] in t_user_mentions:
candidacy = rd['candidatura']
break
else:
if rd['keyword'] in t_hashtags:
candidacy = rd['candidatura']
break
else:
if 'full_text' in original_tweet.keys():
t_text = tweet['full_text']
else:
t_text = tweet['text']
# see if the interested keywords are present in the text
for rd in relevant_data:
if rd['keyword'] in t_text:
candidacy = rd['candidatura']
break
# fix candidacy key
if candidacy:
s_obj['candidatura'] = candidacy
num_fixed_tweets += 1
self.__dbm.save_record(s_obj)
return num_fixed_tweets
class UserPoliticalPreference:
db_tweets, db_users = None, None
def __init__(self):
self.db_tweets = DBManager('tweets')
self.db_users = DBManager('users')
self.hashtags, self.metadata = self.__get_hashtags_and_metadata()
def __get_hashtags_and_metadata(self):
script_parent_dir = pathlib.Path(__file__).parents[1]
config_fn = script_parent_dir.joinpath('config.json')
configuration = get_config(config_fn)
keywords, metadata = parse_metadata(configuration['metadata'])
hashtags = []
for keyword in keywords:
if '@' not in keyword:
# The following hashtags are excluded because they are proper names of
# movements and people
if keyword not in ['HonorColorado', 'ColoradoAñetete', 'tuma']:
hashtags.append(keyword.lower())
return hashtags, metadata
def __get_tweet_hashtags(self, tweet_obj):
tweet_hashtags = []
if 'entities' in tweet_obj.keys():
for hashtag in tweet_obj['entities']['hashtags']:
tweet_hashtags.append(hashtag['text'])
return tweet_hashtags
def __get_hashtag_metadata(self, hashtag):
for metadata in self.metadata:
if metadata['keyword'].lower() == hashtag.lower():
return metadata
def get_user_political_movement(self, user_screen_name):
user_movement = None
user_political_preference = defaultdict(int)
filter = {
'relevante': {
'$eq': 1
},
'tweet_obj.user.screen_name': {
'$eq': user_screen_name
}
}
results = self.db_tweets.search(filter)
for tweet in results:
tweet_obj = tweet['tweet_obj']
if 'retweeted_status' in tweet_obj.keys():
tweet_hashtags = self.__get_tweet_hashtags(
tweet_obj['retweeted_status'])
else:
tweet_hashtags = self.__get_tweet_hashtags(tweet_obj)
for hashtag in tweet_hashtags:
if hashtag.lower() in self.hashtags:
hashtag_metadata = self.__get_hashtag_metadata(hashtag)
if hashtag_metadata['movimiento']:
user_political_preference[
hashtag_metadata['movimiento']] += 1
if user_political_preference:
s_user_political_preference = [
k for k in sorted(user_political_preference.items(),
key=lambda k_v: k_v[1],
reverse=True)
]
user_movement = s_user_political_preference[0][0]
return user_movement
def get_user_political_party(self, user_screen_name):
user_party = None
user_political_preference = defaultdict(int)
filter = {
'relevante': {
'$eq': 1
},
'tweet_obj.user.screen_name': {
'$eq': user_screen_name
}
}
results = self.db_tweets.search(filter)
for tweet in results:
tweet_obj = tweet['tweet_obj']
if 'retweeted_status' in tweet_obj.keys():
tweet_hashtags = self.__get_tweet_hashtags(
tweet_obj['retweeted_status'])
else:
tweet_hashtags = self.__get_tweet_hashtags(tweet_obj)
for hashtag in tweet_hashtags:
if hashtag.lower() in self.hashtags:
hashtag_metadata = self.__get_hashtag_metadata(hashtag)
if hashtag_metadata['partido_politico']:
user_political_preference[
hashtag_metadata['partido_politico']] += 1
if user_political_preference:
s_user_political_preference = [
k for k in sorted(user_political_preference.items(),
key=lambda k_v: k_v[1],
reverse=True)
]
user_party = s_user_political_preference[0][0]
return user_party
def update_users_political_preference(self, include_movement=True):
users = self.db_users.search({})
total_users = users.count()
users_counter = 0
for user in users:
users_counter += 1
user_movement, user_party = None, None
logging.info('Processing {0}/{1} users'.format(
users_counter, total_users))
if include_movement:
user_movement = self.get_user_political_movement(
user['screen_name'])
user_party = self.get_user_political_party(user['screen_name'])
logging.info('User {0} demonstrates to support {1}, {2}'.format(
user['screen_name'], user_party, user_movement))
self.db_users.update_record({'screen_name': user['screen_name']}, {
'party': user_party,
'movement': user_movement
})
def update_user_most_interacted_party_movement(self,
include_movement=True):
users = self.db_users.search({})
total_users = users.count()
users_counter = 0
for user in users:
users_counter += 1
user_most_interacted_movement, user_most_interacted_party = None, None
logging.info('Processing {0}/{1} users'.format(
users_counter, total_users))
if include_movement:
user_interacted_movements = self.db_tweets.get_movement_user(
user['screen_name'])
if len(user_interacted_movements) > 0:
user_most_interacted_movement = user_interacted_movements[
0]['movimiento']
user_interacted_parties = self.db_tweets.get_party_user(
user['screen_name'])
if len(user_interacted_parties) > 0:
user_most_interacted_party = user_interacted_parties[0][
'partido']
self.db_users.update_record(
{'screen_name': user['screen_name']}, {
'most_interacted_party': user_most_interacted_party,
'most_interacted_movement': user_most_interacted_movement
})
def update_tweet_user_political_preference(self, include_movement=True):
tweets = self.db_tweets.search({})
tweet_authors = defaultdict(dict)
total_tweets = tweets.count()
tweet_counter = 0
for tweet in tweets:
tweet_counter += 1
logging.info('Processing {0}/{1} tweets'.format(
tweet_counter, total_tweets))
tweet_obj = tweet['tweet_obj']
new_fields = {}
if tweet_obj['user']['screen_name'] not in tweet_authors.keys():
user = self.db_users.search(
{'screen_name': tweet_obj['user']['screen_name']})
try:
new_fields['author_party'] = user[0]['party']
except IndexError:
new_fields['author_party'] = None
if include_movement:
try:
new_fields.update(
{'author_movement': user[0]['movement']})
except IndexError:
new_fields.update({'author_movement': None})
tweet_authors[tweet_obj['user']['screen_name']] = new_fields
else:
new_fields = tweet_authors[tweet_obj['user']['screen_name']]
self.db_tweets.update_record(
{'tweet_obj.id_str': tweet_obj['id_str']}, new_fields)
def update_tweet_user_pbb(self):
tweets = self.db_tweets.search({})
tweet_authors = defaultdict(dict)
total_tweets = tweets.count()
tweet_counter = 0
for tweet in tweets:
tweet_counter += 1
logging.info('Processing {0}/{1} tweets'.format(
tweet_counter, total_tweets))
tweet_obj = tweet['tweet_obj']
new_fields = {}
if tweet_obj['user']['screen_name'] not in tweet_authors.keys():
user = self.db_users.search(
{'screen_name': tweet_obj['user']['screen_name']})
try:
new_fields['author_pbb'] = user[0]['bot_analysis']['pbb']
except IndexError:
new_fields['author_party'] = -1
tweet_authors[tweet_obj['user']['screen_name']] = new_fields
else:
new_fields = tweet_authors[tweet_obj['user']['screen_name']]
self.db_tweets.update_record(
{'tweet_obj.id_str': tweet_obj['id_str']}, new_fields)
class UserInteractions:
db_tweets, db_users = None, None
def __init__(self):
self.db_tweets = DBManager('tweets')
self.db_users = DBManager('users')
def __get_user_party(self, user_screen_name):
user = self.db_users.search({'screen_name': user_screen_name})
try:
party = user[0]['party']
return party if party else 'desconocido'
except IndexError:
return 'desconocido'
def __get_user_movement(self, user_screen_name):
user = self.db_users.search({'screen_name': user_screen_name})
try:
movement = user[0]['movement']
if movement:
return movement
else:
return 'desconocido'
except IndexError:
return 'desconocido'
def __user_belong_party_movement(self, party, movement, tweet_author,
tweet_authors):
if not party and not movement:
return True, tweet_authors
party = party.lower()
movement = movement.lower()
if tweet_authors[tweet_author]:
tweet_author_party, tweet_author_movement = tweet_authors[tweet_author]['party'], \
tweet_authors[tweet_author]['movement']
else:
tweet_author_party, tweet_author_movement = 'desconocido', 'desconocido'
if party and tweet_author_party == 'desconocido':
logging.info(
'Need to get the party of the user {0}'.format(tweet_author))
tweet_author_party = self.__get_user_party(tweet_author)
if tweet_authors[tweet_author]:
tweet_authors[tweet_author]['party'] = tweet_author_party
else:
tweet_authors[tweet_author] = {
'party': tweet_author_party,
'movement': 'desconocido'
}
if movement and tweet_author_movement == 'desconocido':
logging.info('Need to get the movement of the user {0}'.format(
tweet_author))
tweet_author_movement = self.__get_user_movement(tweet_author)
if tweet_authors[tweet_author]:
tweet_authors[tweet_author]['movement'] = tweet_author_movement
else:
tweet_authors[tweet_author] = {
'party': 'desconocido',
'movement': tweet_author_movement
}
if party and movement:
if party == tweet_author_party and movement == tweet_author_movement:
return True, tweet_authors
else:
return False, tweet_authors
else:
if party:
if party == tweet_author_party:
return True, tweet_authors
else:
return False, tweet_authors
else:
if movement:
if movement == tweet_author_movement:
return True, tweet_authors
else:
return False, tweet_authors
else:
return False, tweet_authors
def __get_mentions_in_tweet(self, tweet_obj):
user_mentions = []
if 'entities' in tweet_obj.keys():
for mention in tweet_obj['entities']['user_mentions']:
user_mentions.append(mention['screen_name'])
return user_mentions
def __process_tweet(self, tweet, type_tweet, interactions):
for interaction in interactions:
if interaction['date'] == tweet['tweet_py_date'] and interaction[
'type'] == type_tweet:
interaction['count'] += 1
return interactions
interactions.append({
'date': tweet['tweet_py_date'],
'type': type_tweet,
'count': 1
})
return interactions
def get_inter_received_user(self,
user_screen_name,
party=None,
movement=None,
exclude_tweet=None):
tweets = self.db_tweets.search({})
interactions_user = []
tweet_authors = defaultdict(dict)
total_tweets = tweets.count()
tweet_counter = 0
for tweet in tweets:
tweet_counter += 1
logging.info('Processing {0}/{1} tweets'.format(
tweet_counter, total_tweets))
tweet_obj = tweet['tweet_obj']
# discard tweets posted by the given user
if tweet_obj['user']['screen_name'] == user_screen_name:
continue
# discard tweet users that do not belong to the given party and movement
belong_party_movement, tweet_authors = self.__user_belong_party_movement(
party, movement, tweet_obj['user']['screen_name'],
tweet_authors)
if not belong_party_movement:
continue
# process replies to the given user
if tweet_obj['in_reply_to_screen_name'] == user_screen_name:
if not exclude_tweet or (exclude_tweet
and tweet_obj['in_reply_to_status_id']
!= exclude_tweet):
interactions_user = self.__process_tweet(
tweet, 'reply', interactions_user)
# process quotes to the given user
elif 'quoted_status' in tweet_obj.keys(
) and tweet_obj['quoted_status']['user'][
'screen_name'] == user_screen_name:
if not exclude_tweet or (
exclude_tweet and
tweet_obj['quoted_status']['id_str'] != exclude_tweet):
interactions_user = self.__process_tweet(
tweet, 'quote', interactions_user)
# process retweets to the given user's tweets
elif 'retweeted_status' in tweet_obj.keys(
) and tweet_obj['retweeted_status']['user'][
'screen_name'] == user_screen_name:
if not exclude_tweet or (
exclude_tweet
and tweet_obj['retweeted_status']['id_str'] !=
exclude_tweet):
interactions_user = self.__process_tweet(
tweet, 'retweet', interactions_user)
# process mentions to the given user if the tweet is not a reply. we are interested in
# original tweets that include the mention to the given user not in replies that by default
# include the screen name of the given user
else:
tweet_mentions = self.__get_mentions_in_tweet(tweet_obj)
if user_screen_name in tweet_mentions:
interactions_user = self.__process_tweet(
tweet, 'mention', interactions_user)
return interactions_user
class SentimentAnalysis:
config_file_name = pathlib.Path(__file__).parents[1].joinpath(
'config.json')
config = None
language = ''
method = ''
__db = None
def __init__(self, collection='tweets', language='spanish'):
self.config = get_config(self.config_file_name)
self.language = language
self.__dbm = DBManager(collection)
def __get_analyzed_tweet(self, analyzed_tweets, id_tweet_to_search):
for analyzed_tweet in analyzed_tweets:
if id_tweet_to_search == analyzed_tweet['id']:
return analyzed_tweet
return None
def update_sentiment_of_non_original_tweets(self,
query={},
update_sentiment=False):
if update_sentiment:
query.update({
'relevante': 1,
})
else:
query.update({'relevante': 1, 'sentimiento': {'$exists': 0}})
tweet_regs = self.__dbm.search(query)
rts_wo_tw = []
for tweet_reg in tweet_regs:
if 'retweeted_status' in tweet_reg['tweet_obj'].keys():
id_original_tweet = tweet_reg['tweet_obj']['retweeted_status'][
'id_str']
original_tweet_reg = self.__dbm.find_record(
{'tweet_obj.id_str': id_original_tweet})
if original_tweet_reg:
sentiment_ot = original_tweet_reg['sentimiento']
if sentiment_ot:
self.__dbm.update_record(
{
'tweet_obj.id_str':
tweet_reg['tweet_obj']['id_str']
}, {'sentimiento': sentiment_ot})
else:
raise Exception(
'Error, found an original tweet without sentiment')
else:
rts_wo_tw.append(tweet_reg['tweet_obj'])
elif tweet_reg['tweet_obj']['in_reply_to_status_id_str']:
rts_wo_tw.append(tweet_reg['tweet_obj'])
logging.info('Tweet not RT {0}'.format(
tweet_reg['tweet_obj']['id_str']))
self.__analyze_sentiment_of_rt_wo_tws(rts_wo_tw)
def __update_sentimient_rts(self, analyzed_tweets):
for analyzed_tweet in analyzed_tweets:
# search rts of the analyzed tweet
rts = self.__dbm.search(
{'tweet_obj.retweeted_status.id_str': analyzed_tweet['id']})
for rt in rts:
self.__dbm.update_record(
{'tweet_obj.id_str': rt['tweet_obj']['id_str']},
{'sentimiento': analyzed_tweet['sentimiento']})
def __analyze_sentiment_of_rt_wo_tws(self, tweets):
tot_tws = len(tweets)
batch_size = 5
tweets_to_analyze = []
for current_tw in range(tot_tws):
tweet_id = tweets[current_tw]['id_str']
if 'retweeted_status' in tweets[current_tw].keys():
tweet = tweets[current_tw]['retweeted_status']
else:
tweet = tweets[current_tw]
if 'full_text' in tweet.keys():
tweet_text = tweet['full_text']
else:
tweet_text = tweet['text']
if len(tweets_to_analyze) < batch_size and current_tw < tot_tws:
tweets_to_analyze.append({'id': tweet_id, 'text': tweet_text})
if len(tweets_to_analyze) < batch_size and current_tw < (
tot_tws - 1):
continue
sentiment_results = self.do_sentiment_analysis(tweets_to_analyze)
tweets_to_analyze = []
for sentiment_result in sentiment_results:
sentiment_info = sentiment_result['sentimiento']
tweet_id = sentiment_result['id']
tweet_text = sentiment_result['text']
self.__dbm.update_record({'tweet_obj.id_str': tweet_id},
{'sentimiento': sentiment_info})
logging.debug('Tweet text: {0}, Sentimiento: {1} ({2})'.format(
tweet_text.encode('utf-8'), sentiment_info['tono'],
sentiment_info['score']))
def analyze_sentiments(self, query={}, update_sentiment=False):
"""
:param query: dictionary of <key, value> terms to be used in querying the db
"""
if update_sentiment:
query.update({
'relevante': 1,
'tweet_obj.retweeted_status': {
'$exists': 0
}
})
else:
query.update({
'relevante': 1,
'tweet_obj.retweeted_status': {
'$exists': 0
},
'sentimiento': {
'$exists': 0
}
})
tweet_regs = self.__dbm.search(query)
analyzed_tweets = []
tot_reg = tweet_regs.count()
logging.info(
'Going to analyze the sentiment of {0} tweets, '
'it can take a lot of time, be patient...'.format(tot_reg))
batch_size = 100
total_batches = ceil(tot_reg / batch_size)
batch = 0
tweets_to_analyze = []
try:
for current_reg in range(tot_reg):
tweet_reg = tweet_regs[current_reg]
tweet = tweet_reg['tweet_obj']
if 'full_text' in tweet.keys():
tweet_text = tweet['full_text']
else:
tweet_text = tweet['text']
if len(tweets_to_analyze
) < batch_size and current_reg < tot_reg:
tweets_to_analyze.append({
'id': tweet['id_str'],
'text': tweet_text
})
if len(tweets_to_analyze) < batch_size:
continue
batch += 1
logging.info(
'Analyzing the sentiment of {0} tweets in batch {1}/{2} '
'out of {3} tweets...'.format(len(tweets_to_analyze),
batch, total_batches,
tot_reg))
sentiment_results = self.do_sentiment_analysis(
tweets_to_analyze)
logging.info(
'Finished analyzing the sentiment of {0} tweets in batch {1}/{2} '
'out of {3} tweets...'.format(len(tweets_to_analyze),
batch, total_batches,
tot_reg))
logging.info('Updating sentiment scores in database...')
tweets_to_analyze = []
for sentiment_result in sentiment_results:
sentiment_info = sentiment_result['sentimiento']
tweet_id = sentiment_result['id']
tweet_text = sentiment_result['text']
self.__dbm.update_record({'tweet_obj.id_str': tweet_id},
{'sentimiento': sentiment_info})
analyzed_tweets.append({
'id': tweet_id,
'texto': tweet_text,
'sentimiento': sentiment_info
})
logging.debug(
'Tweet text: {0}, Sentimiento: {1} ({2})'.format(
tweet_text.encode('utf-8'), sentiment_info['tono'],
sentiment_info['score']))
except Exception as e:
logging.error(e)
finally:
self.__update_sentimient_rts(analyzed_tweets)
return analyzed_tweets
def do_sentiment_analysis(self, tweets):
sa = SentimentAnalyzer(language='spanish')
tweet_texts = []
for tweet in tweets:
tweet_texts.append(tweet['text'] +
' -$%#$&- {0}'.format(tweet['id']))
sa.analyze_docs(tweet_texts)
results = sa.tagged_docs
logging.info(
'Finished the sentiment analysis, now {0} results are going to '
'be processed...'.format(len(results)))
ret = self.__process_results(results)
logging.info('Computed correctly the sentiment of {0} tweets'.format(
len(tweet_texts)))
return ret
def remote_sentiment_analysis(self, tweets):
accepted_codes = [200, 201, 202]
error_codes = [400, 401]
url_base = 'http://159.203.77.35:8080/api'
url_sentiment = url_base + '/analysis/sentiment-analysis/'
url_auth = url_base + '/auth/'
headers = {'Authorization': 'JWT ' + self.config['inhouse']['api_key']}
tweet_texts = []
for tweet in tweets:
tweet_texts.append(tweet['text'] +
' -$%#$&- {0}'.format(tweet['id']))
parameters = {
'neu_inf_lim': -0.3,
'neu_sup_lim': 0.3,
'language': 'spanish'
}
data = {
'name': (None, 'politic-bots'),
'parameters': (None, json.dumps(parameters), 'application/json'),
'data_object': (None, json.dumps(tweet_texts), 'application/json')
}
ret = []
logging.info('Computing the sentiment of {0} tweets'.format(
len(tweet_texts)))
resp = requests.post(url_sentiment, headers=headers, files=data)
if resp.status_code in error_codes:
# have to renew the api token
body_auth = {
'username': self.config['inhouse']['username'],
'password': self.config['inhouse']['password']
}
resp = requests.post(url_auth, data=body_auth)
if resp.status_code in accepted_codes:
resp_json = resp.json()
api_token = resp_json['token']
self.config['inhouse']['api_key'] = api_token
update_config(self.config_file_name, self.config)
resp = requests.post(url_sentiment,
headers=headers,
files=data)
else:
raise Exception(
'Error {0} when trying to renew the token of the api'.
format(resp.status_code))
if resp.status_code in accepted_codes:
resp_json = resp.json()
get_url = url_sentiment + str(resp_json['id']) + '/'
results = []
# wait some time before trying to get
# the results
time.sleep(60)
while len(results) == 0:
# wait some time before trying to
# get the results
time.sleep(30)
resp = requests.get(get_url, headers=headers)
if resp.status_code in accepted_codes:
resp_json = resp.json()
results = json.loads(resp_json['result'])
else:
raise Exception(
'Got an unexpected response, code: {0}'.format(
resp.status_code))
logging.info(
'Obtained the results of sentiment analysis, now the results are going to be processed...'
)
ret = self.__process_results(results)
else:
logging.error(
'Error {0} when trying to compute the sentiment of the tweets'.
format(resp.status_code))
logging.info('Computed correctly the sentiment of {0} tweets'.format(
len(tweet_texts)))
return ret
def __process_results(self, results):
ret = []
for result in results:
text, tone, score = result
if tone == 'neg':
sentiment = 'negative'
elif tone == 'pos':
sentiment = 'positive'
else:
sentiment = 'neutral'
tw_text_id = text.split('-$%#$&-')
id_tweet = tw_text_id[1].strip()
text_tweet = tw_text_id[0].strip()
dic_ret = {
'id': id_tweet,
'text': text_tweet,
'sentimiento': {
'tono': sentiment,
'score': score
}
}
ret.append(dic_ret)
return ret
class BotDetector:
__dbm_tweets = None
__dbm_users = None
__api = None
def __init__(self):
self.__dbm_tweets = DBManager('tweets')
self.__dbm_users = DBManager('users')
name_config_file = pathlib.Path(__file__).parents[1].joinpath('config.json')
conf = get_config(name_config_file)
auth = tweepy.AppAuthHandler(conf['twitter']['consumer_key'], conf['twitter']['consumer_secret'])
self.__api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True)
def __save_user_pbb(self, user_screen_name, pbb, bot_score, user_bot_features, num_heuristics,
sum_weights, exist_user):
new_fields = {
'exists': int(exist_user),
'bot_analysis': {'features': user_bot_features,
'pbb': pbb,
'raw_score': bot_score,
'num_evaluated_heuristics': num_heuristics,
'sum_weights': sum_weights}
}
self.__dbm_users.update_record({'screen_name': user_screen_name}, new_fields)
def __check_if_user_exists(self, user_screen_name):
user_obj = self.__dbm_users.search({'screen_name': user_screen_name})[0]
if 'exists' in user_obj.keys():
return int(user_obj['exists'])
else:
try:
self.__api.get_user(user_screen_name)
return True
except tweepy.TweepError:
return False
def __compute_bot_formula(self, user_bot_features, exists_user):
name_weights_file = pathlib.Path(__file__).parents[0].joinpath('heuristic_weights.json')
weights_file = get_config(name_weights_file)
sum_heuristic_values = 0
sum_weights = 0
for feature_name in user_bot_features.keys():
feature_weight = weights_file[feature_name]
feature_value = user_bot_features[feature_name]['value']
sum_heuristic_values += feature_weight * feature_value
sum_weights += feature_weight
sum_heuristic_values += weights_file['exists'] * (1-int(exists_user))
sum_weights += weights_file['exists']
return sum_heuristic_values, sum_weights, sum_heuristic_values/sum_weights
def __get_timeline(self, user_screen_name, user_tweets):
"""
Get the last 100 tweets in the timeline of a given user
:param user: user from whom her timeline should be obtained from
:return: user's timeline
"""
user_obj = self.__dbm_users.search({'screen_name': user_screen_name})[0]
if 'timeline' in user_obj.keys():
return user_obj['timeline']
logging.info('Get the last 100 tweets from Twitter')
timeline = []
try:
for status in tweepy.Cursor(self.__api.user_timeline, screen_name=user_screen_name).items(100):
timeline.append(status._json)
# save the not electoral tweets of the user's timeline
id_electoral_tweets = [tweet['id_str'] for tweet in user_tweets]
timeline_tweets_to_save = [tweet for tweet in timeline
if tweet['id_str'] not in id_electoral_tweets]
logging.info('To save {0} not electoral tweets of {1}'.format(len(timeline_tweets_to_save),
user_screen_name))
new_field = {
'timeline': timeline_tweets_to_save
}
self.__dbm_users.update_record({'screen_name': user_screen_name}, new_field)
except tweepy.TweepError:
pass
return timeline
def __get_tweets_user(self, user_screen_name):
user_tweets_obj = self.__dbm_tweets.search({'tweet_obj.user.screen_name': user_screen_name})
user_tweets = [user_tweet_obj['tweet_obj'] for user_tweet_obj in user_tweets_obj]
return user_tweets
def __get_user_info_from_twitter(self, user_screen_name):
user_twitter_obj = None
try:
user_twitter_obj = self.__api.get_user(user_screen_name)
except tweepy.TweepError:
pass
return user_twitter_obj._json
def __get_computed_heuristics(self, user_screen_name):
user_obj = self.__dbm_users.search({'screen_name': user_screen_name})[0]
if 'bot_analysis' in user_obj.keys():
return user_obj['bot_analysis']['features']
else:
return {}
def __compute_heuristics(self, user_screen_name, recompute_heuristics=False):
logging.info('\n\nComputing the probability of being bot of the user: {0}\n\n'.format(user_screen_name))
# Get tweets of the user
user_tweets = self.__get_tweets_user(user_screen_name)
# Check if the user still exists on Twitter
exist_user = self.__check_if_user_exists(user_screen_name)
user_timeline = None
if exist_user:
# If the user still exists on Twitter, get her timeline
user_timeline = self.__get_timeline(user_screen_name, user_tweets)
# Get the information about the user and her tweets
user_obj = get_user(self.__dbm_tweets, user_screen_name)
if not user_obj:
user_obj = self.__get_user_info_from_twitter(user_screen_name)
if not user_obj:
raise Exception('Error!, Cannot fetch information about the user {0}'.format(user_screen_name))
if user_obj['verified']:
# It is a verified account, it cannot be bot
logging.info('The user {0} is an account verified by Twitter, it cannot be a bot'.format(user_screen_name))
self.__save_user_pbb(user_screen_name, 0, 0, None, 0, 0, exist_user)
return
# Get the computed heuristics
user_bot_features = self.__get_computed_heuristics(user_screen_name)
if user_bot_features:
user_computed_heuristics = user_bot_features.keys()
else:
user_computed_heuristics = []
if recompute_heuristics or 'retweet_electoral' not in user_computed_heuristics:
if user_tweets:
# Compute the percentage of retweets in the electoral tweets
per_rt = is_retweet_bot(user_tweets)
user_bot_features['retweet_electoral'] = {
'value': per_rt
}
if recompute_heuristics or 'reply_electoral' not in user_computed_heuristics:
if user_tweets:
# Compute the percentage of replies in the electoral tweets
per_rp = reply_percentage(user_tweets)
user_bot_features['reply_electoral'] = {
'value': per_rp
}
if recompute_heuristics or 'retweet_timeline' not in user_computed_heuristics:
# Compute the percentage of retweets in the user's timeline
if user_timeline:
per_rt = is_retweet_bot(user_timeline)
user_bot_features['retweet_timeline'] = {
'value': per_rt
}
if recompute_heuristics or 'reply_timeline' not in user_computed_heuristics:
if user_timeline:
per_rp = reply_percentage(user_timeline)
user_bot_features['reply_timeline'] = {
'value': per_rp
}
if recompute_heuristics or 'creation_date' not in user_computed_heuristics:
# Check the user's creation year
extraction_date = self.__dbm_tweets.find_record({})['extraction_date']
electoral_year = int('20' + extraction_date.split('/')[2])
user_bot_features['creation_date'] = {
'value': creation_date(parse_date(user_obj['created_at']), electoral_year)
}
if recompute_heuristics or 'default_profile' not in user_computed_heuristics:
# Check if the user has default profile.
user_bot_features['default_profile'] = {
'value': default_profile(user_obj)
}
if recompute_heuristics or 'default_profile_picture' not in user_computed_heuristics:
# Check if the user has default profile picture
user_bot_features['default_profile_picture'] = {
'value': default_profile_picture(user_obj)
}
if recompute_heuristics or 'default_background' not in user_computed_heuristics:
# Check if the user has default background picture
user_bot_features['default_background'] = {
'value': default_background(user_obj)
}
if recompute_heuristics or 'empty_description' not in user_computed_heuristics:
# Check if the user has a biography description
user_bot_features['empty_description'] = {
'value': default_description(user_obj)
}
if recompute_heuristics or 'location' not in user_computed_heuristics:
# Check if the user has location
user_bot_features['location'] = {
'value': location(user_obj)
}
if recompute_heuristics or 'ff_ratio' not in user_computed_heuristics:
# Check the user's following followers ratio
ratio = followers_ratio(user_obj)
user_bot_features['ff_ratio'] = {
'value': ratio
}
if recompute_heuristics or 'random_letters' not in user_computed_heuristics:
rl_value = random_account_letter(user_obj)
user_bot_features['random_letters'] = {
'value': rl_value
}
if recompute_heuristics or 'random_numbers' not in user_computed_heuristics:
rn_value = random_account_number(user_obj)
user_bot_features['random_numbers'] = {
'value': rn_value
}
if recompute_heuristics or 'similar_account' not in user_computed_heuristics:
similarity_score = similar_account_name(user_obj, self.__dbm_users, self.__dbm_tweets)
user_bot_features['similar_account'] = {
'value': similarity_score
}
# Compute the user's probability of being bot
num_computed_heuristics = len(user_bot_features.keys())
bot_score, sum_weights, pbb = self.__compute_bot_formula(user_bot_features, exist_user)
self.__save_user_pbb(user_screen_name, pbb, bot_score, user_bot_features,
num_computed_heuristics, sum_weights, exist_user)
logging.info('\n\nThe bot score of {0} is {1}\n\n'.format(user_screen_name, bot_score))
return
def compute_fake_promoter_heuristic(self, users):
name_weights_file = pathlib.Path(__file__).parents[0].joinpath('heuristic_weights.json')
weights_file = get_config(name_weights_file)
if not users:
users = self.__dbm_users.search({'bot_analysis.features.fake_promoter': {'$exists': 0},
'verified': {'$ne': True}})
tot_user = users.count()
idx_user = 1
for user in users:
logging.info('Remaining users: {0}'.format(tot_user - idx_user))
user_screen_name = user['screen_name']
user_obj = self.__dbm_users.search({'screen_name': user_screen_name})[0]
user_bot_features = user_obj['bot_analysis']['features']
# Check if the user interacts with bot accounts
fp = fake_promoter(user_screen_name, self.__dbm_users)
logging.info('User: {0}, fake promoter score: {1}'.format(user_screen_name, fp))
if not user_bot_features:
user_bot_features = {}
user_bot_features['fake_promoter'] = {
'value': fp
}
bot_score = user_obj['bot_analysis']['raw_score']
bot_score += user_bot_features['fake_promoter']['value'] * weights_file['fake_promoter']
heuristics = user_obj['bot_analysis']['num_evaluated_heuristics'] + 1
sum_weights = user_obj['bot_analysis']['sum_weights'] + weights_file['fake_promoter']
pbb = bot_score/sum_weights
exist_user = user_obj['exists']
self.__save_user_pbb(user_screen_name, pbb, bot_score, user_bot_features, heuristics, sum_weights,
exist_user)
idx_user += 1
def compute_bot_probability(self, users, source_users_collection = "", source_users_db = ""):
reusers_db = None
if source_users_db and source_users_collection:
reusers_db = DBManager(source_users_collection, source_users_db)
if not users:
# Get all users who don't have the analysis of bot in current user
users = self.__dbm_users.search({'bot_analysis': {'$exists': 0}})
tot_user = len(users) if type(users) == list else users.count()
idx_user = 1
for user in users:
logging.info('Remaining users: {0}'.format(tot_user-idx_user))
if reusers_db:
reuser_cursor = reusers_db.search({'screen_name': user['screen_name']})
if reuser_cursor.count() > 0:
logging.info('Reusing bot analysis from another DB for {0}'.format(user['screen_name']))
reuser = reuser_cursor[0]
bot_analysis = reuser['bot_analysis']
self.__save_user_pbb(reuser['screen_name'], bot_analysis['pbb'], bot_analysis['raw_score'],
bot_analysis['features'], bot_analysis['num_evaluated_heuristics'],
bot_analysis['sum_weights'], reuser['exists'])
continue
if type(users) == list:
user_screen_name = user
else:
user_screen_name = user['screen_name']
self.__compute_heuristics(user_screen_name)
idx_user += 1
def to_csv(self, output_file_name, include_verified_accounts=True):
if not include_verified_accounts:
query = {'bot_analysis': {'$exists': 1}, 'verified': {'$ne': True}}
else:
query = {'bot_analysis': {'$exists': 1}}
users = self.__dbm_users.search(query)
f_name = str(pathlib.Path(__file__).parents[2].joinpath('reports',output_file_name))
logging.info('Saving bot analysis into the csv file {0}'.format(f_name))
with open(f_name, 'w', encoding='utf-8') as f:
user_info_fields = ['screen_name', 'profile_url', 'party', 'movement', 'exists', 'followers',
'friends', 'tweets', 'rts', 'rps', 'verified']
bot_analysis_fields = ['location', 'default_profile_picture', 'retweet_electoral',
'default_background', 'similar_account', 'random_numbers', 'ff_ratio',
'random_letters', 'default_profile', 'creation_date', 'empty_description',
'retweet_timeline', 'reply_electoral', 'reply_timeline', 'fake_promoter',
'raw_score', 'sum_weights', 'pbb']
writer = csv.DictWriter(f, fieldnames=user_info_fields+bot_analysis_fields)
writer.writeheader()
tot_users = users.count()
logging.info('Going to save the information of the bot analysis of {0} users'.format(tot_users))
idx_user = 1
for user in users:
logging.info('Remaining users: {0}'.format(tot_users - idx_user))
row_dict = {}
for field_name in bot_analysis_fields:
if field_name in user['bot_analysis']['features'].keys():
row_dict[field_name] = user['bot_analysis']['features'][field_name]['value']
elif field_name in user['bot_analysis'].keys():
row_dict[field_name] = user['bot_analysis'][field_name]
for field_name in user_info_fields:
if field_name == 'profile_url':
continue
row_dict[field_name] = user[field_name]
if user['exists']:
row_dict['profile_url'] = 'https://twitter.com/' + user['screen_name']
else:
row_dict['profile_url'] = ' '
writer.writerow(row_dict)
idx_user += 1
logging.info('The saving process has finished, please check the file {0}'.format(f_name))