Invited Talk at the Natural Language Processing for Internet Freedom (NLP4IF) workshop at EMNLP 2019 in Hong Kong. Talk addresses how to use human perception measurements (large scale survey methodology) to identify and defend against propaganda and fake news on social media, toward protecting democratic elections. Elissa Redmiles, Princeton University & Microsoft Research

1. Measuring Human Perception
to Defend Democracy
Elissa M. Redmiles
@eredmil1
eredmiles@gmail.com

2. Recent attacks on
democracy are attacks
on human perceptions

3. This has been going on for a long time…
“The first casualty in war is truth”
“Black Brute” message
in Jim Crow propaganda
The “Evil Jew” message
in Nazi propaganda
Not all influence
has ill intent

4. Propaganda has two goals
Influence people Avoid detection
(labeling as propaganda)

5. Novel technology,
especially targeting,
helps propaganda
achieve these goals

6. Don’t take my word for it, let’s consider
a case study of the Russian DNC Ads
On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook
Ribeiro, F., Saha, K., Babaei, M., Henrique, L., Messias, J., Goga, O., Benevenuto, F., Gummadi, K.P.,
and Redmiles, E.M. ACM FAT* 2019.

7. DNC released dataset of 3,517 ads that
Facebook identified as IRA sponsored

8. USElection
0
10
1
102
103
10
4
105
106
Jun−2015
Jul−2015
Aug−2015
Sep−2015
Oct−2015
Nov−2015
Dec−2015
Jan−2016
Feb−2016
Mar−2016
Apr−2016
May−2016
Jun−2016
Jul−2016
Aug−2016
Sep−2016
Oct−2016
Nov−2016
Dec−2016
Jan−2017
Feb−2017
Mar−2017
Apr−2017
May−2017
Jun−2017
Jul−2017
Aug−2017
Amount
Impressions
Clicks
Cost (USD)
Ads
8
Volume of advertising increased as
election approached & at inauguration
Ad volume increases by nearly an order of
magnitude in the 2 months before election
Inauguration

9. 10.8
1.6
1.0
0.80
0.56
0 10
IRA
Retail
Fitness
Healthcare
Finance
Avg. CTR (%) of Facebook Ads
IRA-sponsored ads had incredibly high engagement
Avg. Clicks: 1,062
Avg. Impressions: 11,536
Avg. Spend: $34.50

10. Propaganda has two goals
Influence people Avoid detection
(labeling as propaganda)

11. Influence people Avoid detection
(labeling as propaganda)
Using human perception measurements to
understand whether IRA ads achieved these goals
ReportingApproval
Perception
Proxy
Propaganda
Goal

12. Influence people
Measured divisiveness in ad approval to proxy
for whether propaganda influenced people
Propaganda
Goal
Survey Measurement
of Approval
1) Do you approve or disapprove
of what this ad says or implies?
Answer choices: Approve;
Disapprove; Neither; There is
nothing in this ad to approve or
disapprove of; I don’t know.
2) “Do you [approve/disapprove]
very strongly, or not so strongly?”
Metrics of
Divisiveness
Between-group:
how different are the
opinions of different
ideological groups?
Within-group:
do people of the same
political ideology have
consistent opinions?

13. Accurate human measurements require
careful questionnaire design
Pre-testing
Questions were evaluated & revised using cognitive interviews
that help ensure respondents consistently understand questions
Cognitive Load
Survey methodology research suggests that the shorter the
survey, the more accurate the measurements. Each participant
in our survey saw 10 ads
Internal measurement validity
Ads were randomized to avoid priming effects & questions had
a ”don’t know” response option to avoid inaccurate answers

14. Accurate human measurements also
require careful choice of respondent sample
Sample should be representative of
the population you wish to study
To represent the U.S. population, we used
an online panel (SSI) to collect a
census-representative sample comprised
of 40% Democrats, 40% Republicans, and
20% Independents across the U.S.
n = 2,886
Studied the 485 most impact ads (>80% spend,
impressions, clicks) run before the election
Each ad evaluated by at least 15 respondents

15. Democratic respondents approve the same ads with similar
and near-opposite strength to Republican respondents
Low within-group divisiveness:
people with similar political
leanings approve of the ads
with similar strength
𝑠𝑡𝑑 𝑑𝑒𝑣 (𝑎𝑝𝑝𝑟𝑜𝑣𝑎𝑙 𝑜𝑓 𝑖𝑑𝑒𝑜𝑙𝑜𝑔𝑖𝑐𝑎𝑙 𝑔𝑟𝑜𝑢𝑝)
𝑠𝑡𝑑 𝑑𝑒𝑣 (𝑎𝑙𝑙 𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑒𝑛𝑡𝑠)
< 1
High between-group divisiveness
We don’t want to honor racism, slavery
and hatred. This is what Confederate
Heritage is. Not My Heritage Rally
America is at risk. To protect our country we
need to secure the border
It’s ok they’re women so they’ll only
find the kitchen

16. Propaganda has two goals
Influence people Avoid detection
(labeling as propaganda)

17. Avoid detection
(labeling as propaganda)
Measured reporting behavioral intent to proxy
for whether propaganda would avoid detection
Reporting
Perception
Proxy
Propaganda
Goal
Survey
Measurement
Some social media platforms
allow you to report content by
clicking "report". Would you
report this ad?
Answer choices:
“Yes”, “No”, “I don’t know”

18. Between-group divisiveness in reporting
For nearly ¾ of the ads, at least 3 of 15 respondents (20%)
who reviewed the ad said they would have reported it

19. Reported by the Liberals
Not Reported by the Conservatives
Reported by the Conservatives
Not Reported by the Liberals20

20. Propaganda has two goals
Influence people Avoid detection
(labeling as propaganda)

21. IRA used targeting to avoid detection: match ads to
those who wouldn’t report / would approve
Reporting Approval
Ad
ProportionReported
Non-Targeted
Targeted
Ad
ApprovalScore
Non-Targeted
Targeted

22. The IRA primarily targeted their ads using
attribute-based targeting
• Using name, phone number, or email address to target the ads
• None of the IRA ads used this method
PII targeting
• Provide user PII or page, Facebook identifies targets "like" that user or page audience
• 1.1% of IRA ads were targeted this way
Look-a-like audience targeting
• Create a targeting formula based on demographics (gender, age, location, language),
advanced demographics (e.g., parents with toddlers, political leaning, etc.), interests (e.g.,
religion, travel) and behaviors (e.g., new vehicle buyers)
• ~99% of high impact IRA ads were targeted this way
• Up to 39 attributes used in targeting; 78% of ads had 2+ attributes used to target
Attributed-based targeting

23. Most used
targeting
attributes
were race-
related

24. Up to 64% of IRA ads may
have been targeted using
Facebook’s automated
targeting suggestions

25. Propaganda has two goals
Influence people Avoid detection
(labeling as propaganda)

26. The rest of this talk:
can we use human perceptions
to defend democracy?

27. Identifying perception
manipulation proactively could
be our “canary in the coal mine”

28. Leveraging human perceptions to protect
democracy: Case study of
fact checking prioritization
Analysing Biases in Perception of Truth in News Stories and their Implications for Fact Checking
Babaei, M., Kulshrestha, J., Chakraborty, A., Redmiles, E.M., Cha, M., and Gummadi, K.P. ACM FAT* 2019.

29. Traditionally, journalist claims were fact
checked; Now, anyone can produce “news”
• Produced by professional
journalists
• Limited number of media
sources
• Media watchdog groups check
for bias and inaccuracies
• Produced by members of
the crowd
• Number of potential news
sources is huge
• Not possible to monitor all
news sources

30. Fake news: news which is verifiably false
Different from the IRA ads
in that it is verifiably false
Similar in that it alters
human perceptions

31. Verifying news is time consuming & costly
• Verify stories to be True, Mostly True, False, Mostly False …
Expert fact checkers follow principled approach
(e.g., Poynter’s Code of Principles)
• Snopes, PolitiFact, FactCheck.org, AltNews, etc.
Dedicated fact-checking organizations
Solution: prioritize stories for fact checking
Problem: cannot fact check all stories

32. Current state-of-the-art: how Facebook,
Twitter, etc. prioritize stories for fact checking
Ask users to report news stories which they perceive to be false
Stories reported as false by many users are prioritized
Goal: Fact check false news with
higher probability than true news
Does the strategy achieve this goal?
Depends on how users perceive
the truthfulness of different stories

33. Let’s consider an example set of news
• S1: President Trump inherited a White House infested with cockroaches due
to the careless behavior of his predecessor, Barack Obama. False
• S2: Sen. John McCain’s vote against a ‘skinny repeal’ health care proposal
stopped attempts to repeal the Affordable Care Act for FY ’17. False
• S3: The national debt saw a ‘surprising’ decline of $102 billion
between January 20 and July 27 2017. True
• S4: President Donald Trump changed the constitution to
read ‘citizens’ instead of persons. Mostly False
Goal: Fact check false news with higher probability than true news
S2, S1 > S4 > S3

34. But, how do people
perceive the truthfulness
of these stories?

35. Created Truth Perception Tests (similar to Implicit
Association Tests) to evaluate truth perceptions
Participants rapidly (in 15 seconds or less) provide their
truth perception for the story or headline

36. Human measurements must be done carefully: we
conducted six micro-experiments validating our TPTs
TPTs are robust to sampling, answer choice, incentive and satisficing effects
Survey panel
Expert panel
Accuracy incentive
7 point scale
6 point scale
5 point scale

37. Going back to our example, relying on people’s truth
perceptions (current approach) does not achieve goal
Goal ranking by ground truth:
S2, S1 > S4 > S3
Ranking by truth perception:
S1 > S4 > S3 > S2

38. But, truth perceptions can help us achieve other goals:
decreasing polarization, correcting misperceptions

39. Identifying perception
manipulation proactively could
be our “canary in the coal mine”
Platforms can use TPTs to assess
perceptions of content in an
evenly-sampled user population
to flag issues as they emerge

40. Opportunities for
future work
Human perception measurements, and human computation,
has rarely been combined with NLP in the democracy space

41. Resources for getting started with
human measurement (survey methods)
Overview of survey methodology best practices & pitfalls:
bit.ly/surveyHandbook
Tutorial presentation of best practices & case studies:
bit.ly/surveyTutorial
University of Maryland / Michigan Joint Institute on Survey Methods
Elissa Redmiles | eredmiles@gmail.com | @eredmil1