The Cambridge Analytica Scandal: Lessons for Government, Business, Consumers and Voters - Ryerson University

The Cambridge Analytica Scandal: Lessons
for Government, Business, Consumers and
Voters

Professor Colin J. Bennett
Department of Political Science
University of Victoria
British Columbia, Canada
www.colinbennett.ca
cjb@uvic.ca

Presentation to the Ryerson University Institute for the Study of
Corporate Social Responsibility / PPOCIR,
December 7, 2018
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OUTLINE

Ø Big Data in North American elections
Ø Micro-targeting
Ø Facebook and access to the social network
Ø Mobile campaigning
Ø Does micro-targeting win elections?
Ø What’s wrong with data-driven elections?
Ø What can we do in Canada?

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I. BIG DATA IN NORTH AMERICAN ELECTIONS

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The Modern “Campaign Ecosystem”
• Complex campaign “ecosystem” in North America involves the
  coordination of:

   •   Data collection
   •   Data analytics
   •   Polling
   •   Fund-raising
   •   Digital advertising
   •   TV advertising
   •   Email and text outreach
   •   Social media outreach
   •   Event management
   •   Volunteer coordination
   •   Get-Out-the-Vote operations

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Sources of Data on the US Electorate
• Basic household data from state electoral registers:
   – Name, address, date-of-birth, Phone, Gender, Social Security No.,
     Party affiliation, Voter history
• Donations data (available through Federal Election Commission
  and some NGOs)
• Census data
• Direct voter contact information (telephone, door-to-door, e-mail)
• Data from social media (e.g. followers and friends)
• Consumer lists from commercial data brokers
• Data from petitions
• Data from website visits
• All linked through ubiquitous personal identifiers (name, address,
  telephone nos. email, IP address, cookies, mobile device IDs)

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Commercial Voter Analytics: Catalist.US

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CAMBRIDGE ANALYTICA – ALSO WORKED FOR LEAVE CAMPAIGN
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Psychographic Profiling

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II. MICRO-TARGETING

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Modeling and Micro-targeting
Ø Statistical models built on individual
  voter files using proprietary algorithms
  to determine
   Ø Who to contact?
   Ø How?
   Ø When?
   Ø And what to say

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BIG DATA OR LITTLE DATA?

““Big data” is a buzzword, but that concept is outdated. Campaigns
have entered the era of “little data.” Huge data sets are often less
helpful in understanding an electorate than one or two key data
points — for instance, what issue is most important to a particular
undecided voter….. With “little data,” campaigns can have direct,
highly personalized conversations with voters both on- and offline,
like an ad on a voter’s Facebook page addressing an issue the voter is
passionate about.”

Jim Messina, Obama’s Campaign Manager,
New York Times, November 3, 2016
(Consultant to Tories, 2017)

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NGP Van, The “Unified View” of the Voter from http://next.ngpvan.com   18

III. SOCIAL MEDIA, POLITICAL INFLUENCE AND
             THE SOCIAL GRAPH

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Facebook and Cambridge Analytica
• Up to 87 Million Facebook profiles harvested through
  personality test app developed by Aleksandr Kogan
• Research suggests Facebook “Likes” can be used to
  predict personality, political persuasion, age, gender,
  even sexual orientation
• Combined “psychographic” and demographic data
• Same policy message could be delivered in thousands
  of different ways depending on psychological profile
• Through AI and machine learning, extensive use of
  automated ‘bots’ and Facebook ’dark posts’

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Social Media and “Targeted Sharing”

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Facebook and Russian Influence

Ø Russian use of advertiser tools on Facebook,
  Instagram, Twitter, Pinterest and Youtube
Ø Promoted Trump, denigrated Clinton and
  sought to divide Americans on sensitive
  social issues
Ø Many purchased by Russian troll farm based
  in St. Petersburg though fake accounts
Ø Reached up to 126 million Americans
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IV. THE MOBILE ELECTION CAMPAIGN

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Integration of mobile apps….

•   For political messaging
•   For “canvassing”
•   For event management
•   For donating
•   For civic engagement

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The Data-Driven Elections in in North America
Ø Massive Accumulation and Consolidation of Personal
  data on political affiliation in integrated Voter
  Relationship Management (VRM) Platforms
Ø Close alliances between political data brokers, digital
  advertising firms, data management and analytical
  companies and political parties
Ø Massive collection and aggregation of user-generated
  data from social media
Ø Decentralization of data to the doorstep
Ø Mass Messaging (broadcasting) to Micro-Targeting
  (narrow-casting), especially through Facebook
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V. DOES MICRO-TARGETING WIN ELECTIONS?

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What does research say?

• Can make small but critical differences in marginal
  constituencies/districts
• Can suppress the opposition vote for key
  demographic groups
• The power of “organic” peer-to-peer campaigning
  -- people more likely to be persuaded by their
  peers than by campaigns and candidates
• Allows for the communication according to the
  most appropriate medium -- whether high-tech or
  low-tech.
• Allows ability to press the “wedge issues” with
  select group of voters – a potentially more divisive
  politics

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Voter Suppression?
“We have 3 major vote suppression operations
underway” (senior official of Trump campaign)
• Idealistic White Liberals (Sanders supporters
  opposed to trade deals)
• Young women (rolling out the Clinton accusers)
• African Americans
  – ”Hillary thinks African Americans are Super-
    Predators” an animation delivered through
    Facebook “dark posts”

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But much scepticism…
• Up-to-date response data from voters themselves is far more
  important than commercial data.
• Commercial data is at best best additive. “The icing on the cake,
  but you still need the cake”
• Modeling is different from micro-targeting: effects will only be as
  good as the assumptions that drive the algorithms
• Data has to be ‘seen’ through the eyes of campaign workers (the
  “perceived voter”) and the local campaigns vary widely
• The effective message must account for content, audience,
  timing and means -- a complex set of variables (what, who, when
  and how)
• It just contributes to the social media ‘echo-chamber’ and
  ‘confirmation bias’?
• Micro-targeting cannot account for the impact of the
  “movement” politician (e.g. Trump)
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VI. WHAT’S WRONG WITH DATA-DRIVEN
            ELECTIONS?

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The Critique of Data-driven elections
• It treats voters like “consumers”
• It fragments the electorate (‘slicing and dicing’).
  Where is the mandate to govern when candidates
  offer contradictory and fragmented messages?
• If voters know that their political views will be
  captured and profiled, will they be less willing to
  participate in elections?
• Is there a bias in favor of larger and richer parties?
• Can it encourage patron-client politics?
• Does voter surveillance enhance the ‘surveillance
  state’?

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Recent reports from the UK Information
            Commissioner

Conclusions: So what steps are needed in Canada?

•   Submit political parties to our privacy legislation – only parties in BC are currently
    covered
•   Impose greater rules for transparency with online ads:
     – The identification of who paid for the ad, including verifying the authenticity of the
        person running the ad
     – The identification of the target audience, and why the target audience received the
        ad; and
     – Mandatory registration regarding political advertising outside of Canada.
•   Bring Canadian privacy legislation into line with new global rules for personal data
    protection
•   Strengthen the powers of the Privacy Commissioner of Canada

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Questions?

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