IIR 2017, Lugano Switzerland
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Transcript of IIR 2017, Lugano Switzerland
Empathic inclination from digital footprints*Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, Marco de Gemmis and Giovanni
SemeraroUniversity of Bari “Aldo Moro”, Dept. of Computer Science, Italy
* These results are already published in “Inclination to Empathy from Social Media Footprints” in proceedings of User Modelling,
Adaptation and Personalization, FIIT STU, Bratislava, Slovakia, July 2017 (UMAP 2017), DOI: http://dx.doi.org/10.1145/3079628.3079639
Hello!I am Marco
PolignanoYou can find me at [email protected]
Intelligent Information Access
• Affects detection and extraction
• Recommender Systems
• Information Filtering
• Hybrid Recommendation Strategies
• Machine Learning Techniques for Recommender Systems
http://www.di.uniba.it/~swap/index.php?n=Membri.MarcoPolignano
“
Outline
• The role of affects in human reasoning
• Digital Footprints on the Internet and Social Media
• Prediction Model of Empathy Inclination
• Experimental Session
• Discussion of Results
• Recap and future work
Human Decisions
A person facing a choosing problem has to consider different solutions and take a decision.
Traditional approaches of behavioural decision making, consider choosing as a rational process that
estimates which of various alternatives would yield the one with most positive consequences. A modern
view, consider in this process also other influences, such as them of emotions, feeling and sentiment.
Area of feelings and emotions
Each person is influenced differently by affects, without really knowing the
reason. We have some psychological studies but they are restricted to some specific
context such as gambling or high risk situation. But, Many new models have been
proposed in the last years.
• Less open to changes
• Less social
• More conservative
• Take less risks
• More connected with the past
… This doesn’t mean that standard preferences doesn’t matter.
T. E. Nygren, A. M. Isen, P. J. Taylor, J. Dulin, The influence of posi- tive affect on the decision rule in risk situations: Focus on outcome (and especially avoidance of loss) rather than probability,
Organizational be- havior and human decision processes 66 (1) (1996) 59–72.
How can I balance them?
People are influenced by affects with different intensity and empathy is a meaningful
indicator about the impact of affective aspects on the user's everyday life.
A System should be able to:
(a) detect user affects,
(b) understand influences of affects on users,
(c) use them in a core reasoning process,
(d) generate actions for supporting coherently users,
considering their affective state.
A system, able to show this functions, is Emotional Intelligent!*
* Mayer, J. D., Salovey, P., Caruso, D. R., & Sitarenios, G. (2003). Measuring emotional intelligence with the MSCEIT V2. 0. Emotion, 3(1), 97.
Empathy?“Empathy is the ability to
understand and be
influenced by self and other
emotions.
It can be correlated with social
self-confidence, even-
temperedness, sensitivity
and nonconformity. *Empathy is not a self-trait of personality, it is considered as an affective-cognitive
process over a specific situation.” **
* Hogan, Robert. "Development of an empathy scale." Journal of consulting and clinical psychology 33.3 (1969): 307.
** Zillmann, Dolf. "Empathy: Affect from bearing witness to the emotions of others." Responding to the screen: Reception and reaction processes (1991): 135-167.
Can I detect psychological
aspects?
They can be detected using different strategies: questionnaire, face-voice analysis,
text analysis, biological parameters observation… Are they accurate? Not always, but
non-intrusive strategies are going to be very accurate.*
A modern approach is based on the Analysis of all the data that the user leaves on
Social Networks. This acting strategy is called: Social Footprints Analysis.
* Park, G., Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Kosinski, M., Stillwell, D.J., Ungar, L.H., Seligman, M.E.: Automatic personality assessment through social media language. J. Pers. Soc. Psychol. 108(6), 934 (2015)
We need approaches for analyzing text and all the
information that digitally describe the user.
• Machine Learning Approaches (Multinomial Naive
Bayes, MSO – SVM, Random Forest, …)
• Thesaurus based Approaches (WordNet-Affects,
Senti – WordNet)
• …
We defined a model
for predicting the
Empathy Inclination of
users from
Social Media Sites“Privacy is dead and social media hold the smoking
gun”
Pete Cashmore, Mashable CEO
The empathy prediction model
Each user Ui is represented as the concatenation of five features vectors.
Each vector captures a particular aspect of the user profile which are really important for their
influence on most of the aspects of area of feelings and emotions.
Our starting point?
Dataset of myPersonality Project*
http://mypersonality.org/
More than 4,000,000 individual Facebook profiles
More than 6,000,000 test results
More than 36,000,000 user-like pairs
22mn status updates of 154k users
224m records of friendships connections
…
* Kosinski, M., Matz, S., Gosling, S., Popov, V. & Stillwell, D. (2015) Facebook as a Social Science
Research Tool: Opportunities, Challenges, Ethical Considerations and Practical Guidelines. American
Psychologist.
Pre-processing operations
1. Construction of Word2Vec distributional space
The word2vec model is learned over the 22 millions of user status
updates of the “mypersonality” dataset, in an attempt to discover the
semantics behind social media user language.
For each user a pseudo document that contains all her posts is
created. The pseudo document is turned into a feature vector using
the mean aggregation strategy over all the word embeddings
encountered while scanning the document
Moreover, we divide the whole vocabulary of word2vec vectors
involved in the user’s posts into clusters (k-means), which should
represent topics of discussion.
Status Updates
Pseudo
Document
200-dimensional features vector
Pre-processing operations
2. User filtering
We obtained 903 user’s from myPersonality which have
information about:
• Demographic Data: general data about the users, including age, gender, …
• BIG5 Personality Scores: personality traits of the users, including Openness,
Conscientiousness, Extroversion, Agreeableness, Neuroticism
• Facebook activity: statistics about the activity of the users, including number of
user’s likes
• User-concept SVD reduced data (SVD): 100-dimensional vectors of weights for
latent concepts associated to the users.
• User-topic membership data (LDA): 600-dimensional vectors of weights of topics
of interest associated to the users
• Facebook Status Updates: posts by the users on their personal profiles
• Empathy Quotient Scale(EQS): results of the empathy level questionnaire
Pre-processing operations
3. Data Representation
We represent each User as a vector of 1088 numeric features. The
nominal features have been binarized.
Demographic
Data
Personalit
y
FB
ActivitySVD+LDA
Word2Ve
c of Posts
+
Clusters
Empathy
120 features 5 features 12 features 700 features 250 features 1 feature
Research Questions and set upRQ1: Is it possible to predict empathy from social media footprints?
RQ2: What are the most important features to consider for
improving the prediction accuracy?
We exploit three different regression algorithms:
1. Linear Regression (Lr)
2. Simple Regression (Sr)
3. Different configurations of kernel of the SVM Regression with SMO algorithm (SMO)
For the SMO we used the polynomial kernel (SMOpoly) and the Radial Basis Function (RBF) kernel
(SMOrbf), by varying the c parameter from 1 to 8.
Metrics and Baseline
We adopted the Root Mean Square Error (RMSE) and the Mean
Absolute Error (MAE) as evaluation metrics over an interval of
possible values between 0-80. The evaluation protocol was 10
folds cross validation.
We evaluated our approach with the following baseline:
Baseline Value Predicted MAE RMSE
Majority 8 7.4784 10.8258
Avg. EQS 13.9169 6.8457 9.0757
The former always predicts the most frequent value in the dataset
(Majority), while the later computes the empathy score as the simple
average of EQS observed in the dataset (Avg. EQS).
Results of
evaluation
All Features Filtered Features –
CfsSubsetEval
Approach c MAE RMSE MAE RMSE
SMOpoly 1 12.7137 19.1565 5.714 7.8407
SMOpoly 2 15.5265 23.9027 5.7227 7.8445
SMOpoly 4 - - 5.7167 7.8412
SMOpoly 8 - - 5.725 7.8501
SMOrbf 1 5.9101 8.2341 5.6894 7.9163
SMOrbf 2 5.9543 8.2432 5.6673 7.8631
SMOrbf 4 6.1049 8.3623 5.6701 7.8275
SMOrbf 8 6.539 8.7748 5.686 7.8236
Lr - 22.7929 34.4679 5.7854 7.7269
Sr - 6.1045 8.233 6.1045 8.233
Majority 8 7.4784 10.8258 7.4784 10.8258
Avg. EQS 13.9169 6.8457 9.0757 6.8457 9.0757
Best result considering
both MAE and RMSE
textBest result considering
singular values of MAE
and RMSE
Legend of results:
Outcome 1
RQ1: Is it possible to predict empathy from social media footprints?
• 14% Atheist
• 3% Separated
• 8% from country (AG, EG, KW, HN, AR, SR)
• 75% extrovert and
aggregable
• 29% Atheist
• 11% Separated
• 11% from country (AG, EG, KW, HN, AR, SR)
• 38% extrovert and
aggregable
Low Inclination
to EmpathyScore < 30
High Inclination
to EmpathyScore >= 30
The feature selection process left 37 relevant features. If we analyze some
of them we obtain interesting statistics:
Second Round:
Ablation Test
Best result considering
both MAE and RMSE
textBest result considering
singular values of MAE
and RMSE
Legend of results:
Features MAE RMSE dif. MAE % dif. RMSE%
all – SMOrbf
15.9206 8.2949
- activity 5.9072 8.2811 0.2263 0.1664
-
demographic5.8717 8.311 0.8259 -0.1941
- personality 6.4908 9.0482 -9.6308 -9.0815 - LDA 5.9261 8.2921 -0.0929 0.0338
- SVD 5.9261 8.2921 -0.0929 0.0338
- LDA&SVD 5.8988 8.1903 0.3682 1.261
- W2V 5.9523 8.2768 -0.5354 0.2182
- W2V
clusters 5.9096 8.2643 0.1858 0.3689
Outcome 2
RQ2: What are the most important features to consider for
improving the prediction accuracy?
The removal of personality features
generate a drastically reduction of the
accuracy of the approach, considering
both MAE and RMSE
Recap and future work
• It has been showed a study of the physiological area of feeling and emotion
in literature
• It has been investigated the phenomenon of digital footprints
• It has been described a model for predicting the user’s inclination to be
empathic from data on social media
• Results of the experimental session have been discussed showing the
importance of Personality Traits and a good accuracy in the prediction task
• We are working for including these findings as part of the user profile and as
part of an Affective-Based recommendation strategy
Thanks!Any questions?You can find me at:
◇ [email protected]◇ http://www.di.uniba.it/~swap/index.php?n=Membri.MarcoPolign
ano