Featured Presentation: Critical Approaches to Data Science & Machine Learning Codes & Modes Symposium, March 17th, 2017. (http://ima-mfa.hunter.cuny.edu/reframe/)

1. Media, Algorithms
and the
Filter Bubble
Codes & Modes Geetu Ambwani
3/17/2017 @geetuji

2. Filter Bubble
Personalized algorithms serve up
information that a user wants to see
based on their likes and past history
Users become separated from
contradictory information resulting in
isolation into cultural or ideological
bubbles

3. Filter Bubble in News
6 in 10 Americans get their
news from social media
[Pew 2016]
Confirmation
Bias
Personalization

4. News Breaks...
News is now a choose your own adventure story
Jim Rutenberg’s NYT column 3/12/2017

5. A veer to the right …

6. The boring middle

7. A veer to the left ...

8. Information Dissemination Through Social Media
Retweet graphs of trending news topics
[Garimella et al. WSDM 2016]

9. How should media respond ?
We can design interfaces that help
people read more balanced news in
their environment of choice
We can create compelling user
experiences that “nudge” users to
break out of the bubble

10. How should media respond ?
1. Show the opposing view
2. Show people their bias
3. Show source credibility

11. Show the opposing view
1. People say they seek diversity [Stromer-Galley 2003]
2. People agree with the norm of diverse news exposure
[Garrett & Resnick 2011]

12. Show the opposing view

13. However ...
1. Recommending opposite views … had a negative
emotional effect [Graells-Garrido et al 2013]
2. … people would still preferentially select information that
reinforced their existing attitudes [Liao et al 2013]
3. … “backfire effect” in which corrections actually increase
misperceptions [Nyhan et al 2010]

14. Show people their bias
1. … showed users feedback about their political lean … led to a modest move toward
balanced exposure [Munson et al 2013]

15. Show people their bias

16. Show source credibility
1. … explicit knowledge of the source
credibility or expertise … not only affects
what users read, but also how credible
they perceive the documents to be
[Vydiswaran et al 2012]
2. … providing not only the valence (pro or
con) but also the magnitude (moderate or
extreme) of information source position is
useful for encouraging exposure to diverse
information [Liao et al 2014]

17. Show source credibility
1. … explicit knowledge of the source
credibility or expertise … not only affects
what users read, but also how credible
they perceive the documents to be
[Vydiswaran et al 2012]
2. … providing not only the valence (pro or
con) but also the magnitude (moderate or
extreme) of information source position is
useful for encouraging exposure to diverse
information [Liao et al 2014]

18. How did we get here?
Think incentives!
● The business of media is in dire straits
● Business Model: Ad $$$ in exchange for “engagement”
● Metrics that capture engagement: page views, unique users, session time

19. Newsroom Production versus Audience Consumption

20. How did we get here?
Think incentives!
● The business of media is in dire straits
● Business Model: Ad $$$ in exchange for “engagement”
● Metrics that capture engagement: page views, unique users, session time
● Publishers and platforms are incentivised to optimize these metrics, to ensure
their very existence
Optimization

21. Where do the algorithms come in ?
Optimization lies at the heart of machine learning.
We model our problem as:
● Objective function to maximize
● System parameters that we can control
● Constraints that are limits on the inputs
We learn from the data the parameters that maximize our objective function while
satisfying the constraints.

22. Where do the algorithms come in ?
● Personalization algorithms all try to
maximize engagement (revenue) given user’s past interactions.
● Unsurprisingly, some parameters learned are -
○ “Nuance does not click well” @juliabeizer
○ Some things that do click/share well are:
● Clickbaity headlines
● Kittens
● Articles that match the user’s confirmation bias

23. Where do the algorithms come in ?
● We can reframe our optimization problem as
maximize engagement (revenue) given user’s past interactions
with additional constraints
● The additional constraints could be to ensure a diverse mix of articles or to
ensure threshold level of credible sources.
● Adding constraints ⇒ revenue tradeoff

24. Conclusion
● To address the filter bubble, we need a hybrid approach of a compelling user
interface and refined personalization algorithms.
● Always, the revenue tradeoff remains.
“These are questions that go way beyond whether we can develop AI technology
that solves the problem, ….So the technology exists or can be developed, but ...
does it make sense to deploy it.” [Yann Le Cun, Head of AI, Facebook]