CMN 152V EXAM PREP | MOST OF THE EXAM
QUESTIONS WILL COME FROM HERE | QUESTIONS
AND ANSWERS | 2026 UPDATE | WITH COMPLETE
SOLUTION
1. Wait! How much do some top celebrities get paid per tweet?
Answer> US$ 10,000 or more
2. What does the "two-step flow model" say? Information flows
Answer>from the media, to influencers, to the public
3. What are the two complementary aspects of Computational Social Science Prof. Lamberson from UCLA
will talk about?
,Answer> empirical work + computer simulations
4. The only truly influential people in society are celebrities and famous people, like Justin Bieber, Barack
Obama, and Kim Kardashian.
Answer> False
5. What trick did Prob. Lamberson use in order to track who re-posted a URL-link to a story on Twitter?
Answer> He tracked short URL, which are unique to the post
6. What are 'Twitter cascades'?
Answer> An information dittusion process on Titter in which a number of people make the same decision of passing along information in a
sequential fashion
7. What is the first thing researchers found when looking at the empirical evidence about Tweets that go viral?:
Answer> The vast majority of posts never get retweeted, but a small fraction of links go viral
8. Does this sound familiar? In a data science framework, what are the first and second part of the data
refer to?
Answer> Trainingset& testset
9. If it turned out that one of the top-25 most retweeted URLs came from an account that had
little influence in the past, and has a small number of followers, where would it be located in this
graph?
Answer> Bottom left
,10. If it turned out that one of the top-25 most retweeted URLs came from an account that had MID-
LEVEL influence in the past, and has a small number of followers, where would it be located in this graph?
Answer> Mid-bottom
11. Let's assume that if you have this disease, you will surely have these symp- toms. Now, 1 in 6 million
people have this disease. Let's assume that 1/3 of the population has these symptoms. What's your
chance of having the disease, given that you have the symptoms?
Answer> 1 in 2 million
12. Sticking to our previous example, we said that the probability p(disease | given
the | symptoms) was 1/2,000,000 = 0.00005 %
What had we assumed is the probability p(symptoms | given the | disease)?-
Answer> 100 %
13. If something went viral, looking at it, there is a high probability that it has been sent by an
influencer: p(influencer | viral)
, What about p(viral | influencer), the probability that something goes viral, given that it was sent by an
influencer?
Answer> Low probability
14. The proposed model consists of the following:
Answer> Take a hypothetical network, select some nodes, and simulate a contagion process by assuming that neighboring nodes have a
fixed probability of getting infected
15. The horizontal x-axis presents:
Answer> If a given network (of the simulated networks) has few or many links
16. What is the lesson learned here? Whether someone is influential depends on:
Answer> the general structure of the network
17. Even tough we cannot predict who is influential, why is it still worthwhile for companies to pay
large amounts of money to celebrities to send out mes- sages?
Answer>: Because chances are that among large number of people reached by them, some turn out to be influential, who will then influence others
18. What is the difference between this lecture on Social Network Analysis, and the previous lecture on the
same topic?
Answer>: Today we will look into dynamically evolving networks + start to simulate theoretical networks
QUESTIONS WILL COME FROM HERE | QUESTIONS
AND ANSWERS | 2026 UPDATE | WITH COMPLETE
SOLUTION
1. Wait! How much do some top celebrities get paid per tweet?
Answer> US$ 10,000 or more
2. What does the "two-step flow model" say? Information flows
Answer>from the media, to influencers, to the public
3. What are the two complementary aspects of Computational Social Science Prof. Lamberson from UCLA
will talk about?
,Answer> empirical work + computer simulations
4. The only truly influential people in society are celebrities and famous people, like Justin Bieber, Barack
Obama, and Kim Kardashian.
Answer> False
5. What trick did Prob. Lamberson use in order to track who re-posted a URL-link to a story on Twitter?
Answer> He tracked short URL, which are unique to the post
6. What are 'Twitter cascades'?
Answer> An information dittusion process on Titter in which a number of people make the same decision of passing along information in a
sequential fashion
7. What is the first thing researchers found when looking at the empirical evidence about Tweets that go viral?:
Answer> The vast majority of posts never get retweeted, but a small fraction of links go viral
8. Does this sound familiar? In a data science framework, what are the first and second part of the data
refer to?
Answer> Trainingset& testset
9. If it turned out that one of the top-25 most retweeted URLs came from an account that had
little influence in the past, and has a small number of followers, where would it be located in this
graph?
Answer> Bottom left
,10. If it turned out that one of the top-25 most retweeted URLs came from an account that had MID-
LEVEL influence in the past, and has a small number of followers, where would it be located in this graph?
Answer> Mid-bottom
11. Let's assume that if you have this disease, you will surely have these symp- toms. Now, 1 in 6 million
people have this disease. Let's assume that 1/3 of the population has these symptoms. What's your
chance of having the disease, given that you have the symptoms?
Answer> 1 in 2 million
12. Sticking to our previous example, we said that the probability p(disease | given
the | symptoms) was 1/2,000,000 = 0.00005 %
What had we assumed is the probability p(symptoms | given the | disease)?-
Answer> 100 %
13. If something went viral, looking at it, there is a high probability that it has been sent by an
influencer: p(influencer | viral)
, What about p(viral | influencer), the probability that something goes viral, given that it was sent by an
influencer?
Answer> Low probability
14. The proposed model consists of the following:
Answer> Take a hypothetical network, select some nodes, and simulate a contagion process by assuming that neighboring nodes have a
fixed probability of getting infected
15. The horizontal x-axis presents:
Answer> If a given network (of the simulated networks) has few or many links
16. What is the lesson learned here? Whether someone is influential depends on:
Answer> the general structure of the network
17. Even tough we cannot predict who is influential, why is it still worthwhile for companies to pay
large amounts of money to celebrities to send out mes- sages?
Answer>: Because chances are that among large number of people reached by them, some turn out to be influential, who will then influence others
18. What is the difference between this lecture on Social Network Analysis, and the previous lecture on the
same topic?
Answer>: Today we will look into dynamically evolving networks + start to simulate theoretical networks
