Social Power in INteractions (SPIN):
Computational Analysis of Manifestations of Power
Thesis Proposal
Vinodkumar Prabhakaran
Dept. of Computer Science, Columbia University, NY
http://www.cs.columbia.edu/~vinod
Motivation
There are large digital repositories of social interactions.
Can we computationally analyze these interactions to
identify the interactants
“powerful” participants
in them?
understand
and their inter-relations?
Vinodkumar Prabhakaran
2
Why computationally analyze power?
 Detect power relations automatically
–
Law enforcement and intelligence
–
Email summarization
–
Advertising
Vinodkumar Prabhakaran
3
Why computationally analyze power?
 Detect power relations automatically
 Perform large scale sociolinguistic analysis
–
answer fundamental questions in social sciences about
power
Vinodkumar Prabhakaran
4
Why computationally analyze power?
 Detect power relations automatically
 Perform large scale sociolinguistic analysis
 Build socially aware dialog systems
–
to enable more natural interactions
Vinodkumar Prabhakaran
5
Computational Analysis of Power
Social Network Analysis (SNA)
 who talked to whom?
 how much did they talk?
 who is central? who has reach?
 do not analyze the content
Collection of Messages
Vinodkumar Prabhakaran
e.g.,
Diesner and Carley, (2005)
Rowe et al. (2007)
6
Computational Analysis of Power
Lexical Analysis
Sara  Kim
Sara  Kim
Sara  Kim
Sara  Kim
 who said what to whom?
 does analyze the content
Kim  Sara
Kim  Sara
Kim  Sara
Kim  Sara
Collection of Messages
Vinodkumar Prabhakaran
 do not consider the context
e.g.,
Bramsen et al. (2011)
Gilbert (2012)
7
Computational Analysis of Power
Lexical Analysis
Sara  Kim
Sara  Kim
Sara  Kim
Sara  Kim
Kim  Sara
Kim  Sara
Kim  Sara
Kim  Sara
Interaction Analysis
Kim  Sara, John
Sara  Kim
Kim  Sara
John  Kim
Collection of Messages
Vinodkumar Prabhakaran
Interactions
8
Proposal: Social Power in INteractions
Interaction Analysis
Proposed Thesis
Social Power in INteractions
Kim  Sara, John
(SPIN)
Sara  Kim
 how did the participants interact
with each other?
 use deeper NLP analysis to
analyze dialog structure and
content
Kim  Sara
John  Kim
Interactions
Vinodkumar Prabhakaran
9
SPIN: Overall Research Questions
Interaction Analysis
1
Q1
2
Q2
Are power relations manifested
in the linguistic and structural
aspects of interactions?
Can we use linguistic and
structural aspects to predict
power relations?
Kim  Sara, John
Sara  Kim
Kim  Sara
John  Kim
Interactions
Vinodkumar Prabhakaran
10
SPIN: Overall Research Questions
Interaction Analysis
1
Q1
2
Q2
3
Q3
Are power relations manifested
in the linguistic and structural
aspects of interactions?
Can we use linguistic and
structural aspects to predict
power relations?
Kim  Sara, John
Sara  Kim
Kim  Sara
John  Kim
Does gender play a role in how
power is manifested in
interactions?
Vinodkumar Prabhakaran
11
SPIN: Overall Research Questions
Interaction Analysis
1
Q1
2
Q2
Are power relations manifested
in the linguistic and structural
aspects of interactions?
Can we use linguistic and
structural aspects to predict
power relations?
3
Q3
Does gender play a role in how
power is manifested in
interactions?
4
Q4
Do different types of power
manifest differently in
interactions?
Vinodkumar Prabhakaran
Kim  Sara, John
Sara  Kim
Kim  Sara
John  Kim
12
Outline of Rest of the Talk
Part A
Aspects of Interactions Analyzed in SPIN
Part B
SPIN Analysis on Organizational Email
Part C
SPIN Analysis on Political Debates
Part D
Conclusion
Vinodkumar Prabhakaran
13
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
We analyze 12 different
aspects of interactions
What is an aspect?
• collection of features
that relate to a specific
facet of an interaction
14
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
Dialog Structural Aspects
Non Structural Aspects
15
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
No NLP (Meta Data)
16
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
When did the participant
start and stopped
participating?
1
Kim
2
Sara
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
Kim
John
3
17
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
How much did a participant
contribute in an interaction?
Kim
2
messages
Sara
1
message
Kim
John
1
message
18
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
How many recipients were
there? How many replies one
got?
Kim  Sara, John
Sara Kim
Kim  Sara
John 
Kim
19
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
How often did the
participants interrupt each
other?
 applicable only to
spoken interactions
20
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
No NLP (Meta Data)
Basic NLP
21
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
Basic lexical analysis
 Word (lemma) ngrams
– “I need”, “I need the”…
 POS ngrams
– “PRP VBP”, “PRP VBP DT”…
 Mixed ngrams
– “I VBP the NN”…
22
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
 Full name
Committed Belief
CB
 Title and name
Modality
MOD
Vinodkumar Prabhakaran
How often were the
participants mentioned in the
content?
What was the form of
addressing?
 First name
 Last name
23
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
No NLP (Meta Data)
Basic NLP
Deeper NLP
24
Aspects of Interactions
NAACL2013
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
 Conventional (CONV)
Dialog Links
DL
Improved DA Tagger
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
Dialog Act (DA) Tagging (Yeh
et al. 2007)
 Req. Action (R-A)
 Req. Info (R-I)
 Inform (INF)
 Overall Accuracy
– 92% (10% error reduction)
 Minority (R-A) F-measure
– 54% (23% error reduction)
25
Aspects of Interactions
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
Gold annotations from
Yeh et al. 2007
Performance of automatic
link predictor is low (~30-40
F measure)
26
Aspects of Interactions
NAACL2012
Positional
PST
Overt Display of Power (ODP):
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Utterances that create additional
constraints on its response beyond
those imposed by the general DA.
Lexical
LEX
Mentions
MNT
Dialog Acts
DA
New Annotations for ODP
Dialog Links
DL
 5% ODP sentences
Overt Displays of Power
ODP
New ODP Tagger
Topic Shifts
TS
 F = 65.8 (using Gold DA)
Committed Belief
CB
 F = 54.2 (DA tagger)
Modality
MOD
Vinodkumar Prabhakaran
 “I need the report ASAP.” (ODP)
 “Do you think you can send the
report today?” (Not an ODP)
 F = 10.4 (random baseline)
27
Aspects of Interactions
NLPSD2014
EMNLP2014b
Positional
PST
Verbosity
VRB
Thread Structure
THR
Interruptions
INT
Lexical
LEX
How to assign topics?
Mentions
MNT
 LDA
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
 Speaker Identity for Topic
Segmentation: SITS
(Nguyen et al. 2012)
Committed Belief
CB
 SITS-VAR (EMNLP2014b)
Modality
MOD
Vinodkumar Prabhakaran
Who shifted topics and who
stayed on topic?
 LDA with Substantivity
Handling (NLPSD2014)
28
Aspects of Interactions
COLING2010
Positional
PST
Verbosity
VRB
Thread Structure
THR
Committed Belief Tagger
Interruptions
INT
Lexical
LEX
 Determine the level of belief
speaker express towards the
propositions he states
Mentions
MNT
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
- Committed Belief (CB)
- Non Committed Belief
(NCB)
- Non Attributable (NA)
29
Aspects of Interactions
ExProM2012
Positional
PST
Verbosity
VRB
Thread Structure
THR
Modality Tagger
Interruptions
INT
Lexical
LEX
Mentions
MNT
 Detect extra propositional
facets of meaning speakers
express about their
propositions
Dialog Acts
DA
Dialog Links
DL
Overt Displays of Power
ODP
Topic Shifts
TS
Committed Belief
CB
Modality
MOD
Vinodkumar Prabhakaran
-
Want
Effort
Ability
Success
Intention
30
Outline of Rest of the Talk
Part A
Aspects of Interactions Analyzed in SPIN
Part B
SPIN Analysis on Organizational Email
Part C
SPIN Analysis on Political Debates
Part D
Conclusion
Vinodkumar Prabhakaran
31
Corpus
Enron email threads
 Task-oriented discussions mostly: planning
events, discussing trading etc.
 Language is mostly formal
Total
Train (50%)
Dev (25%)
Test (25%)
# of threads
36,615
18,498
8,973
9,144
# of messages
111,933
56,447
27,565
27,921
 Org chart annotations (Agarwal et al. 2012)
Vinodkumar Prabhakaran
32
Detecting Direction of Power
Given an email thread t and a related
interacting participant pair (p1,p2),
can we automatically predict the
direction of power?
ACL2014
Kim  Sara, John
Sara  Kim
Kim  Sara
John  Kim
e.g., PowerRel(p1, p2) = Subordinate (Sub) vs. Superior (Sup)?
# of pairs
Vinodkumar Prabhakaran
Total
Train
Dev
Test
15,048
7,510
3578
3960
33
Aspects Analyzed
ACL2014
Positional
PST
✔
Verbosity
VRB
✔
Thread Structure
THR
✔
Interruptions
INT
n/a
Lexical
LEX
✔
Mentions
MNT
n/a?
Dialog Acts
DA
✔
Dialog Links
DL
✔
Overt Displays of Power
ODP
✔
Topic Shifts
TS
n/a?
Committed Belief
CB
to do
Modality
MOD
to do
Vinodkumar Prabhakaran
34
Aspects Analyzed
ACL2014
Positional
PST
✔
Verbosity
VRB
✔
Thread Structure
THR
✔
Lexical
LEX
✔
Student’s t-Test
mean(f Sub) vs. mean(f Sup)
are they statistically different?
Dialog Acts
DA
✔
Dialog Links
DL
✔
Overt Displays of Power
ODP
✔
Vinodkumar Prabhakaran
35
PST
VRB
THR
DA
DL
ODP
Sub.
Sup.
Feature Name
0.45
0.04
0.15
0.64
0.44
91.22
0.45
140.60
21.14
18.19
0.82
0.86
0.16
0.48
0.41
0.15
0.78
0.02
0.05
0.12
0.03
Initiate
0.56
FirstMsgPos
0.03
LastMsgPos
0.11
MsgCount
0.70
MsgRatio
0.56
TokenCount
83.26
TokenRatio
0.55
TokenPerMsg
120.87
Avg#Recipients
43.10
Avg#ToRecipients
38.94
InToList%
0.80
ReplyRate
1.23
0.10 ReplyRateWithinPair
0.47
AddPerson
0.37
RemovePerson
0.17
Conventional%
0.72
Inform%
0.04
ReqAction%
0.06
ReqInform%
0.15
DanglingReq%
0.06
ODP%
-150%
Vinodkumar Prabhakaran
Rel. Diff. = (Sup-Sub)/Sub
-50%
50%
150%
36
Predicting Direction of Power
ACL2014
Kim  Sara, John
Sara  Kim
Kim  Sara
John  Kim
PST
VRB
THR
PowerRel(Sara, Kim)?
LEX
Binary SVM
classifier
(quadratic kernel)
DA
Superior
DL
ODP
Vinodkumar Prabhakaran
37
Experimental Results
Feature Set
ACL2014
Accuracy
Majority Baseline
52.5
LEX
70.7
BEST Combination: LEX + THR
73.0
Vinodkumar Prabhakaran
38
SPIN: Overall Research Questions
1
Q1
Are power relations manifested
in the linguistic and structural
aspects of interactions?
Yes
2
Q2
Can we use linguistic and
structural aspects to predict
power relations?
Yes, with reasonable
accuracy
3
Q3
Does gender play a role in how
power is manifested in
interactions?
Vinodkumar Prabhakaran
39
Gender Assignment
EMNLP2014a
New Gender Identified Enron Corpus using Social Security
Administration’s names database
 identifies gender of authors of 87% of messages
Total
Train
Dev
Test
# of threads
17,788
8,911
4,328
4,549
# of pairs
4,649
2,260
1,080
1,309
Are the mean values of factorial groups significantly different?
f FemaleSub, f FemaleSup, f MaleSub, f MaleSup
(ANOVA and Tukey’s HSD tests)
Vinodkumar Prabhakaran
40
Power vs. Gender
EMNLP2014a
Hypothesis 1:
Female superiors tend to use “face-saving” strategies at
work that include conventionally polite requests and
impersonalized directives, and that avoid imperatives
(Herring, 2008).
ODP Count
0.15
0.135
0.114
0.1
0.091
0.113
0.086
0.096
0.072
0.086
0.05
0
Sub
Sup
Vinodkumar Prabhakaran
Female Male
(F)
(M)
F Sub F Sup M Sub M Sup
41
Predicting Direction of Power
same task and system as
presented before (ACL2014)
Kim  Sara, John
Sara  Kim
Kim  Sara
John  Kim
PST
VRB
THR
PowerRel(Sara, Kim)?
EMNLP2014a
LEX
Binary SVM
classifier
(quadratic kernel)
DA
Superior
DL
ODP
Vinodkumar Prabhakaran
GENDER
42
Experimental Results
Feature Set
EMNLP2014a
Accuracy
Majority Baseline
55.8
GENDER
57.6
ACL2014BEST (LEX + THR)
68.2
ACL2014BEST (LEX + THR) + GENDER
70.7
Vinodkumar Prabhakaran
43
Research Questions
EMNLP2014a
1
Q1
Are power relations manifested
in the linguistic and structural
aspects of interactions?
Yes
2
Q2
Can we use linguistic and
structural aspects to predict
power relations?
Yes, with reasonable
accuracy
3
Q3
Does gender play a role in how
power is manifested in
interactions?
Yes, and it helps
improve prediction
4
Q4
Do different types of power
manifest differently in
interactions?
Vinodkumar Prabhakaran
44
Types of Power
LREC2012
 Hierarchical Power (HP) - Person A is above Person B in the
organizational hierarchy
 Situational Power (SP) – Person A has power or authority to
direct and/or approve person B’s actions in the current situation
or while a particular task is being performed
 Power over Communication (PC) – Person with PC actively
attempts to achieve the intended goals of the communication
 Influence (INFL) – Person with INFL are consulted for their
advice/opinion and their advice/opinions are considered
important*
*Definition of INFL adapted from SCIL definition of Influence
Vinodkumar Prabhakaran
45
Power Annotations
LREC2012
Corpus: a subset of 122 threads from Enron corpus
 Hierarchical Power:
– Org chart annotations (Agarwal et al. 2012)
 Situational Power, Power over Communication & Influence:
– Manual Power Annotations on the 122 thread subset
– Inter-annotator agreement (47 threads), K : 0.47, 0.76, 0.79
Vinodkumar Prabhakaran
46
Statistics
IJCNLP2013a
Given an email thread, can we automatically find the
participants with power of a particular type?
# of data points: 221
Participants without power in the thread
Participants with power in the thread
100%
50%
0%
HP
Vinodkumar Prabhakaran
SP
PC
INFL
47
Aspects Analyzed
IJCNLP2013a
Positional
PST
✔
Verbosity
VRB
✔
Thread Structure
THR
✔
Lexical
LEX
✔
Student’s t-Test
mean(f Sub) vs. mean(f Sup)
are they statistically different?
Dialog Acts
DA
✔
Dialog Links
DL
✔
Overt Displays of Power
ODP
✔
Vinodkumar Prabhakaran
48
Student’s t-test results
Features
PST
VRB
DA
DL
ODP
Vinodkumar Prabhakaran
IJCNLP2013a
HP
SP
PC
INFL
Initiator
First message
Last message
Message count
Message ratio
Token count
Token ratio
Tokens per message
Request action
Request information
Inform
Conventional
Forward links
Backward links
Connected links
Dangling links
Dangling link ratio
ODP Count
49
Finding Persons With Power
IJCNLP2013a
Kim  Sara, John
Sara  Kim
Kim  Sara
PST
John  Kim
VRB
Who has SP?
LEX
DA
{Sara}
Binary SVM
classifier
(quadratic kernel)
DL
ODP
Vinodkumar Prabhakaran
50
Automatic Power Predictor
IJCNLP2013a
HP
F-Measures
SP
PC
Random Baseline
11.3
42.1
54.2
9.5
Lexical (LEX)
0.0
55.2
73.9
0.0
Best Combination
29.5
64.4
90.0
22.6
PST +
VRB +
ODP
DL +
ODP
PST
DL
Feature Set
Vinodkumar Prabhakaran
INFL
51
Research Questions
IJCNLP2013a
1
Q1
Are power relations manifested
in the linguistic and structural
aspects of interactions?
Yes
2
Q2
Can we use linguistic and
structural aspects to predict
power relations?
Yes, with reasonable
accuracy
3
Q3
Does gender play a role in how
power is manifested in
interactions?
Yes, and it helps
improve prediction
4
Q4
Do different types of power
manifest differently in
interactions?
Vinodkumar Prabhakaran
Yes
52
Outline of Rest of the Talk
Part A
Aspects of Interactions Analyzed in SPIN
Part B
SPIN Analysis on Organizational Email
Part C
SPIN Analysis on Political Debates
Part D
Conclusion
Vinodkumar Prabhakaran
53
Corpus
2012 Republican Primary Debates
for the US Presidential election
 Persuasive: objective to pursue
power over one another
 Well structured interactions with
occasional interruptions
# of Debates
Interaction time
Average # of Candidates per debate
Vinodkumar Prabhakaran
Transcripts: www.presidency.ucsb.edu
20
30-40 hours
6.6
54
Research Questions
1
Q1
Are power relations manifested
in the linguistic and structural
aspects of interactions?
2
Q2
Can we use linguistic and
structural aspects to predict
power relations?
3
Q3
Does gender play a role in how
power is manifested in
interactions?
4
Q4
Do different types of power
manifest differently in
interactions?
Vinodkumar Prabhakaran
55
How do we model Power?
National &
State Poll
Scores
WWW2013
Candidate’s
Power
Debate
How we model candidate’s power
Vinodkumar Prabhakaran
56
Aspects Analyzed
Positional
PST
n/a
Verbosity
VRB
✔
Thread Structure
THR
n/a
Interruptions
INT
✔
Lexical
LEX
✔
Mentions
MNT
✔
Dialog Acts
DA
-
Dialog Links
DL
-
Overt Displays of Power
ODP
-
Topic Shifts
TS
✔
Committed Belief
CB
to do
Modality
MOD
to do
Vinodkumar Prabhakaran
57
Aspects Analyzed
Verbosity
VRB
✔
Interruptions
INT
✔
Lexical
LEX
✔
Mentions
MNT
✔
Topic Shifts
TS
✔
Vinodkumar Prabhakaran
Pearson Correlation
f ~ P(X)
58
Correlation with Power Index
VRB
Words % Deviation
Turns % Deviation
Qstn % Deviation
Longest Turn
Words Per Turn
Words Per Sentence
INT
Interrupting Others
Others Interrupting
MNT
IJCNLP2013b
Mention Percent
First Name Mention %
Last Name Mention %
Full Name Mention %
Title Name Mention %
-0.5
Vinodkumar Prabhakaran
-0.3 -0.1
0.1
0.3
0.5
Pearson Correlation Coefficient
59
0.7
Correlation with Power Index
NLPSD2014
TS_Attempt#
TS_Attempt#N
TS_AttemptAfterMod#
TS_AttemptAfterMod#N
EuclideanDist
TS_SustTurns
TS_SustTime
TS
TS_Success#
LDA +
TS_Success#N
Substantivity
TS_SuccessRate
TS_Intro#
TS_IntroImpTurns
TS_IntroImpTime
-0.5
-0.3
-0.1
0.1
0.3
0.5
0.7
Pearson Correlation Coefficient with Power
Vinodkumar Prabhakaran
60
Correlation with Power Index
EMNLP2014b
Topic Shift Tendency
TS
Weighted Topic Shifts
SITS-VAR
Total Topic Shifts
TS
SITS
Weighted Topic Shifts
Total Topic Shifts
-0.3 -0.2 -0.1 0
0.1 0.2
Pearson Correlation
Vinodkumar Prabhakaran
61
Automatic Power Ranker
IJCNLP2013b
VRB
INT
SVMlight Ranker
LEX
1. Romney
MNT
2. Gingrich
3. Paul
Vinodkumar Prabhakaran
62
Automatic Power Ranker - Results
Feature Set
IJCNLP2013b
Kendall Tau
nDCG
Unigram Baseline
0.25
0.860
Lexical (LEX)
0.36
0.880
Verbosity (VRB) + Mentions (MTN)
0.47
0.961
LEX + VRB + MTN
0.37
0.902
Vinodkumar Prabhakaran
63
Automatic Power Ranker
EMNLP2014b
VRB
INT
SVMlight Ranker
LEX
MNT
1. Romney
2. Gingrich
3. Paul
Vinodkumar Prabhakaran
TS
64
Automatic Power Ranker - Results
Feature Set
EMNLP2014b
Kendall Tau
nDCG
IJCNLP2013b Ranker
0.55
0.962
SITS TS
0.36
0.907
SITS-VAR TS
0.47
0.961
IJCNLP2013b Ranker + SITS-VAR TS
0.6
0.97
Vinodkumar Prabhakaran
65
Research Questions
1
Q1
Are power relations manifested
in the linguistic and structural
aspects of interactions?
2
Q2
Can we use linguistic and
structural aspects to predict
power relations?
Vinodkumar Prabhakaran
Yes
Yes, with very good
ranking performance
66
Outline of Rest of the Talk
Part A
Aspects of Interactions Analyzed in SPIN
Part B
SPIN Analysis on Organizational Email
Part C
SPIN Analysis on Political Debates
Part D
Conclusion
Vinodkumar Prabhakaran
67
SPIN: Genres Analyzed
Organizational
Email
1
Q1
Are power relations manifested
in the linguistic and structural
aspects of interactions?
2
Q2
Can we use linguistic and
structural aspects to predict
power relations?
3
Q3
Does gender play a role in how
power is manifested in
interactions?
4
Q4
Do different types of power
manifest differently in
interactions?
Vinodkumar Prabhakaran
Political
Debates
68
Research Done
Positional
PST
✔
n/a
Verbosity
VRB
✔
✔
Thread Structure
THR
✔
n/a
Interruptions
INT
n/a
✔
Lexical
LEX
✔
✔
Mentions
MNT
n/a?
✔
Dialog Acts
DA
✔
-
Dialog Links
DL
✔
-
Overt Displays of Power
ODP
✔
-
Topic Shifts
TS
n/a?
✔
Committed Belief
CB
to do
to do
Modality
MOD
to do
to do
Vinodkumar Prabhakaran
69
Research Planned: I
Positional
PST
✔
n/a
Verbosity
VRB
✔
✔
Thread Structure
THR
✔
n/a
Interruptions
INT
n/a
✔
Lexical
LEX
✔
✔
Mentions
MNT
n/a?
✔
Dialog Acts
DA
✔
-
Dialog Links
DL
✔
-
Overt Displays of Power
ODP
✔
-
Topic Shifts
TS
n/a?
✔
Committed Belief
CB
to do
to do
Modality
MOD
to do
to do
Vinodkumar Prabhakaran
70
Research Planned: II
Positional
PST
✔
n/a
Verbosity
VRB
✔
✔
Thread Structure
THR
✔
n/a
Interruptions
INT
n/a
✔
Lexical
LEX
✔
✔
Mentions
MNT
n/a?
✔
Dialog Acts
DA
✔
-
Dialog Links
DL
✔
-
Overt Displays of Power
ODP
✔
-
Topic Shifts
TS
n/a?
✔
Committed Belief
CB
to do
to do
Modality
MOD
to do
to do
Vinodkumar Prabhakaran
71
Research Planned: III
Positional
PST
✔
n/a
Verbosity
VRB
✔
✔
Thread Structure
THR
✔
n/a
Interruptions
INT
n/a
✔
Lexical
LEX
✔
✔
Mentions
MNT
n/a?
✔
Dialog Acts
DA
✔
-
Dialog Links
DL
✔
-
Overt Displays of Power
ODP
✔
-
Topic Shifts
TS
n/a?
✔
Committed Belief
CB
to do
to do
Modality
MOD
to do
to do
Vinodkumar Prabhakaran
72
Plan of Action
Timeline
Work
Progress
I: Explore mentions and topic shifts in
Dec. 2014
Enron
In Progress
Dec. 2014 II: Utility of CB & Modality in Power
In Progress
III: Exploring Power in Wikipedia
Dec. 2014
Discussions
In Progress
Jan. 2015 Start thesis writing
Apr. 2015 Thesis defense
Vinodkumar Prabhakaran
73
Thank You
Questions?
Vinodkumar Prabhakaran
74