emotions in argumentation: an empirical evaluation @ ijcai 2015
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Emotions in Argumentation
an Empirical Evaluation
Sahbi Benlamine, Maher Chaouachi, Claude Frasson
Serena Villata, Elena Cabrio, Fabien Gandon
question:Connection between the arguments proposed by the participants of a debate and their emotional status?• correlation of polarity of
arguments and polarity of detected emotions?
• relation between kinds and amount of arguments, and the engagement of participants?
argumentation support decision-
making and persuasion
e-democracy and online debates
abstract bipolar argumentation[Dung, 1995; Cayrol and Lagasquie-Schiex, 2013]
support and attack relations
e.g.A. Elena: This information is
important, we must publish it
B. Serena: It is a private information about a person who does not want to publish it
C. Elena: This person is the Prime Minister so the information is not private
D. Fabien: Yes, being a governmental officer makes the information about him public
attack
attack
support
emotion computing beyond purely
rational behavior detect emotional
state to adapt reactions
e.g.(serious) games
emotion detection webcams for facial
expressions analysis [FACEREADER 6.0]
physiological sensors(EEG) for cognitivestates [Chaouachi et al., 2010]
real–time engagement engagement index
[Pope et al.,1995]
EEG frequency bands (4-8Hz) α (8-13Hz) β (13-22Hz)
real-time facial analysis classifying 500 key
points in facial muscles
neural network trained on 10 000
examples happy, sad, angry,
surprised, scared, disgusted.
valence, arousal neutral probability.
Seempad Joint Research Lab Emotions play an
important role in decision making [Quartz, 2009]
Assess connection between argumentation and emotions
Final goal = Detect on the Web…• a debate turning into a
flame war,• a content reaching an
agreement,• a good or bad emotion
spreading in a community…
+
1st Experiment & Public datasetfocus on associating participants’ arguments and the relations
among them mental engagement
detected by EEG facial emotions detected via
Face Emotion Recognition
protocol of the experiment topics from popular
discussions in iDebate & DebateGraph
12 debates - 4 participants and 2 moderators each
participants equipped with emotions detection tools
messages in plain English through IRC
participants are anonymous debate for 20 minutes debrief questionnaire
participants 6 sessions of 4 participants
(-1 session) 20 participants (7 women, 13
men) age range from 22 to 35
years not all of them were native
English speakers students in a North American
university signed an ethical agreement good computer skills
data collection during the experiment minimum, average and
maximum engagement of every participant in a debate
most dominant emotion (having maximum value)
pleased/unpleased valence active/inactive arousal
data annotation after the experiment synchronize arguments,
relations and emotional indexes
bipolar argumentation labelled with : sources, arguments, emotional states
two independent annotators with agreement of 91%
Cohen’s kappa 0.82 >> 0.6
dataset content xml structure of debate flow 598 arguments in 12
different debates 263 argument pairs
127 supports 136 attacks
gender, age and personality type
dominant emotion, valence and arousal
mental engagement levels
dataset extract (flow)<argument id="2" debate_id="4" participant="4" time-from="20:30" time-to="20:30">The religion is an independent factor, it should not be a dissociative factor separating people. </argument>
<argument id="3" debate_id="4" participant="1" time-from="20:32" time-to="20:32">The religion gives to his followers hope and help them to overcome some problem of the life so it's not all bad. </argument>
<argument id="4" debate_id="4" participant="4" time-from="20:32" time-to="20:32">Here in Canada it is appreciable to find the liberty of religion a practice in a peaceful way. </argument>
dataset extract (relations)<debate id="4" title="Religion" task="relation">
<pair id="1" relation="support">
<argument id="2" debate_id="4" participant="4" time-from="20:30" time-to="20:30">The religion is an independent factor, it should not be a dissociative factor separating people. </argument>
<argument id="3" debate_id="4" participant="1" time-from="20:32" time-to="20:32">The religion gives to his followers hope and help them to overcome some problems of the life so it's not all bad. </argument>
</pair>
<pair id="2" relation="attack">
<argument id="3" debate_id="4" participant="1" time-from="20:32" time-to="20:32">The religion gives to his followers hope and help them to overcome some problems of the life so it's not all bad. </argument>
<argument id="5" debate_id="4" participant="3" time-from="20:32" time-to="20:32">During all the existence of human being, religion makes a lot of issue. It make more hurts than curs. </argument>
</pair>
dataset extract (emotions)<argument id="30" debate_id="4" participant="4" time-from="20:43" time-to="20:43" emotion_p1="neutral" emotion_p2="neutral" emotion_p3="neutral" emotion_p4="neutral">
Indeed but there exist some advocates of the devil like Bernard Levi who is decomposing arabic countries. </argument>
<argument id="31" debate_id="4" participant="1" time-from="20:43" time-to="20:43" emotion_p1="angry" emotion_p2="neutral" emotion_p3="angry" emotion_p4="disgusted">
I don’t totally agree with you Participant2: science and religion don’t explain each other, they tend to explain the world but in two different ways.
</argument>
<argument id="32" debate_id="4" participant="3" time-from="20:44" time-to="20:44" emotion_p1="angry" emotion_p2="happy" emotion_p3="surprised" emotion_p4="angry">
Participant4: for recent wars ok but what about wars happened 3 or 4 centuries ago? </argument>
initial analysis H1 : some emotional and
behavioral trends can be extracted from a set of debates.
H2 : the number and the strength of arguments, attacks and supports exchanged between the debaters are correlated with particular emotions.
H3 : the number of expressed arguments is connected to the degree of mental engagement and social interactions.
initial analysis mean percentage of
appearance of each basic emotion (with 95% confidence interval)
that the most frequent : anger 8:15% to 15:6%
second most frequent : disgust 7:52% to 14:8%
negativity effect [Rozin and Royzman, 2001]
high level engagement (70:2% to 87:7% of time)correlated with anger (r=0.306)
correlation table for Session 3 “Distribute condoms at schools”“Encourage fewer people to go to the university”
number of attacks increased linearly with disgust (H2)
aggregated correlation table
supports increase linearly with engagement (r=0.31)more pronounced when no conflict (r=0.80)
high engagement for most participative participants (H3)
to wrap-upconnection between the arguments proposed by the participants of a debate and their emotional status? H1 : emotional trends can be
extracted from debates H2 : arguments, attacks,
supports correlated with emotions
H3 : the number of arguments is connected to the engagement
current work granularity: from sessions
to debates existence or not of a priori
positive/negative opinion actual changes of opinion impact of personality (big
5 test) dynamics of the debate
and emotion changes
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