4 v’s of social media
● Volume
● Veracity - degree to which data can be trusted
● Variety
● Velocity
6 degrees of separation - any two random ppl can be connected via 6 ppl
Strength of weak ties - imp of ppl you don’t have a strong connection to
Node centrality
● Degree centrality - assigns an imp score based on no. of links held by
each node - how many direct connections a node has
● Closeness centrality - avg shortest distance from a vertex to every
other vertex - can be used to find influencers
● Betweenness centrality - how often a node occurs on all shortest
paths b/w 2 nodes - shows which nodes are bridges in the network
Homophily - same type of ppl connect w/ each other
Exponential vs power law
● Degrees of a random network follow poisson distribution - most
nodes have same no. of links
● In a network that follows power law most nodes have a few links, few
nodes have a lot of links
Local clustering coefficient - ci = no. of triangles connected to node i / no. of
triples centered around node i
Global clustering coefficient - no. of closed triplets / no. of all triplets = 3 *
no. of triangles / no. of all triplets
Phishing
● Spear - for a specific group
● Whaling - for a large group
● Vishing
Methods of data collection
● Simple search
● Api calls
● Web scraping & crawling
Streaming api - collect live data - override functions of MyStreamListener
class - runs until stopped