Persuasive communication is the process of shaping, reinforcing and changing others’ responses. In political debates, speakers ex- press their views towards the debated topics by choosing both the content of their discourse and the argumentation process. In this work we study the use of semantic frames for modelling argumentation in speakers’ discourse. We investigate the impact of a speaker’s argumentation style and their effect in influencing an audience in supporting their candidature. We model the influence index of each candidate based on their relative standings in the polls released prior to the debate and present a system which ranks speakers in terms of their relative influence using a combination of content and persuasive argumentation features. Our results show that although con- tent alone is predictive of a speaker’s influence rank, persuasive argumentation also affects such indices.

1. A Study of the Impact of
Persuasive Argumentation in
Political Debates
Dr. Amparo E. Cano-Basave and Dr.Yulan He
1

2. Objective
Study whether persuasive cues and persuasive argumentations can
be used as predictors of speakers’ influence ranking on a political
debate.

3. Argumentation
Claim:
• Controversial statement to be judge true or false
• Not accepted by an audience without additional support
Premises
• Support underpinning the validity of a claim
Verbal and social activity of reason which aims to increase the
acceptability of a controversial standpoint by putting forward a set of
connected propositions intending to justify or refute a standpoint
before a rational judge (van Eemeren et al., 1996)

4. Argumentation, Claim and Premise Example
“[People aren’t investing in America] [because this president has made
America a less attractive place for investing and hiring than other places
in the world]” (Former Governor Mitt Romney)
claim
premise
Argumentation focuses on the rational support to justify or refute a
Standpoint.

5. Persuasive language
perceptions
Persuasion focuses on language cues aiming at: shaping, reinforcing and
changing a response.
beliefs
attitudes
behaviors
Persuasive
Language
• Emotive lexicons (e.g., atrocious,dreadful,
sensational,highly effective)
• Use of alliteration(repetitionof first consonants in series of words)

6. Persuasive Language, Example
“I’m convinced that part of the divide that we’re experiencing in the
United States, which is unprecedented, it’s unnatural and it’s
un-American, is because we’re divided economically, too few jobs,
too few opportunities” (Former Governor Huntsman).
alliteration
Emotive language
Persuasive Argumentation?

7. NLP in Political text
Rhetoric
• Detecting ideological positions (Sim et al., 2013)
• Predicting voting patterns (Thomas et al., 2006; Gerrish and Blei 2011)
• Characterizing Power based on linguistic features (Prabhakaranet al., 2013)
Argumentation
• Argumentation extraction (Cabrio and Villata,2012)
• Recognizing arguments in online discussions (FilipBoltužić and Jan
Šnajder, 2014)
• Stance classification of ideological debates (Hasan and Ng, 2013)

8. Contributions
We propose to:
• Persuasive argumentationas a feature for ranking candidates’ influence in
political debates
• Port annotations by means of semantic frames

9. Datasets
Persuasive essays Corpus (Stab & Gurevych 2014)
• 90 essays comprising1,673 sentences
• Class-level argument components and argument relations
Table 1. Persuasive essay argument annotation scheme.

10. Datasets
Presidential Political Debates Corpus
Manual transcripts of debates for the Republican party presidential primary
election(The American PresidencyProject)1
• 20 debates (May 2011- February 2012)
• 10 candidates (Avg. participationof 6.7 candidates per debate)
• 30-40 hours of interactiontime
• On avg. 20,466 words per debate
• Each transcript delimitsturns between speakers and moderators
• Each transcript marks-upoccurrences of the audiences’ reactions (e.g.,
booing, laughter)
1 http:// www.presidency.ucsb.edu/debates.php

11. Semantic Frames
Descriptionof context in which a word sense is used.
FrameNet (Baker et al., 1998)2:
- Over 1000 patterns used in English (e.g., Leadership, Causality, Aware
ness, Hostile encounter).
2SEMAFOR (Das et al., 2010) for semantic frame extraction

12. Semantic Frames as Pivoting Features
Persuasive
Essays(PE)
Claim
.
.
Attack
Relations
Semantic Frames
(SF)
Intentionality act
Cause Change
..
Emotion Directed
Extract most representative frames for each annotation
a1
an
SFa
Claim
Attack
Relations
.
.

13. Semantic Frames as Pivoting Features
Speaker u Entry in
a Debate d
Semantic Frames
(SF)
Intentionality act
Cause Change
..
Emotion Directed
Speaker u’s
Bag of SF
SFa
Claim
Attack
Relations
.
.
fd,a
u
x1
xk
.
.
.
fu
d,claim
fu
d,premise
fu
d,attack
.
.
PD
PE

14. Semantic Frames an Argument Types
Table 2. Top semantic frames for each argumentationtype
Argument
Type
Top 5 Semantic Frames
Claim Reason, Stage of Progress, Evaluative Comparison,
Competition, Cause to Change
Premise Removing, Inclusion, Killing, Cognitive Connection, Causation
For Stance Cause to Make Progress, Collaboration, Purpose, Kinship,
Expensiveness
Against
Stance
Intentionally Act, Importance, Capability, Leadership,
Usefulness
Support
Relation
Dead or Alive, Institutions, State Continue, Taking Sides,
Reliance
Attack
Relation
Usefulness, Likelihood, Desiring, Importance, Intentionally Act

15. Semantic Frames as Pivoting Features
Arg.
Type
Sentence Semantic Frames
Claim If we can turn Syria and Lebanon
away from Iran, we finally have the
capacity to get Iran to pull back.
Cause Change,
Manipulation,
Capability
For
Stance
And the reason is because that’s
how our founding fathers saw this
country set up.
Reason, Kinship,
Perception
Experience
Attack
Rel.
But you can’t stand and say you give
me everything I want or I’ll vote no.
Desiring, Posture,
Capability

16. Influence Ranking in Political Debates
Influence index (Prabhakaranet al., 2013) calculated based on a speaker’s
relative standing on polls released prior to the debate.
The influence index P of speaker u in UD is:
Where pi is the poll percentage assign to speaker u in poll i in the
reference polls.

17. Influence Ranking Approach
Let D denote a debate with a set of speakers UD={u1,..,un} and influence indexes
P(ui) for 1< i <n.
Training set for ranking:
R={(ui, γi),.., (un, γn) } where γi is the ranking of ui based on its P(ui) .
We use the Ranking SVM (Joachims,2006) to estimate the ranking function F
which outputs a score for each instance from which a global ordering of data is
constructed.

18. Features for each speaker
External EmotionCues
- Applause (APL)
- Booing (BOO)
- Laughs (LAU)
- Crosstalk(CRO)
Persuasiveness
- Persuasive argumentationfeatures
(semantic frame feature vector represent
ing each annotationscheme (fu
d,a) for
each speaker on each debate)
- Alliteration
- Emotive Language (based on emotionre
lated semantic frames,e.g., emotion_dir
ected, emotions_by_stimulus,emotions
_by_posibility).

19. Features for each speaker
Content Features Baselines for Influence Ranking (Prabhakaranet al., 2013)
- Unigrams (UG)
- Question Deviation (QD)
- Word Deviations (WD)
- Mention Percentage (MP)

20. Ranking Task Evaluation
For each debate, compare the generatedranked list of candidates using the
influence ranking approach, against a reference ranked list (gold standard) based
on the poll scores for that debate.
- 5-fold cross validation
- Report nDCG, nDCG-3, Kendall’s Tau and Spearman correlations.

21. Influence Ranking Results
Persuasive argumentationalone improves upon UG
• Premise and support relation(nDCG and nDCG-3)
• Attack Relation (Tau and Spearman)
Results suggest
• Tendency to use well supported arguments
• Tendency to attack more other candidates by presentingpremises refutinga
claim.
Persuasive argumentation+ WD + MP significantlyoutperformsall baselines
• Claim
• Premise
• For stance
• Support relation

22. Summary
Good predictors of influence ranking:
• Relevance of “What they said”
• Relevance of “Persuasiveness style of their arguments”
• Relative importance given by others by means of mentions
Candidates with higher influence ranking
• Tend to present more premises while clearly stating their stance (i.e., supporting a claim)
on a particular topic.

23. Conclusions
• Studied the impact of argumentationin speaker’s discourse and their effect in
influencingan audience on supportingtheir candidature
• To start persuasive argumentationfeatures,proposeda method to port
annotations by means of semantic frames
• Premise and Support Relation types, better predictors of a speaker’s influence
rank

24. Thank you!
@Pixarelli
a.cano-basave@aston.ac.uk