Analyzing Social Media Data in Python
Alex Hanna
Computational Social Scientist
BethMohn ChristianMohn
ASilNY LarrySchweikart
mattg444 WhiteHouse
hlthiskrieger aravosis
Herky86 SenJeffMerkley
PatrickParsons9 TwitterGov
New_Narrative CFR_org
dddlor roywoodjr
scrivener50 michaelscherer
ChiefsHeadCoach johnpavlovitz
import networkx as nx
## ... flatten and convert JSON
G_rt = nx.from_pandas_edgelist( tweets, source = 'user-screen_name', target = 'retweeted_status-user-screen_name', create_using = nx.DiGraph())
import networkx as nx ## ... flatten and convert JSON
G_quote = nx.from_pandas_edgelist( tweets, source = 'user-screen_name', target = 'quoted_status-user-screen_name', create_using = nx.DiGraph())
import networkx as nx
## ... flatten and convert JSON
G_reply = nx.from_pandas_edgelist(
tweets,
source = 'user-screen_name',
target = 'in_reply_to_screen_name'
create_using = nx.DiGraph())
nx.draw_networkx(T)
plt.axis('off')
sizes =
[x[1]*100 for x in T.degree()]
nx.draw_networkx(T,
node_size = sizes,
with_labels = False,
alpha = 0.6,
width = 0.3)
plt.axis('off')
circle_pos =
nx.circular_layout(T)
nx.draw_networkx(T,
pos = circle_pos,
node_size = sizes,
with_labels = False,
alpha = 0.6,
width = 0.3)
plt.axis('off')
Analyzing Social Media Data in Python