Personality prediction from network structure

Personality Prediction from Network Structure

Research Project by Marco Milano

Supervised by Fabio Pianesi

22/01/2014Rovereto

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Main Contributions Acknowledgement

The SocioMetric Badges Corpus: A Multilevel Behavioral Dataset for Social Behavior in Complex Organizations,  Lepri et al., SocialCom/PASSAT 2012

Friends don't lie: inferring personality traits from social network structure, Staiano et. al, Proceedings of the 2012 ACM Conference on Ubiquitous Computing

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Presentation Outline

Research Hypothesis and Goal Task Description Automatic Classification Results Discussion Further Work

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Research Hypothesis

Can psychological predispositions of individuals – like personality traits – add explanatory capacity (cit.) and predictive power to standard SNA tools?

22/01/2014Personality Prediction from Network Structure

Cit. from Kalish & Robins, 2005

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Research Goal

To predict one individual’s personality trait from its social network in the Sociometric Badges experiment corpus

Based on previous study by Staiano et al., 2012

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Benchmark study – Staiano et al.,2012

Goal: to predict one individual’s personality trait from its social network

Network Data: mobile phone call records between students of the MIT (USA)

Personality Data: survey

22/01/2014Personality Prediction from Network Structure

Personality Prediction from Network Structure

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Sociometric Badges Corpus

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53 participants 6 weeks duration Real work setting 1 wearable device 4 recording sensors:

IR,BT,Accelerometer,Speech

Initial + final survey Daily questionnaire

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About personality (Allport, 1962)

People’s behaviour can be explained to some extent in terms of underlying personality constructs (cit.)

Trait characteristics are constructs that remain stable over time, whilst state characteristics appear in context and are temporarily induced by external circumstances

22/01/2014Personality Prediction from Network Structure

Cit. from Kalish & Robins, 2005

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Quantify Personality – Big Five Framework

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Image from: http://blog.lib.umn.edu/

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Personality Data From initial survey The Big Five Marker Scale (BFMS) to assess personality

traits. The BFMS scale is an adjective list composed by 50 items

specifically designed to optimize the simplicity of the big-five factor solution in the light of results of psycho-lexical studies on the Italian language

Sample composed of 90.56% Italian native Non-Italian speakers received a validated translation of the

BFMS

22/01/2014Personality Prediction from Network Structure

Trait Mean

Std Max Min

Extra 41.83 9.83 62 23

Agree 51.6 6.03 62 29

Consc

50.44 8.61 66 34

Em. St.

41.02 6.92 54 23

Creat 46.11 3.74 55 38

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IR and BT Recording

Infrared hit used as indicator of face-to-face interaction

IR signal coverage: cone of height h ≤ 1 meter and radius of r ≤ htanθ, where θ = +/- 15degrees

Trasmission rate set at 1HZ

BT used as indicator of proximity RSSI (radio signal strength indicator) set to values

range -128 to 127 Sampling rate every 5 secs

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IR and BT Networks Summaries

Density: Ratio of the number of edges and the number of possible edges

Avg Short. Path Length:  Average of the length of all shortest paths from or to the vertices in the network

Avg Clustering: Ratio of the triangles and connected triples in the graph

Diamater: Maximum distance from a vertex to all other vertices in the graph

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Statistics IR BT

Number of Nodes 50 49

Number of Edges 334 981

Average Degree 13.36 40.04

Density 0.27 0.83

Avg Shortest Path Length

1.83 1.16

Avg Clustering 0.52 0.90

Diamater 3 3

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Degree-level Network Visualisation

IR BT

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Network Metrics

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Whole-network level

Transitivity Efficiency Centrality measures

Degree Closeness Betweenness Eigenvector Information

Ego-network level

Transitivity Efficiency Triadic Measures

Davis & Leinhardt (DL) Triads Census Kalish & Robins (KR) Triads Census

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Centrality Measures

22/01/2014Personality Prediction from Network Structure

Degree(A): number of neighbours of a node

Closeness(B): the reciprocal of the sum of the shortest path distances from a node to all other n-1 nodes

Betweenness(C): the sum of the fraction of all-pairs shortest paths that pass through a node

Eigenvector(D): connections to high-scoring nodes contribute more to the score of the node

Information: importance of a node is related to the ability of the network to respond to the deactivation of the node from the network

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Centrality Measures – Interpretation

Degree: number of connections of a node (popularity)

Closeness: how long would take to spread information from node to all others

Betweenness: quantifies relevance of node on information flow

Eigenvector: measures influence of a node Information: measures efficiency of propagation

of information from a node

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Personality traits /Centrality Measures Pearson’s CorrelationIR Extra Agree Consc Emo. St. Creat

Degree 0.012 -0.086 -0.135 0.162 -0.113Between 0.061 -0.124 -0.024 0.062 -0.213Close -0.001 -0.087 -0.101 0.166 -0.188Eigen 0.048 -0.073 -0.186 0.182 -0.008Inform 0.277 -0.147 0.019 -0.138 0.031

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BT Extra Agree Consc Emo. St. Creat

Degree -0.161 0.077 0.188 -0.183 0.163Between 0.160 0.097 0.110 -0.114 0.076Close -0.126 0.114 0.157 -0.148 0.133Eigen -0.179 0.072 0.187 -0.181 0.159Inform -0.031 0.168 0.042 -0.235 0.216

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Classification Task

Python’s Scikit-Learn Library Binary classification task Feature space: centrality measures Target: each Big-Five personality traits’ score

(0 for low, 1 for high) Random Forest Classifier with Leave-One-Out

Cross Validation strategy and sample bootstrapping

Compare random forest classifier vs SVM, KNN

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Classification Algorithm(s) Random Forest classifier (as used in Staiano et

al., 2012) Little parameters tuning

Support Vector Machine Performance highly dependent on parameters

tuning K-nearest Neighbors

No parameters tuning

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Random Forest

Ensemble method: divide-and-conquer based approach Combine weak learners to form a strong learner Each weak learner is a decision tree

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Random Forest – In detail For some number of trees T:

1. Sample N cases at random with replacement to create a subset of the data. The subset should be about 66% of the total set.

2. At each node: 1. For some number m, m predictor variables are selected at random from all the predictor

variables. 2. The predictor variable that provides the best split, according to some objective function, is

used to do a binary split on that node. 3. At the next node, choose another m variables at random from all predictor variables and

do the same.

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Credits: citizennet.com

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Classification ResultsF1 Score – Random Forest

IR Extra Agree Consc Emo. St Creat

Low 0.43 0.55 0.60 0.41 0.46

High 0.36 0.32 0.51 0.50 0.36

Avg/Total 0.40 0.43 0.56 0.46 0.42

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BT

Low 0.51 0.47 0.45 0.53 0.56

High 0.44 0.49 0.43 0.59 0.52

Avg/Total 0.48 0.48 0.44 0.56 0.54

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Classification ResultsF1 Score – Random Forest, SVM, KNN IR

BT

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Algorithm

Extra Agree Consc Emo. St Creat

RF 0.40 0.43 0.56 0.46 0.42

SVM 0.46 0.50 0.31 0.74 0.06

KNN 0.50 0.37 0.40 0.53 0.44

Algorithm

Extra Agree Consc Emo. St Creat

RF 0.48 0.48 0.44 0.56 0.54

SVM 0.10 0.60 0.44 0.56 0.18

KNN 0.40 0.44 0.38 0.40 0.52

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Classification Accuracy: Staiano, 2012

Bluetooth

Extra Agree Consc Emo. St. Creat

Baseline 60 58 52 60 54

RF 73.08 73.59 72.25 60.54 65.56

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Calls Extra Agree Consc Emo. St. Creat

Baseline 54.5 56.8 56.8 59.1 56.8

RF 59.45 68.82 63.83 73.74 68.39

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Discussion

Random Forest classifier underperformed Different classifiers better at predicting

different personality traits Personality traits as discrete vs continuous

variables

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Thank you

22/01/2014Personality Prediction from Network Structure

Image credits: @sinanaral