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Joint Models of Disagreement and Stance in Online Debate
Dhanya Sridhar, James Foulds, Bert Huang, Lise Getoor, Marilyn Walker
University of California, Santa CruzVirginia Institute of Technology
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• Social media sites for debating issues
• Valuable resources for:– Argumentation– Dialogue– Sentiment– Opinion mining
Online Debate Forums
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CreateDebate.org
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CreateDebate.org Debate topic
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CreateDebate.org Debate topic
Posts
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CreateDebate.org Debate topic
Posts
Replies
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CreateDebate.org Debate topic
Posts
Replies
Reply polarity
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4Forums.com
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4Forums.comQuotation
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Graph of posts:tree structure
Online Debate Forums
Graph of users:loopy structure= reply link
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Graph of posts:tree structure
Online Debate Forums
Graph of users:loopy structure
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Zoom in on an Example
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Zoom in on an Example
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I believe Obama is likely
the worst!
He’s been infinitely more effective than
Bush!
Zoom in on an Example
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I believe Obama is likely
the worst!
He’s been infinitely more effective than
Bush!
Anti Obama
Pro Obama
Task: Stance Classification
Useful for advocacy and get-out-the-vote campaigns
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I believe Obama is likely
the worst!
He’s been infinitely more effective than
Bush!
Anti Obama
Pro Obama
Task: Stance Classification
Study argumentation and dialogue
Posts express stance
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I believe Obama is likely
the worst!
He’s been infinitely more effective than
Bush!
Anti Obama
Pro Obama
Task: Disagreement Classification
Study argumentation and dialogue
Disagree on stance
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I believe Obama is likely
the worst!
He’s been infinitely more effective than
Bush!
Anti Obama
Pro Obama
Task: Disagreement Classification
Study argumentation and dialogue
Disagree on stance
Posts express disagreement
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Stance
Stance
Stance
Stance
Classification Targets
• Stance• Author-level• Post-level
• Disagreement• Author-level• Post-level• Textual
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Stance
Stance
Stance
Stance
Disagrees
Disagrees
Disagrees
Disagrees
Classification Targets
• Stance• Author-level• Post-level
• Disagreement• Author-level• Post-level• Textual
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Stance
Stance
Stance
Stance
Disagrees
Disagrees
Disagrees
Disagrees
Classification Targets
• Stance• Author-level• Post-level
• Disagreement• Author-level• Post-level• Textual
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Related Work• Walker et. al (2012), Decision Support Sciences
State-of-the-art local stance classifier using linguistic features
• Walker et. al (2012), NAACLCollective stance classification using MaxCut over rebuttal links
• Hasan and Ng. (2013), EMNLPCollective stance sequence labeling with CRF; benefits of user stance consistency constraint
• Abbott et. al (2012), ACLLocal disagreement classifier using linguistic features
• Burfoot et. al (2011), ACLJoint classification of stance and reply polarity in Congressional debates
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Related Work• Walker et. al (2012), Decision Support Sciences
State-of-the-art local stance classifier using linguistic features
• Walker et. al (2012), NAACLCollective stance classification using MaxCut over rebuttal links
• Hasan and Ng. (2013), EMNLPCollective stance sequence labeling with CRF; benefits of user stance consistency constraint
• Abbott et. al (2012), ACLLocal disagreement classifier using linguistic features
• Burfoot et. al (2011), ACLJoint classification of stance and reply polarity in Congressional debates
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Related Work• Walker et. al (2012), Decision Support Sciences
State-of-the-art local stance classifier using linguistic features
• Walker et. al (2012), NAACLCollective stance classification using MaxCut over rebuttal links
• Hasan and Ng. (2013), EMNLPCollective stance sequence labeling with CRF; benefits of user stance consistency constraint
• Abbott et. al (2012), ACLLocal disagreement classifier using linguistic features
• Burfoot et. al (2011), ACLJoint classification of stance and reply polarity in Congressional debates
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Related Work• Walker et. al (2012), Decision Support Sciences
State-of-the-art local stance classifier using linguistic features
• Walker et. al (2012), NAACLCollective stance classification using MaxCut over rebuttal links
• Hasan and Ng. (2013), EMNLPCollective stance sequence labeling with CRF; benefits of user stance consistency constraint
• Abbott et. al (2012), ACLLocal disagreement classifier using linguistic features
• Burfoot et. al (2011), ACLJoint classification of stance and reply polarity in Congressional debates
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Related Work• Walker et. al (2012), Decision Support Sciences
State-of-the-art local stance classifier using linguistic features
• Walker et. al (2012), NAACLCollective stance classification using MaxCut over rebuttal links
• Hasan and Ng. (2013), EMNLPCollective stance sequence labeling with CRF; benefits of user stance consistency constraint
• Abbott et. al (2012), ACLLocal disagreement classifier using linguistic features
• Burfoot et. al (2011), ACLJoint classification of stance and reply polarity in Congressional debates
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Stance Classification:“Teach the Controversy”
• Previous work employs many modeling strategies
• How best to model stance in online debate?
• Answers may be different to Congressional debates– Links have different semantics– Posts much shorter than speeches. Many posts per author– Dialogue is informal
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Stance Classification:“Teach the Controversy”
• Previous work employs many modeling strategies
• How best to model stance in online debate?
• Answers may be different to Congressional debates– Links have different semantics– Posts much shorter than speeches. Many posts per author– Dialogue is informal
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Stance
Stance
Stance
Stance
Stance
Stance
Stance
Stance
Stance
Modeling at author-level or post-level?
[Hasan and Ng 2013] [Other Related Work]
Modeling Question 1)
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Stance
Stance
Stance
Stance
Stance
Stance
Stance
Stance
Stance
Modeling at author-level or post-level?
[Hasan and Ng 2013] [Other Related Work]
Modeling Question 1)
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Stance
Stance
Stance
Stance
Stance
Stance
Stance
Stance
Modeling Question 2)
[Walker et al. 2012, Hasan and Ng 2013]
Collective classification vs. local classification?
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Stance
Stance
Stance
Stance
Stance
Stance
Stance
Stance
Modeling Question 2)
[Walker et al. 2012, Hasan and Ng 2013 ] [Walker et al. 2012]
Collective classification vs. local classification?
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Stance
Stance
Stance
Stance
Disagrees
Disagrees
Disagrees
Disagrees
Jointly model disagreement together with stance?
[Abbott et. al 2012 - Linguistic Features], [Burfoot et. al 2011 for Congressional Debates]
Modeling Question 3)
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Stance
Stance
Stance
Stance
Disagrees
Disagrees
Disagrees
Disagrees
Jointly model disagreement together with stance?
[Abbott et. al 2012 - Linguistic Features], [Burfoot et. al 2011 for Congressional Debates]
Modeling Question 3)
Stance
Stance
Stance
Stance
Disagrees
Disagrees
Disagrees
Disagrees
Stance
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Our Contributions
• A unified framework to explore multiple models
• Fast, highly scalable inference– Large post-level graphs– Loopy author-level graphs
• Systematic study of modeling options
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Our Contributions
• A unified framework to explore multiple models
• Fast, highly scalable inference– Large post-level graphs– Loopy author-level graphs
• Systematic study of modeling options
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Our Contributions
• A unified framework to explore multiple models
• Fast, highly scalable inference– Large post-level graphs– Loopy author-level graphs
• Systematic study of modeling options–Modeling recommendations
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Author
Post
Local
Collective
Joint
Author Local
Author Coll.
Author Joint
Post Local
Post Joint
Post Coll.
Modeling Granularity
Statistical Models
All Combinations of Models
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Probabilistic Soft Logic (PSL)
• Templating language for highly scalable graphical model called Hinge-loss Markov Random Fields
Bach et. al (2015), ArXiVOpen source software: https://psl.umiacs.umd.edu
5.0: Disagrees(A1, A2) ^ Pro(A1) ~Pro(A2)
Rule Weight
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Probabilistic Soft Logic (PSL)
• Templating language for highly scalable graphical model called Hinge-loss Markov Random Fields
5.0: Disagrees(A1, A2) ^ Pro(A1) ~Pro(A2)
Rule Weight Predicates are continuous Random Variables!
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Probabilistic Soft Logic (PSL)
• Templating language for highly scalable graphical model called Hinge-loss Markov Random Fields
5.0: Disagrees(A1, A2) ^ Pro(A1) ~Pro(A2)
Rule Weight Predicates are continuous Random Variables!
Relaxations of Logical Operators
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Probabilistic Soft Logic (PSL)
• Rules instantiated with variables from real network
5.0: Disagrees(A1, A2) ^ Pro(A1) ~Pro(A2)
Stance
Stance
Stance
Stance
Disagrees
Disagrees
Disagrees
Disagrees
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Probabilistic Soft Logic (PSL)
• Rules instantiated with variables from real network
5.0: Disagrees( , ) ^ Pro( ) ~Pro( )
5.0: Disagrees( , ) ^ Pro( ) ~Pro( )
…
Continuous Random Variables!
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Hinge-loss MRFs Over Continuous Variables
Bach et al. NIPS 12, Bach et al. UAI 13Bach et al. (2015), ArXiV
Conditional random field over continuous RVs in
[0,1]
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Hinge-loss MRFs Over Continuous Variables
Bach et al. NIPS 12, Bach et al. UAI 13
Conditional random field over continuous RVs in
[0,1]
Feature function for each
instantiated rule
5.0: Disagrees( , ) ^ Pro( ) ~Pro( )
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Hinge-loss MRFs Over Continuous Variables
Bach et al. NIPS 12, Bach et al. UAI 13
Conditional random field over continuous RVs in
[0,1]
Feature functions are
hinge-loss functions
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Hinge-loss MRFs Over Continuous Variables
Bach et al. NIPS 12, Bach et al. UAI 13
Encodes distance to satisfaction of each instantiated rule
Linear function
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Fast Inference in Hinge-loss MRFs
Bach et al. NIPS 12, Bach et al. UAI 13
Convex, continuous inference objective…
Convex optimization!
Solved using efficient, parallelizable algorithm:
Alternating Direction Method of Multipliers (ADMM)
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Unigrams, Bigrams, Lengths, Initial n-grams, Repeated Punctuation
Obama
Bush
believe
Constructing Local Predictors
Bag-of-words
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Unigrams, Bigrams, Lengths, Initial n-grams, Repeated Punctuation
Logistic Regression
Obama
Bush
believe
Constructing Local Predictors
Pro
Not Pro
Bag-of-words
Training Labels
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Unigrams, Bigrams, Lengths, Initial n-grams, Repeated Punctuation
Logistic Regression
Observed Prediction Probabilities
Obama
Bush
believe
Constructing Local Predictors
Pro
Not Pro
Bag-of-words
Training Labels
LocalPro: 0.8
LocalPro: 0.1
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PSL Rules Shared by All Models
Stance
StanceLocalPro: 0.8
LocalPro: 0.1
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PSL Rules for Simple Collective Models
Stance
Stance
Disagrees: 1.0
LocalPro: 0.8
LocalPro: 0.1
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Joint Disagreement Models
Stance
Stance
Disagrees
LocalPro: 0.8
LocalPro: 0.1
LocalDis: 0.5
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Joint Disagreement Models
Stance
Stance
Disagrees
LocalPro: 0.8
LocalPro: 0.1
LocalDis: 0.5
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Details 4Forums.com CreateDebate.org
Topics Abortion, Gay Marriage, Evolution, Gun Control
Abortion, Gay Rights, Obama, Marijuana
Avg. Users/Topic
336 311
Avg. Posts/User
19 4
Evaluation - Datasets
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Evaluation Settings
• Prediction Tasks: Author Stance, Post Stance
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Evaluation Settings
• Prediction Tasks: Author Stance, Post Stance
• Ground Truth for CreateDebate.org:
Stance
StanceStanceStance
Majority
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Evaluation Settings
• Prediction Tasks: Author Stance, Post Stance
• Ground Truth for CreateDebate.org:
Stance
StanceStanceStance
Majority
• Ground Truth for 4Forums:Stanc
eStanc
e
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Post Local
PostColl.
Post Joint
AuthorLocal
AuthorColl.
AuthorJoint
Accuracy
Author Stance Prediction – CreateDebate.org
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Post Local
PostColl.
Post Joint
AuthorLocal
AuthorColl.
AuthorJoint
Accuracy
Author Stance Prediction – CreateDebate.org
Post < Author
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Post Local
PostColl.
Post Joint
AuthorLocal
AuthorColl.
AuthorJoint
Accuracy
Author Stance Prediction – CreateDebate.org
Post < Author
Author-JointModel is best
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Accuracy
Post Local
PostColl.
Post Joint
AuthorLocal
AuthorColl.
AuthorJoint
Post Stance Prediction – CreateDebate.org
Post < Author(still!)
Author-JointModel still best!
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Post Local
PostColl.
Post Joint
AuthorLocal
AuthorColl.
AuthorJoint
Accuracy
Author Stance Prediction – CreateDebate.org
Local < Collective < Joint
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Post Local
PostColl.
Post Joint
AuthorLocal
AuthorColl.
AuthorJoint
Accuracy
Naïve collectiveharmful at author level!
Author Stance Prediction – 4Forums.com
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Post Local
PostColl.
Post Joint
AuthorLocal
AuthorColl.
AuthorJoint
Accuracy
Author Stance Prediction – 4Forums.com
Naïve collectiveharmful at author level!
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Details 4Forums.com CreateDebate.org% Opposite Stance Posts
71.6 73.9
% Opposite Stance Authors
52.0 68.9
Explanation for Naïve Collective’s Performance
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Details 4Forums.com CreateDebate.org% Opposite Stance Posts
71.6 73.9
% Opposite Stance Authors
52.0 68.9
Explanation for Naïve Collective’s Performance
Naïve collective assumptionmostly true for posts
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Details 4Forums.com CreateDebate.org% Opposite Stance Posts
71.6 73.9
% Opposite Stance Authors
52.0 68.9
Explanation for Naïve Collective’s Performance
Naïve collective assumptionmostly true for posts
Assumption doesn’t holdat author level!
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I agree with everything except the
last part. Safe gun storage is very
important…
I can agree with this. And in case it seemed
otherwise,I know full well how to
store guns safely…
My point was that I don’t like the idea of such a
law…
Benefit of Disagreement Prediction
Agree
Anti Gun Control
Anti Gun Control
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I agree with everything except the
last part. Safe gun storage is very
important…
I can agree with this. And in case it seemed
otherwise,I know full well how to
store guns safely…
My point was that I don’t like the idea of such a
law…
Benefit of Disagreement Prediction
Agree
Anti Gun Control
Anti Gun Control
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Summary
• Unified modeling framework for efficiently, systematically exploring all modeling choices
• Author-level joint disagreement and stance model best, even for post-level prediction
• Disagreement model can be vital when modeling at author level
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Summary
• Unified modeling framework for efficiently, systematically exploring all modeling choices
• Author-level joint disagreement and stance model best, even for post-level prediction
• Disagreement model can be vital when modeling at author level
74
Summary
• Unified modeling framework for efficiently, systematically exploring all modeling choices
• Author-level joint disagreement and stance model best, even for post-level prediction
• Disagreement model can be vital when modeling at author level
75
Summary
• Unified modeling framework for efficiently, systematically exploring all modeling choices
• Author-level joint disagreement and stance model best, even for post-level prediction
• Disagreement model can be vital when modeling at author level Thank you for your attention!
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