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Summary SMWA (Master Marketing Analytics
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Summary of all the lectures, labs, presentation prompts for the course Social Media & Web Analytics given in 2022
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wouterkrooshof
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Lectures, labs, presentation promptsWeek 1:
Video lectures:
Social media landscape:
Massive growth in social media use over last two decades:
- 2005: 9% of internet users aged 18-29 used social media (Aral, 2020).
- 2021: 53.6% of the world’s population uses social media
- 4.2 billion people
- Average daily usage approx. 2,5 hours.
Social network popularity over time:
- Myspace first
- YouTube started as one of the first to grow, but what really changed the game is Facebook.
Are people using social media to change their purchasing behavior?
- Answer is clearly yes:
o Across all demographics, people use social networks and what’s on social media sites
to research products which they are going to buy.
, o People using social media to discover brands/products that they wouldn’t otherwise
be aware of via ads or recommendations.
o People would increase change of purchase when their is a buy button on social
media.
Main takeaway: people’s time on social media is important for marketers to understand.
Some useful definitions:
User generated content: content that is generated or created by an internet user who is a consumer
of this information or content.
- Example: people who write movie reviews on review platform, who also consume movie
reviews on that platform.
Social media: online platforms that host user generated content
- Working definitions for our course
- People have a tendency to be use these terms relatively interchangeably
Diversity of social media platforms:
Previous definitions means many platforms fit our scope of definitions:
Including ones we might not have traditionally view as ‘social media ‘ from our day-to-day notions.
Social media landscape part 2:
Why did social media take off? What causes the massive explosion in use of social media?
Three factors:
1. Digital social networks: structured information flow around us.
2. Machine intelligence: facilitated the way that recommendations could been made and ease
of speed of powerful recommendations of who to follow or of friends and content to view
over the network.
3. Smartphones: “always on”, feeds our social brain. Were always on and available. Because
phones are everywhere we go.
These three factors came all together at the same time.
,Aral’s “hype machine”
The three factors all matter, because they are layered on top of each other.
- There was the digital social network → machine intelligence takes information of that social
network gives us a whole bunch of recommendations → feeds these to our phones.
1. Digital social networks: structures the flow of information and influence around us→ human
social network and way we are connected now comes digitally. We have the social network
and it’s observable, network structure is provide insight about preferences. Digitization of
social networks → we can now cleanly see relationships between people. So, we can use this
social network to feed the persons with the things they want to see.
2. Machine intelligence: is used to feed people with wat they want to see. What is machine
intelligence: guiding evolution of network → suggesting who wee follow friends with.
Suggesting and guiding consumer through content they might want to see. These predictions
and recommendations come at a very large scale. Because clear pattern of social network
became available machine intelligence became very useful. Machine intelligence takes more
than only your network they going to see what you are doing on social media: what you post,
what you read, who you follow takes all these things and gives you recommendations (these
people you need to follow, might wat to see this content). Earlier didn’t have the power and
inputs to generate the powerful recommendations as today.
3. Smartphones: idea always online, always on→ phone is everywhere we go. Signals come to
us 24/7. Were also inputting data by writing stuff from our smartphone. If we always putting
in data, we can continual feeding in new information that can be used to machine
intelligence. Our brains really react to what we do on social media. Our brains get dopamine
hits when we get likes/reactions.
To conclude: social media took really off due to digitization (we have now digital connection between
people), availability of machine intelligence and power of it (computational power and statistical
analysis) in combination with the smartphone.
Dark side of social media:
Rise of social media got downsides:
- Mental health effects: can makes us depressed, all people are portraying the best version of
themselves.
- Echo chambers (idea that different communities / individual people) due to self selection
into content and recommender systems → get content that is really specific to our needs.
Not getting a wide view of what is going on.
o Political polarization: Someone who votes for FVD is going to get different things to
see on Facebook comparing to some people voting PVDD. Getting more FVD content,
, suggesting more friends that also see this content so you’re getting in those
chamber.
- Privacy concerns: amount of data that platforms have access to our data.
Video 3: Why people contribute to social media?
(why are people posting on social media)?
Intrinsic vs Image related utility:
Why does a user want an audience? (Toubia & Stephan, 2013). Two possible reasons why we want to
be posting:
1. Intrinsic utility: inherent satisfaction → we just enjoy posting on social media:
If intrinsic utility is what is driving our posting behavior on social media, they argue that if for
some reason you get bunch new followers you want to post more. Why, because being
happy broadcasting to more people.
a. Prediction: exogenous increase number of followers → more posts
b. Why? Broadcasting to more people
2. Image-related utility: motivated by perceptions of others→ posting because I’m motivated
by other people perceptions. If image-related utility is what matters, you’re motivated by
perception of others. If I can throw a bunch of new followers → you get these and you’re not
going to change/decrease amount of posts online. Argument: having followers is enough →
happy for contributions and you like people having positive perceptions → more people
don’t need to attract more people, so I’m decreasing my posting behavior.
a. Prediction: exogenous increase number of followers → no change / decrease in
posting behavior
b. Why? Having followers is ‘enough’, post content to attract new followers.
RQ: Can we disentangle these components? That certain people behave in different ways?
Field experiment:
Platform: Twitter → approx. 2500 non-commercial accounts via Twitter API
1. Watch them for 52 days, to see if active
Active; more tweets, or followed more people, 1355 are active
2. Gradually add new 100 followers to 100 of the accounts (“treatment group”).
a. New followers? Synthetic accounts designed to look realistic
b. Treatment group: 100 of the 1355 accounts selected at random
3. Watch what happens: monitor whether posting intensity increases or decreases as followers
are added.
4. Analyze the data.
Main findings (model free evidence):
Finding 1: no main effect on posting activity:
- Control group 34.19% of users had greater posting rate after intervention started.
- Treatment group: 40.82% of users had greater posting rate after intervention started.
- 6 percentage point difference, but not statistically.
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