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Social Media & Politics: Data Analysis Presentation

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Pre-Midterm
review
Digital trace data
Digital trace data
An example case:
Predicting election from social media
SAMPLING
DISTRIBUTION
• On average, � will center around, or cluster
among, its population value � :
• It forms normal distribution
• the mean of � � = �
• central limit theorem (중심극한정리)
• Variability of � � (= how close or far � � is
apart from � ) decreases as the sample size
increases
• The standard deviation (SD) of the sampling
distribution is proportional to the SD of its
population value, so as the SD of the sample
SAMPLING
DISTRIBUTION IN
PRACTICE
• You gather your sample, and
obtain “sample-specific”
estimate of � , which is � �
• This is your best-possible guess
of � (you assume � � = � )
• Using the SD of the sample, you
approximate SD of your
sampling distribution, calculate
the range of possible values of
your � given probability mass
(e.g., 95% or 99%)
( = Confidence interval)
The case of predicting election from social media
• Few conceptual and m ethodological issues:
• A m achine-learning approach: social media data predicting previous election
results (training & building prediction models) à predict new election using a
developed model using out-of-sample data
Data extraction
Preprocessing
Feature extraction
ML algorithm
training
Prediction on
previous data
Data extraction
Preprocessing
Feature extraction
ML algorithm
application
Prediction on
new data
Problem s?
Artificial Intelligence
Three different perspectives of democracy
Data “creation” in datafication
• sampling bias: when does it occur?
Data “creation” in datafication
• Psychology of survey response (assumptions)
• Examples of “bad” questionnaires
Data “creation” in datafication
• Advantages vs. disadvantages of digital trace / online data
• Capacity to collect and analyze massive amount of data
• Nonreactive measures (avoiding response bias)
Data “creation” in datafication
• Advantages vs. disadvantages of digital trace / online data
• Issues with reflexivity (ppl change their behaviors when recognized)
• Ethics and privacy
• A lack of robust model of collaboration w/ industry
The big data “mythology”
When to rely on social media data?
many
are analogues to what would be observed offline
behaviors studied using social media
Pitfalls when relying on social media data
social media
Black-box proprietary sampling algorithms used by
Similar to the critic of lab-based experiment:
• Affordances of platform may change the logic of ppl’s behavior
used by social media:
Black-box proprietary sampling algorithms
Campaigns and Big data
• Political campaigns: Deliberate, self-conscious efforts on the part of elites to
influence citizens
influence
prospective voters
• political advertising & mass media (i.e., news or debate) appearance
Advantages and disadvantages of ads
• To (almost) anyone they like, given sufficient audience attention, and to the
extend that they can afford such ads (i.e., money)
• But not necessarily mean every prospective voter is uniformly influenced by
such ads
“Free” media appearance
• Therefore, political actors have a strong incentive to “supplement” these
paid media with “free” media:
press conference, talk-show campaigning
• Disadvantage (especially in news coverage): lack of message control &
targeting
Microtargeting: Combining two sources of data
In what ways has consumer and proprietary data shaped our understanding of political attitudes and
behaviors?
In what ways has consumer and proprietary data shaped our understanding of political attitudes and
behaviors?
Increasing social and cultural differences
• Seemingly apolitical domains – such as food, artistic or cultural preferences,
consumer decisions, moral senses, etc. – can provide some “cues” about
one’s political identity (“lifestyle politics”)
• Some evidence suggests that ppl do evaluate co-partisans more favorably
than out-partisans in seemingly apolitical arena
• These increasing social and cultural differences are (quite naturally)
reflected in what people write and share in social media:
Data-driven campaigning in practice
- Data protection requirement
• - Campaign finance laws
• - Election contexts
Democratic consequences of micro-targeting
• In principle, MC could “strengthen” democracy by increasing political participation:
• (1) Micro-targeting may amplify the effects of campaigns by reaching citizens who are difficult to
reach:
• (2) Microtargeting increase the diversity of political campaigns, and voters’ knowledge
about certain issues:
• (3) Help voters to manage information overload:
Threats for democracy from micro-targeting
• In practice, this means gathering as much as data about individual voters:
data privacy issues
• More serious issue concerns with data breaches:
• mobilization by microtargeting also means suppressing voter turnout for their opponents
Differential issue focus creates biased perceptions of the parties’ priorities
• Certain groups may be ignored:
Ethics of data and knowledge production
“Emotional contagion” study
“Taste, ties, and time” data
Encore & Mark of criminal record examples
Social media and Market forces in news
production
Economic theories of news production
Economic theories of news production
Hard = high level of newsworthiness
(current affairs), demanding immediate
publication
Soft = do not need timely publication,
low substantive informational value
Consumers’ informational demand
Producers’ informational “costs”
The “contestable” News market on social media
• No Entry/Exit barriers: No formal barriers to entry or exit from the market.
• On social media, platform itself provides audience and distribution channels
• Symmetric Information/technology: There cannot be any specialized technology or
knowledge available to the incumbent firms but not the new entrants.
• On a given platform, technological affordances are the same
• No Sunken Costs: There cannot be any capital investments (in either physical or
intellectual capital) that cannot be recouped.
• Results: more “hit-and-run” type competition & declining “quality” of news in
general – “clickbait media”
Attention-maximization by algorithmic curation
users’ behaviors
quantitative audience metric
quantitative information about
The shift in journalistic routines
• Questions naturally arise about the ways in which people actually read and
engage with these pieces, beyond the feel-good tally of how many visitors they
attract.
The use of audience metric
The use of audience metric
The use of audience metric
Attitude assessment: measuring attitudes
• Direct measurement:
• Some people don’t have attitudes about topics, or don’t know their attitudes!
Psychology of attitude response (assumptions)
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