def user_to_depth(user_depth, user_depth2, user_depth3):
#time_depth = get_depth_time_series('True')
#time_depth2 = get_depth_time_series('False')
#time_depth3 = get_depth_time_series('Mixture,Mostly True,Mostly False')
#mean time to get to depth
print(user_depth)
x_ticks = np.arange(0, 20, 1)
x_ticks1 = user_depth.keys()
x_ticks2 = user_depth2.keys()
x_ticks3 = user_depth3.keys()
y_ticks1 = [np.mean(user_depth[depth].values()) for depth in x_ticks1]
y_ticks2 = [np.mean(user_depth2[depth].values()) for depth in x_ticks2]
y_ticks3 = [np.mean(user_depth3[depth].values()) for depth in x_ticks3]
if len(x_ticks1) > len(x_ticks2) and len(x_ticks1) > len(x_ticks2):
x_ticks = x_ticks1
elif len(x_ticks2) > len(x_ticks1) and len(x_ticks2) > len(x_ticks3):
x_ticks = x_ticks2
else:
x_ticks = x_ticks3
line = LinePlot()
line.set_ylog()
line.set_label('Depth', 'Mean Unique Users')
line.set_plot_data(y_ticks1, x_ticks)
line.set_plot_data(y_ticks2, x_ticks)
line.set_plot_data(y_ticks3, x_ticks)
line.set_legends(['True', 'False', 'Mixed'])
line.set_xticks(x_ticks)
line.save_image('Image/user_depth_line.png')
def draw_5_2_1_figures():
with open('Data/Figure/5_2_1.json', 'r') as f:
data = json.load(f)
#print(data)
#draw_time_to_depth_echo_chamber([echo_chamber_values['time_depth'], non_echo_chamber_values['time_depth']], ['echo chamber', 'no echo chamber'], 'median minutes', 'time_depth_echo_chamber_line')
#x_ticks = np.arange(1,18)
x_ticks = range(1, 18)
line = LinePlot()
line.set_ylog()
line.set_label('Depth', 'Depth Increment Time')
xtick_labels = []
print('sadfasdfasdfasdf')
print(len(data))
x_tickslabel = range(0, 17)
x_tickslabel.append('')
for item in data:
#yticks = [np.mean(item[depth]) for depth in x_ticks]
yticks = [np.median(item[str(depth)]) for depth in x_ticks]
#u_ticks1 = [np.mean(outlier.remove_outlier(item[depth])) for depth in x_ticks]
print(yticks)
line.set_plot_data(yticks, x_tickslabel)
print(x_ticks)
#print(x_ticks[0]['time_depth'])
#print(x_ticks[1]['time_depth'])
line.set_legends(['Echo chamber', 'Non-echo chamber'])
line.set_xticks(x_tickslabel)
line.set_yticks(['0', '1 m', '5 m', '1 h', '1 day', '10 day'],
index=[0, 1, 5, 60, 24 * 60, 24 * 10 * 60])
line.save_image('Image/Figure/5_2_1.png')
print(xtick_labels)
def draw_time_to_depth_echo_chamber(data, legend, data_type, filename):
x_ticks = np.arange(1,20)
line = LinePlot()
line.set_ylog()
line.set_label('Depth', data_type)
for item in data:
#yticks = [np.mean(item[depth]) for depth in x_ticks]
yticks = [np.median(item[depth]) for depth in x_ticks]
#u_ticks1 = [np.mean(outlier.remove_outlier(item[depth])) for depth in x_ticks]
line.set_plot_data(yticks, x_ticks)
line.set_legends(legend)
line.set_xticks(x_ticks)
line.save_image('%s/%s.png'%(foldername, filename))
def time_to_depth():
#index = filename.replace(".json", "").split('echo_chamber')
#print(index)
_, _, time_depth_cascade, user_ids, cascade_depth_users = get_depth_time_series('True')
_, _, time_depth_cascade2, user_ids2, cascade_depth_users2 = get_depth_time_series('False')
_, _, time_depth_cascade3, user_ids3, cascade_depth_users3 = get_depth_time_series('Mixture,Mostly True,Mostly False')
t = {}; e = {}; u_all = {}
#true, false, mixture time to depth
t_td = {}; f_td = {}; m_td = {};
#true, false, mixture user to depth
t_ud = {}; f_ud = {}; m_ud = {};
for i in range(1,20):
t[i] = []
e[i] = []
u_all[i] = []
t_td[i] = []
f_td[i] = []
m_td[i] = []
t_ud[i] = []
f_ud[i] = []
m_ud[i] = []
for key in time_depth_cascade.keys():
for i in range(1, max(time_depth_cascade[key].keys())):
t[i].append(time_depth_cascade[key][i]) # 1 ~ max_depth
t_td[i].append(time_depth_cascade[key][i]) # 1 ~ max_depth
try:
u_all[i].append(cascade_depth_users[key][i])
t_ud[i].append(cascade_depth_users[key][i])
except KeyError :
pass
for key in time_depth_cascade2.keys():
for i in range(1, max(time_depth_cascade2[key].keys())):
f_td[i].append(time_depth_cascade2[key][i]) # 1 ~ max_depth
try:
f_ud[i].append(cascade_depth_users2[key][i])
except KeyError :
pass
for key in time_depth_cascade3.keys():
for i in range(1, max(time_depth_cascade3[key].keys())):
m_td[i].append(time_depth_cascade3[key][i]) # 1 ~ max_depth
try:
m_ud[i].append(cascade_depth_users3[key][i])
except KeyError :
pass
x_ticks = np.arange(1,20)
"""
depth_list = []
veracity_list = []
time_list = []
for depth in x_ticks:
for value in t_td[depth]:
depth_list.append(depth)
time_list.append(value)
veracity_list.append('True')
for value in f_td[depth]:
depth_list.append(depth)
time_list.append(value)
veracity_list.append('False')
for value in m_td[depth]:
depth_list.append(depth)
time_list.append(value)
veracity_list.append('Mixed')
df = pd.DataFrame({'time':time_list, 'depth':depth_list, 'type':veracity_list})
line = LinePlot()
line.set_sns_plot(df)
"""
y_ticks1 = [np.median(t_td[depth]) for depth in x_ticks]
y_ticks2 = [np.median(f_td[depth]) for depth in x_ticks]
y_ticks3 = [np.median(m_td[depth]) for depth in x_ticks]
print(y_ticks1)
print(y_ticks2)
print(y_ticks3)
line = LinePlot()
line.set_ylog()
line.set_label('Depth', 'Median Minutes')
line.set_plot_data(y_ticks1, x_ticks)
line.set_plot_data(y_ticks2, x_ticks)
line.set_plot_data(y_ticks3, x_ticks)
line.set_legends(['True', 'False', 'Mixed'])
line.set_xticks(x_ticks)
line.save_image('%s/time_depth_line_echo_chamber.png'%(foldername))
#number of users to depth
u_ticks1 = [np.mean(t_ud[depth]) for depth in x_ticks]
u_ticks2 = [np.mean(f_ud[depth]) for depth in x_ticks]
u_ticks3 = [np.mean(m_ud[depth]) for depth in x_ticks]
print(u_ticks1)
line = LinePlot()
line.set_ylog()
line.set_label('Depth', 'Mean Unique Users')
line.set_plot_data(u_ticks1, x_ticks)
line.set_plot_data(u_ticks2, x_ticks)
line.set_plot_data(u_ticks3, x_ticks)
line.set_legends(['True', 'False', 'Mixed'])
line.set_xticks(x_ticks)
line.save_image('%s/user_depth_line_echo_chamber.png'%(foldername))
def draw_graph():
depth_time1, depth_user1, unique_user_time1, cascade_depth1 = time_series('True')
x_ticks1 = depth_time1.keys()
y_ticks1 = [np.mean(depth_time1[depth].values()) for depth in x_ticks1]
depth_time2, depth_user2, unique_user_time2, cascade_depth2 = time_series('False')
x_ticks2 = depth_time2.keys()
y_ticks2 = [np.mean(depth_time2[depth].values()) for depth in x_ticks1]
#draw mean minutes - depth line plot
line = LinePlot()
line.set_ylog()
line.set_label('Depth', 'Mean Minutes')
line.set_plot_data([y_ticks1, y_ticks2], x_ticks1)
line.set_legends(['True', 'False'])
line.save_image('Image/time_depth_line.png')
x_ticks1 = unique_user_time1.keys()
x_ticks2 = unique_user_time2.keys()
x_ticks1 = sorted(x_ticks1)
y_ticks1 = [np.mean(unique_user_time1[num].values()) for num in x_ticks1]
y_ticks2 = [np.mean(unique_user_time2[num].values()) for num in x_ticks2]
#draw mean minutes - unique users line plot
line = LinePlot()
line.set_ylog()
line.set_label('Unique Users', 'Mean Minutes')
line.set_plot_data([y_ticks1, y_ticks2], x_ticks1)
line.set_xticks(x_ticks1)
line.set_legends(['True', 'False'])
line.save_image('Image/time_users_line.png')
all_depth_true = [[key] * len(depth_time1[key]) for key in depth_time1.keys()] #True
all_depth_false = [[key] * len(depth_time2[key]) for key in depth_time2.keys()] #True
all_depth_sum_true = []
all_depth_sum_false = []
for item in all_depth_true:
all_depth_sum_true.extend(item)
for item in all_depth_false:
all_depth_sum_false.extend(item)
#Depth CDF, CCDF
#cdf = CDFPlot()
#cdf.set_data(all_depth_sum_true, 'True')
#cdf.set_data(all_depth_sum_false, 'False')
#cdf.set_legends(['True', 'False'], '')
#cdf.save_image('Image/depth_cdf.png')
true_cascade = []
false_cascade = []
for postid in cascade_depth1.keys():
for depth in cascade_depth1[postid].values(): #origin tweet : depth
true_cascade.extend(depth)
for postid in cascade_depth2.keys():
for depth in cascade_depth2[postid].values(): #origin tweet : depth
false_cascade.extend(depth)
print('true')
for i in range(1, 15):
print(i, true_cascade.count(i))
print('false')
for i in range(1, 15):
print(i, false_cascade.count(i))
cdf = CDFPlot()
cdf.set_legends(['True', 'False'], '')
cdf.set_xlim(0, 11)
#cdf.set_log(True)
#cdf.set_ylog()
cdf.set_label('Depth', 'CDF')
cdf.set_data(true_cascade, 'True')
cdf.set_data(false_cascade, 'False')
cdf.save_image('Image/depth_cdf.png')
category_count = []
for c in category_all:
count_list = []
for i in range(2011, 2018):
count = category_count_by_year(i, c)
count_list.append(count)
category_count.append(count_list)
#print(category_count)
df = pd.DataFrame(category_count, index = category_all, columns = range(2011, 2018))
#print(df)
LinePlt = LinePlot()
LinePlt.set_label('year', 'number of articles')
LinePlt.set_plot_data(category_count, 'category count')
LinePlt.set_xticks(range(2011, 2018))
LinePlt.set_legends(category_all)
LinePlt.save_image('./image/category_count_year.png')
print("top key words by category")
writer = pd.ExcelWriter('./trending_words/trending_keywords_category.xlsx', engine='xlsxwriter')
dataframe_list = []
category_data = []
for item in categories:
words = frequency(titles_category(item))
category_data.append(tuple_to_string(words))
df = pd.DataFrame(category_data, index = categories, columns = range(1,11))
dataframe_list.append(df)
#df.to_csv('./trending_words/%s.csv'%i, encoding='utf-8')
for i, item in enumerate(dataframe_list):
item.to_excel(writer, sheet_name=years[i])
def edge_homogeneity():
files = os.listdir(dir_name)
retweet_cache = {}
homogeneity = []
for ccc, postid in enumerate(files):
#users_polarity[postid] = {}
with open(dir_name + '%s'%postid, 'r') as f:
tweets = json.load(f)
retweet_cache[postid] = tweets
for tweet in tweets.values():
p_score = get_polarity(tweet['user'])
#calculate edge homogeneity
if tweet['depth'] != 1:
#compare with parents if parent is not root node
p_score2 = get_polarity(tweet['parent'])
if p_score == -999 or p_score2 == -999:
continue
e = p_score * p_score2
#print(p_score, p_score2, round(e, 1))
homogeneity.append(round(e, 1))
# if ccc == 10:
# break
#compare with echo chamber node's edge homogeneity
echo_chamber_users = {}
e_homogeneity = []
ne_homogeneity = []
with open('Data/echo_chamber2.json') as f:
echo_chamber = json.load(f)
for key in echo_chamber:
users = echo_chamber[key]
if len(users) < 1:
continue
for postid in key.split('_'):
echo_chamber_users[postid] = echo_chamber_users.get(postid, {})
for user in users:
echo_chamber_users[postid][user] = 1
for postid in echo_chamber_users.keys():
tweets = retweet_cache[postid]
for tweet in tweets.values():
#echo chamber user's edge homogeneity
if tweet['depth'] != 1:
p_score = get_polarity(tweet['user'])
p_score2 = get_polarity(tweet['parent'])
if p_score == -999 or p_score2 == -999:
continue
e = p_score * p_score2
#print(p_score, p_score2, round(e, 1))
if tweet['user'] in echo_chamber_users[postid].keys():
e_homogeneity.append(e)
#e_homogeneity.append(round(e, 1))
else:
ne_homogeneity.append(e)
#ne_homogeneity.append(round(e, 1))
draw_cdf_plot([e_homogeneity, ne_homogeneity], 'Homogenety', ['Echo Chambers', 'Non-Echo Chambers'], 'User type', 'homogeneity')
with open('Data/homogeneity.json', 'w') as f:
json.dump({'e':e_homogeneity, 'ne' : ne_homogeneity}, f)
x_ticks = np.arange(-1,1.1, 0.1)
x_ticks = np.around(x_ticks, decimals=1)
e_count = []
ne_count = []
for x in x_ticks:
e_count.append(e_homogeneity.count(x))
ne_count.append(ne_homogeneity.count(x))
line = LinePlot()
line.set_ylog()
line.set_label('Homogeneity', 'Number of Homogeneity')
line.set_plot_data(e_count, x_ticks)
line.set_plot_data(ne_count, x_ticks)
line.set_legends(['Echo Chambers', 'Non-Echo Chambers'])
line.set_xticks(x_ticks)
line.save_image('Image/%s/homogeneity_line.png'%foldername)