CMN 150V Final Exam – Questions With Verified
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Terms in this set (206)
Session 6 Case Studies Twitter networks, influencers, disease vs symptoms,
cost/benefit/spread in networks
Influencers and influencial people people in networks who can 'tap in' to communities
of interest
Example of influencers/influencial a famous person tweeting on behalf of a company to
people promote a product
What is the Law of Few? Influential people find out about trends and spread
the word through social connections.
How do influential people contribute They create cascades to get people on trends.
to trends according to the Law of
Few?
Two-step flow model of influence info --> media --> influencers --> public
what happens in the two-step flow influencers decide what should be consumed, then
model of influence the public bases their decisions off of the influence
Difference between influencers and influencers are consistently popular, regular people
viral but regular people randomly go viral
Twitter study of cascade tracked shorted urls that were shared and reposted
to see how links propogate through networks; the
tracked links created twitter cascade models
,Twitter cascades an information diffusion process in which a number
of people make the same decision of passing along
information in a sequential fashion
Predicting influence through twitter split data into past and future
reposts 'past' data - model - predict - compare to 'future'
data
'past' data training set
'future' data testing set
findings of twitter study number of follower and past influence predict one's
influence
what is wrong with the twitter study outcome factors are statistically significant but poor
findings fits to the model groups actually going viral meaning
the ability to go viral must be because of an outside
factor
googling symptoms study symptoms do not equal disease but disease does
equal symptoms
base-rate fallacy occurs when the likelihood of an event is inaccurately
assessed by ignoring the general frequency of the
event itself
Baye's rule P(A|B) = P(B|A)P(A)/P(B)
what does baye's rule describe the probability of an event, based on prior
knowledge of conditions that might be related to the
event
Baye's rule with influencers P (viral | influence) does not equal P (influence | viral)
Baye's rule with disease P (disease | symptoms) does not equal P (symptoms |
disease)
, Computer simulations and social create hypothetical networks, select nodes, simulate
networks a contagion process by assuming that neighboring
nodes have a fixed probability of getting infected
Density of a network determines if/how things are absorbed and spread
among people/institutions
Network Structure determines influences
example of network structure degree distributions
another word for social contagion influence
social contagion/influence how things are spread/diffuse in networks; infinite
number of ways of distribution
random networks used to find out if there is anything special about the
network or if it's just random
hypothesis testing in random networks test own networks against random ones to see if your
hypothesis holds (uses induction and glass-of-red-
wine theorizing)
random (erdos-renyi) graph G (n, p) or G (n, M)
benchmark - n nodes (form independent links)
- p probability (for each node)
- m independent links
purpose of Erdos-renyi benchmark to see what m (links) connect with what nodes
Use G (n, M) for calculate average degree of network
numerical solution for average degree degree/links = avg. degree (simulate and count)
of networks
analytical solution for average degree (n-) x p = avg. degree (mathematical derivation)
of networks
Solutions
Save
Terms in this set (206)
Session 6 Case Studies Twitter networks, influencers, disease vs symptoms,
cost/benefit/spread in networks
Influencers and influencial people people in networks who can 'tap in' to communities
of interest
Example of influencers/influencial a famous person tweeting on behalf of a company to
people promote a product
What is the Law of Few? Influential people find out about trends and spread
the word through social connections.
How do influential people contribute They create cascades to get people on trends.
to trends according to the Law of
Few?
Two-step flow model of influence info --> media --> influencers --> public
what happens in the two-step flow influencers decide what should be consumed, then
model of influence the public bases their decisions off of the influence
Difference between influencers and influencers are consistently popular, regular people
viral but regular people randomly go viral
Twitter study of cascade tracked shorted urls that were shared and reposted
to see how links propogate through networks; the
tracked links created twitter cascade models
,Twitter cascades an information diffusion process in which a number
of people make the same decision of passing along
information in a sequential fashion
Predicting influence through twitter split data into past and future
reposts 'past' data - model - predict - compare to 'future'
data
'past' data training set
'future' data testing set
findings of twitter study number of follower and past influence predict one's
influence
what is wrong with the twitter study outcome factors are statistically significant but poor
findings fits to the model groups actually going viral meaning
the ability to go viral must be because of an outside
factor
googling symptoms study symptoms do not equal disease but disease does
equal symptoms
base-rate fallacy occurs when the likelihood of an event is inaccurately
assessed by ignoring the general frequency of the
event itself
Baye's rule P(A|B) = P(B|A)P(A)/P(B)
what does baye's rule describe the probability of an event, based on prior
knowledge of conditions that might be related to the
event
Baye's rule with influencers P (viral | influence) does not equal P (influence | viral)
Baye's rule with disease P (disease | symptoms) does not equal P (symptoms |
disease)
, Computer simulations and social create hypothetical networks, select nodes, simulate
networks a contagion process by assuming that neighboring
nodes have a fixed probability of getting infected
Density of a network determines if/how things are absorbed and spread
among people/institutions
Network Structure determines influences
example of network structure degree distributions
another word for social contagion influence
social contagion/influence how things are spread/diffuse in networks; infinite
number of ways of distribution
random networks used to find out if there is anything special about the
network or if it's just random
hypothesis testing in random networks test own networks against random ones to see if your
hypothesis holds (uses induction and glass-of-red-
wine theorizing)
random (erdos-renyi) graph G (n, p) or G (n, M)
benchmark - n nodes (form independent links)
- p probability (for each node)
- m independent links
purpose of Erdos-renyi benchmark to see what m (links) connect with what nodes
Use G (n, M) for calculate average degree of network
numerical solution for average degree degree/links = avg. degree (simulate and count)
of networks
analytical solution for average degree (n-) x p = avg. degree (mathematical derivation)
of networks
