Empathic inclination from digital footprints* Marco Polignano, Pierpaolo Basile, Gaetano Rossiello, Marco de Gemmis and Giovanni Semeraro University 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
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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
• 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.
They are in the area of feelings and emotions
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)
• …
A very large
source for
profiling
users…
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,