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Abstraction and Expression on the Web
Srinath Srinivasa
Web Sciences Lab
IIIT Bangalore
http://cds.iiitb.ac.in/wsl
Web Observatory Launch Workshop, IIIT Bangalore, Bengaluru, India,
17 Feb 2015
The Free Speech Conundrum
The Free Speech Conundrum
The holy grail of democratic societies – freedom of
speech (and expression) – is suddenly at the center of
a new found controversy
At the core of this debate is a call to distinguish
between “free speech” and “bad speech”
Free Speech and Bad Speech
The line is not always clear:
Disagreeing with popular opinion (free speech)
Supporting/opposing a political party (free speech)
Racial slur (bad speech)
Inciting mob violence publicly (bad speech)
Scholarly writing criticizing government or specific religions (free
speech considered bad speech in some places)
Artistic depiction that offends religious sentiments (hotly debated)
Characterizing Speech
Claim:
The free speech versus bad speech debate presents a false
dilemma, which can never be completely resolved
Need:
Semantic characterization of speech and conversations and
creating awareness and tool-support for online
conversations based on this characterization
Abstraction and Expression
Articulation of our objective
understanding of something
Communicates an idea
      ExpressionExpression
Articulation of our
subjective feeling about
something
Communicates an emotion
Abstraction
Abstraction and Expression
Reporting: mostly abstraction
Opinion: mix of abstraction and expression
Emotional reaction: mostly expression
Abstraction
● Semantic construct used to
build our worldview
● Processing is resource
intensive (“System 2” in
Prospect Theory [KT79]
terminology)
● Subject to resistance in
assimilation due to factors like
bounded rationality and
conformance pressures
Images source: Wikipedia
Abstraction and conformance
Asch Conformance Experiments
Conformance and Diffusion of Ideas
Information diffusion is faster in sparsely connected parts of a network,
rather than densely connected (entrenched) parts due to conformance effects.
Node d in the above figure does not switch to the new idea because of
conformance pressures from nodes e, f and g
Image Source: [Sri 06]
Models for Diffusion of Ideas
Typically based on an element of “criticality” that
balances between ability to communicate a new idea
and pressure to conform to existing ideas
Example models [EK 10]
Percolating clusters
Ising model
Cluster density based diffusion
Expression
● Semantic construct
encapsulating our emotional
state for communication
● Affects receiver's emotional
state by means of emotional
contagion
● Emotional contagion also
spreads through the web (Ex:
Facebook Experiment [KGH 14])
● Characteristically different from
spread of ideas, which have a
natural resistance due to
conformance effects
Images source: Wikipedia
Modelling Spread of Emotions
Models based on spread of epidemics, useful in modeling
spread of emotions
Emotional state entails physiological change in humans
People hence “recover” from emotional states after a while --
akin to the spread of epidemics
Example epidemic models [EK 10]
– SIR (Susceptible-Infected-Recovered)
– SIS (Susceptible-Infected-Susceptible)
Mental Model: Linking Abstractions and
Expressions
Axiomatic framework within which we perform reasoning.
Encapsulates underlying assumptions, ground truths and inference rules
Active mental model
Reasoning and deduction carried out within the framework
of the currently active mental model
A sudden change in active mental model usually elicits an emotional
reaction (laughter, terror, etc.)
Characterizing Online Communication
Communication (both online and offline) comprise of both abstractive and expressive
elements
The spread of a meme varies greatly if it is spreading due to its abstractive content or due
to its expressive content
Ex: Spread of rumours, primarily due to the emotions they elicit, rather than the content
they embody
Online communication especially prone to this A/E interplay due to online disinhibition
[Sul 04] factors like anonymity, solipsistic introjection, asynchrony, etc.
Specifically, online communication lacks coherence in mental models between
conversationalists.
Characterizing Online Communication
Mental model 1 Mental model 2
Mental model 1 Mental model 2
Characterizing Online Communication
Mental model 1 Mental model 2
Mental model 1 Mental model 2
Characterizing Online Communication
Mental model 1 Mental model 2
End Result?
Mental model 1 Mental model 2
Characterizing Online Communication
Online communication complicated by following factors:
– Lack of coherence between mental models (due to anonymity,
asynchrony, solipsistic introjection, etc.)
– Interplay between abstractive and expressive content in
conversation
Emotions spread faster than ideas
Spread of emotions greatly complicates the spread of
ideas
Coherence
Abstraction and Expression can affect group behaviour in
different ways
A given abstraction or expression can gain “coherence” over a
group of people (most people in the group think the same way /
most people in the group feel the same way)
Coherence in abstraction and expression can explain some
failures of crowdsourcing efforts
Wisdom of Crowds?
Not all groups of people form
“wise” crowds!
Classification of Groups
Some coherence in
abstractions
(Ex: NPOV, NOR,V
for Wikipedia)
Classification of Groups
Crowds
Group of people having shared attention but no shared abstraction or shared
expression
Rich in insights due to diverse opinions
No major emotional contagion
Members act as individuals
Pose high cognitive load on members
Unstable
Wise Crowds
Share some common abstraction in the form of “ground rules” to facilitate
management of diverse opinions without degenerating
Classification of Groups
Herds
Group sharing a common abstraction
“Herd mentality” pertains to every member of the group
thinking in the same way
High in persuasive power
Low on collective insight
Manipulable by external forces if the characteristics of the
herd are known
Classification of Groups
Mobs
Groups sharing a common emotional state
Common emotional state could be either positive emotion
(jubilant football fans) or negative emotion (lynch mobs)
Need not have common abstraction (members of an angry
mob may each be venting personal frustrations through the
mob)
Highly unpredictable behaviour
Classification of Groups
Gangs
Groups sharing both a common abstraction and common emotion
All members of the group think and feel the same way about
something
Passionate and highly persuasive
Common emotion could be positive (The researcher “gang of
four” on design patterns) or negative (bandits and other organized
criminals)
Powerful and highly impactful collective actions
Free speech revisited
What appears as the online free speech conundrum is actually a
complex phenomenon caused by abstraction, expression, dissonance
across mental models and group coherence of abstractions and
expressions
The issue is not (just) a question of what is or should be legal
provisions
The issue is of understanding the cognitive and emotional aspects of
human communication
Free speech revisited
The web is affecting who we are as a person – at a very
fundamental level, offering both opportunities and
challenges
Our understanding of web-scale abstraction and expression
dynamics too premature to advocate any form of regulatory
solutions
Specific solution proposals beyond the scope of this talk..
May you be born in interesting times...
-- an ancient Chinese curse
Thank You!
References
[EK 10] David Easley, Jon Kleinberg. Networks, Crowds and Markets: Reasoning
about a Highly Connected World. Cambridge University Press, 2010.
[KA 79] Daniel Kahneman and Amos Tversky. "Prospect theory: An analysis of
decision under risk." Econometrica: Journal of the Econometric Society (1979):
263-291.
[KGH 14] Kramer, Adam DI, Jamie E. Guillory, and Jeffrey T. Hancock.
"Experimental evidence of massive-scale emotional contagion through social
networks." Proceedings of the National Academy of Sciences 111.24 (2014):
8788-8790.
[Sul 04] Suler, John. "The online disinhibition effect." Cyberpsychology &
behavior 7.3 (2004): 321-326.

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Abstraction and Expression on the Web

  • 1. Abstraction and Expression on the Web Srinath Srinivasa Web Sciences Lab IIIT Bangalore http://cds.iiitb.ac.in/wsl Web Observatory Launch Workshop, IIIT Bangalore, Bengaluru, India, 17 Feb 2015
  • 2. The Free Speech Conundrum
  • 3. The Free Speech Conundrum The holy grail of democratic societies – freedom of speech (and expression) – is suddenly at the center of a new found controversy At the core of this debate is a call to distinguish between “free speech” and “bad speech”
  • 4. Free Speech and Bad Speech The line is not always clear: Disagreeing with popular opinion (free speech) Supporting/opposing a political party (free speech) Racial slur (bad speech) Inciting mob violence publicly (bad speech) Scholarly writing criticizing government or specific religions (free speech considered bad speech in some places) Artistic depiction that offends religious sentiments (hotly debated)
  • 5. Characterizing Speech Claim: The free speech versus bad speech debate presents a false dilemma, which can never be completely resolved Need: Semantic characterization of speech and conversations and creating awareness and tool-support for online conversations based on this characterization
  • 6. Abstraction and Expression Articulation of our objective understanding of something Communicates an idea       ExpressionExpression Articulation of our subjective feeling about something Communicates an emotion Abstraction
  • 7. Abstraction and Expression Reporting: mostly abstraction Opinion: mix of abstraction and expression Emotional reaction: mostly expression
  • 8. Abstraction ● Semantic construct used to build our worldview ● Processing is resource intensive (“System 2” in Prospect Theory [KT79] terminology) ● Subject to resistance in assimilation due to factors like bounded rationality and conformance pressures Images source: Wikipedia
  • 9. Abstraction and conformance Asch Conformance Experiments
  • 10. Conformance and Diffusion of Ideas Information diffusion is faster in sparsely connected parts of a network, rather than densely connected (entrenched) parts due to conformance effects. Node d in the above figure does not switch to the new idea because of conformance pressures from nodes e, f and g Image Source: [Sri 06]
  • 11. Models for Diffusion of Ideas Typically based on an element of “criticality” that balances between ability to communicate a new idea and pressure to conform to existing ideas Example models [EK 10] Percolating clusters Ising model Cluster density based diffusion
  • 12. Expression ● Semantic construct encapsulating our emotional state for communication ● Affects receiver's emotional state by means of emotional contagion ● Emotional contagion also spreads through the web (Ex: Facebook Experiment [KGH 14]) ● Characteristically different from spread of ideas, which have a natural resistance due to conformance effects Images source: Wikipedia
  • 13. Modelling Spread of Emotions Models based on spread of epidemics, useful in modeling spread of emotions Emotional state entails physiological change in humans People hence “recover” from emotional states after a while -- akin to the spread of epidemics Example epidemic models [EK 10] – SIR (Susceptible-Infected-Recovered) – SIS (Susceptible-Infected-Susceptible)
  • 14. Mental Model: Linking Abstractions and Expressions Axiomatic framework within which we perform reasoning. Encapsulates underlying assumptions, ground truths and inference rules Active mental model Reasoning and deduction carried out within the framework of the currently active mental model A sudden change in active mental model usually elicits an emotional reaction (laughter, terror, etc.)
  • 15. Characterizing Online Communication Communication (both online and offline) comprise of both abstractive and expressive elements The spread of a meme varies greatly if it is spreading due to its abstractive content or due to its expressive content Ex: Spread of rumours, primarily due to the emotions they elicit, rather than the content they embody Online communication especially prone to this A/E interplay due to online disinhibition [Sul 04] factors like anonymity, solipsistic introjection, asynchrony, etc. Specifically, online communication lacks coherence in mental models between conversationalists.
  • 16. Characterizing Online Communication Mental model 1 Mental model 2 Mental model 1 Mental model 2
  • 17. Characterizing Online Communication Mental model 1 Mental model 2 Mental model 1 Mental model 2
  • 18. Characterizing Online Communication Mental model 1 Mental model 2 End Result? Mental model 1 Mental model 2
  • 19. Characterizing Online Communication Online communication complicated by following factors: – Lack of coherence between mental models (due to anonymity, asynchrony, solipsistic introjection, etc.) – Interplay between abstractive and expressive content in conversation Emotions spread faster than ideas Spread of emotions greatly complicates the spread of ideas
  • 20. Coherence Abstraction and Expression can affect group behaviour in different ways A given abstraction or expression can gain “coherence” over a group of people (most people in the group think the same way / most people in the group feel the same way) Coherence in abstraction and expression can explain some failures of crowdsourcing efforts
  • 22. Classification of Groups Some coherence in abstractions (Ex: NPOV, NOR,V for Wikipedia)
  • 23. Classification of Groups Crowds Group of people having shared attention but no shared abstraction or shared expression Rich in insights due to diverse opinions No major emotional contagion Members act as individuals Pose high cognitive load on members Unstable Wise Crowds Share some common abstraction in the form of “ground rules” to facilitate management of diverse opinions without degenerating
  • 24. Classification of Groups Herds Group sharing a common abstraction “Herd mentality” pertains to every member of the group thinking in the same way High in persuasive power Low on collective insight Manipulable by external forces if the characteristics of the herd are known
  • 25. Classification of Groups Mobs Groups sharing a common emotional state Common emotional state could be either positive emotion (jubilant football fans) or negative emotion (lynch mobs) Need not have common abstraction (members of an angry mob may each be venting personal frustrations through the mob) Highly unpredictable behaviour
  • 26. Classification of Groups Gangs Groups sharing both a common abstraction and common emotion All members of the group think and feel the same way about something Passionate and highly persuasive Common emotion could be positive (The researcher “gang of four” on design patterns) or negative (bandits and other organized criminals) Powerful and highly impactful collective actions
  • 27. Free speech revisited What appears as the online free speech conundrum is actually a complex phenomenon caused by abstraction, expression, dissonance across mental models and group coherence of abstractions and expressions The issue is not (just) a question of what is or should be legal provisions The issue is of understanding the cognitive and emotional aspects of human communication
  • 28. Free speech revisited The web is affecting who we are as a person – at a very fundamental level, offering both opportunities and challenges Our understanding of web-scale abstraction and expression dynamics too premature to advocate any form of regulatory solutions Specific solution proposals beyond the scope of this talk..
  • 29. May you be born in interesting times... -- an ancient Chinese curse Thank You!
  • 30. References [EK 10] David Easley, Jon Kleinberg. Networks, Crowds and Markets: Reasoning about a Highly Connected World. Cambridge University Press, 2010. [KA 79] Daniel Kahneman and Amos Tversky. "Prospect theory: An analysis of decision under risk." Econometrica: Journal of the Econometric Society (1979): 263-291. [KGH 14] Kramer, Adam DI, Jamie E. Guillory, and Jeffrey T. Hancock. "Experimental evidence of massive-scale emotional contagion through social networks." Proceedings of the National Academy of Sciences 111.24 (2014): 8788-8790. [Sul 04] Suler, John. "The online disinhibition effect." Cyberpsychology & behavior 7.3 (2004): 321-326.