In this Notebook, the timeline of each account is being analyzed:
The Function analyzeTimeline takes as input the BibTwitter file for each library group and the timestamp of the timeline.json-files. The function returns an csv file for each library group (NatBib_timeLineStats_[datestamp].csv).
The file contains one observation for each tweet with information in these categories:
The file will be extended in Notebook 7.2 Communication with the category
In [1]:
#from collections import Counter
#import json
def readJSON(filename):
import json
with open(filename) as f:
data = json.load(f)
return data
import csv
def impCSV(input_file):
'''
input_file = csv with keys: "URL", "Twitter"
output = list of dictionaries
'''
f = open(input_file, 'r')
d = csv.DictReader(f)
LoD = [] # list of dictionaries
for row in d:
LoD.append(row)
f.close()
return LoD
def exp2CSV(listOfDict, filename):
'''
arguments = list of dictionaries, filename
output = saves file to cwd (current working directory)
'''
#creating the filename of the csv with current datestamp
import datetime
datestamp = datetime.datetime.now().strftime('%Y-%m-%d')
outputfile = filename + '_' + datestamp + '.csv'
keyz = listOfDict[0].keys()
f = open(outputfile,'w')
dict_writer = csv.DictWriter(f,keyz)
dict_writer.writer.writerow(keyz)
dict_writer.writerows(listOfDict)
f.close()
In [2]:
def analyzeTimeline(bibTwitterfile,timeStamp):
'''
input: filename of NatBibTwitter.csv, OeBibTwitter.csv or UniBibTwitter.csv and
Timestamp = Timestamp of the timeline files
output: saves a LoD as a csv of the timeline stats
'''
# open UniBibTwitter.csv
f = impCSV(bibTwitterfile)
l = [] # list of screen_names
LoD = [] # List of Dictionary for entire library group
for i in f: # create list of screen_names/twitter handles
l.append(i['Twitter'])
for handle in l:
lod = [] # list of dictionary for each library
filename = handle + '_timeline_' + timeStamp + '.json'
tl = readJSON(filename)
for e in tl:
d = {}
# 1. get date
#
t = e['created_at']
t0 = t.find(' ') # separates Weekday from Month
t1 = t.find(' ', t0+1) # separates Month from day
t2 = t.find(' ', t1+1) # separates day from time
t3 = t.find(':', t2+1) # separates hour from minute
weekday = t[:t0]
wd = {'Mon':1, 'Tue':2, 'Wed':3, 'Thu':4, 'Fri':5, 'Sat': 6, 'Sun':7}
if weekday in wd:
weekday = wd[weekday]
month_day = t[t0+1:t1]
mon = {'Jan':1, 'Feb':2, 'Mar':3, 'Apr':4, 'May':5, 'Jun': 6, 'Jul':7, 'Aug':8, 'Sep':9,
'Oct':10, 'Nov':11, 'Dec':12}
if month_day in mon:
month_day = mon[month_day]
hour = t[t2+1:t3]
# 2. RT?
#
if 'RT' in e['text'] or 'MT' in e['text']: # just filter out RT and MT, keep 'via @' though, since these tweets often are considerably altered
rt = 0 # if it's a genuine tweet of the library: 1, else: 0
rt_count = 'NA' # don't count the favs & RTs of tweets the library retweeted!
fav_count = 'NA'
resFac = 'NA' # don't count the tweet content analysis
hashtag = 'NA'
mention = 'NA'
URL = 'NA'
media = 'NA'
else:
rt = 1
# 3. get retweet- & favorite count & calculate resonance factor
#
if type(e['retweet_count']) == int: # "Number of times this Tweet has been retweeted."
rt_count = e['retweet_count'] # "This field is __no longer__ capped at 99 and will not turn into a String for '100+'"
elif e['retweet_count'] == '100+':
rt_count = 100
else:
rt_count = 0
fav_count = e['favorite_count'] # "Indicates approximately how many times this Tweet has been "favorited" by Twitter users."
resFac = rt_count * 1.0 + fav_count * 0.5 # calculating the resonance factor (1.0 for RT, 0.5 for Favs)
# 4. Content Analysis
#
if e['entities']['hashtags'] != []:
hashtag = 1
else:
hashtag = 0
if e['entities']['user_mentions'] != []:
mention = 1
else:
mention = 0
if e['entities']['urls'] != []:
URL = 1
else:
URL = 0
try:
m = e['entities']['media']
media = 1
except:
media = 0
# 5. Auto tweet?
#
autoTweetSource = ['twitterfeed', 'wp.com', 'wp-to-twitter', 'tumblr',
'instagram', 'blogs.', 'SharePress', 'facebook', 'studivz', 'paper.li']
for v in autoTweetSource:
if v in e['source']:
at = 1
break
else:
at = 0
# 6. Communiacation Analysis
# is later imported from the ArchiveStats-Files
# 7. fill dictionary
#
d = {'screen_name': handle, 'id_str': e['id_str'], 'created_at': t, 'tweet_weekday': weekday, 'tweet_month': month_day,
'tweet_time': hour, 'genuine_tweet': rt, 'auto_tweet': at, 'rt_count': rt_count, 'favorite_count': fav_count,
'resonance_Factor': resFac, 'has_hashtag': hashtag, 'has_mention': mention, 'has_url': URL, 'has_media': media}
lod.append(d)
LoD += lod
outputfilename = bibTwitterfile[:-11] + '_timelineStats'
exp2CSV(LoD, outputfilename)
return LoD
In [7]:
nattl = analyzeTimeline('NatBibTwitter2.csv', '2014-04-07')
print len(nattl)
In [4]:
ubtl = analyzeTimeline('UniBibTwitter2.csv', '2014-04-07')
print len(ubtl)
In [5]:
oebtl = analyzeTimeline('OeBibTwitter2.csv', '2014-04-07')
print len(oebtl)
In [ ]:
#
#control functions to check from which sources a tweet was sent
#
#extracting the sources of the tweets
def tweetSource(timeline):
tweet_source = [sources['source']
for sources in timeline] # List of all sources
countedSources = Counter(tweet_source) #type = class: Counter({u'xxx': 12, u'yyy': 10, ...}) descending ordered
countedSources = countedSources.most_common()
#Printing in a table
# Code adapted from MTSW 2. Ed.
from prettytable import PrettyTable
ptLang = PrettyTable(field_names=['Source', 'Count'])
[ptLang.add_row(kv) for kv in countedSources]
ptLang.align['Source'], ptLang.align['Count'] = 'l', 'r'
print str(len(tweet_source)) + ' Tweets were analyzed.'
print ptLang
def printTweetFromSource(timeline, source):
'''
input: timeline and a word from the source under consideration (e.g. 'wp.com,' or 'tweetfeed')
output: prints out the index of the tweet in the timeline and the text of the tweet
'''
for e in range(len(timeline)):
if source in timeline[e]['source']:
print e, timeline[e]['text']
print
# getting the timeline of an account:
timeline = readJSON(json-filename) # <===== enter the json filename
tweetSource(timeline)
printTweetFromSource(timeline, source)