Creativity, simplicity, and

Creativity, Simplicity, and
Collective Wisdom
Ebrahim Bagheri
Canada Research Chair in Software and Semantic Computing
NSERC Industrial Research Chair in Social Media Analytics
bagheri@ryerson.ca
@ebrahim_bagheri

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• Ryerson is in Toronto and was founded in 1948
• Over 38,000 students, from over 145 countries
• We have 6 faculties and more than 100 undergraduate
programs, close to 60 masters and PhD programs
• Over 190 local, national and global partners
• Promote experiential learning enabling students to
partake in hands-on learning and to pursue
entrepreneurial pathways through collaboration
About Ryerson
September 6, 2019 |

Laboratory for Systems,
Software and Semantics
(LS3)

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• Our lab has been very active in industrial collaborations
• We have worked with over 15 industrial partners
• This consists of:
• Over $9 Million project value
• 65 trained HQP
• Currently have projects with 2 hospitals:
• Saint Mike’s Hospital
• Women’s College Hospital
Industrial Collaborations
September 6, 2019 |

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• Publications with students is a priority
• To date, we have published:
• Peer-reviewed journal papers: 69
• Peer-reviewed conference/workshop papers: 94
• Patents: 8
Publications and Patents
September 6, 2019 |

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• Our goal is for all graduate students to:
• Be career-ready upon graduation
• Be equipped with the right skillsets of the job market
• Understand how their work is applicable in the real-world
• Our lab, LS3, currently has 30 members
• We are proud of our over 65 alumni
Lab Members
September 6, 2019 |

Research Vision
September 6, 2019 | 9
Can traditional forms of field research, ethnography, surveys,
and interviews be replaced or augmented with social analytics?
Advance the state of the art in identifying and extracting
actionable insight from social media and network platforms.
Research Question
Research Objective

10September 6, 2019 |
Big Data (Big Social Data)

Big Data (Big Social Data)

Semantic Interpretation of Unstructured Content
dbpedia.org/resource/Thomas_Müller
• Tremendous amounts of ‘big’ unstructured textual content are
often ambiguous
• We have developed a fast and accurate semantic interpretation
platform, which has open API
(http://denote.rnet.ryerson.ca/rysann/)

Learning Semantic Representations
Explicit mention

Learning Implicit Semantic Representations
Implicit mention

Understanding User Perceptions
• Entity attribute aspects are fixed
• e.g. product specifications: screen size, camera resolution
• Social perception is subjective
• Is a larger screen better or worse?
• Social perception is temporal by nature
• Look at signal properties such as seasonality or trend.
• Also rougher estimates such as mean and standard deviation
Learning correlational or causal relations between:
Attribute aspects &
Social perception

• Study correlation in discrete time intervals post-release
• Perform analysis of user perception based on stationary product features
• Study correlation between competing entities’ social
perception
• Does the change or stability of social perception affect other entities?
These correlation studies give us predictive power
But not necessarily planning capability

• Planning requires the understanding of causal effects
• Correlations could be tainted with Endogeneity Problem
• In order to capture causal effects, we employ two strategies:
• Quasi-Experimental Designs (QED)
• Granger’s Notion of Causality

Quasi-ExperimentalDesigns(QED)
• A variation of Randomized Controlled Trial (RCT) without random
assignment to treatment or control
• This might suffer from confounding variables that cannot be controlled
or accounted for (damaging to internal validity)
• Given the nature of the social data:
• Time series designs with/out comparison group
• Interrupted time series design with/out comparison group
• Difference in differences (less reliable)

• Children who sleep with a night light until the age of two have
a higher incident of nearsightedness (myopia)
• Two studies published in Nature a year later failed to replicate
the result and saw no such correlation
Night Light Causes Myopia

• Children who sleep with a night light until the age of two have
a higher incident of nearsightedness (myopia)
• Two studies published in Nature a year later failed to replicate
the result and saw no such correlation
Night Light Causes Myopia
Myopic parents are more likely to employ night-time lighting aids for their children
There is an association between myopia in parents and their children

Some Examples I
Quasi-ExperimentalDesigns(QED)
• Time series design without comparison group (repeated observation of
treatment)
• Time series design with comparison group
Does the release of newer products impact social perception of the brand?
Does a brand have certain favorable quarters?
Do certain product features receive more attention in specific times of year?

GrangerCausality
• Granger defines prediction power as a sign of causality
• if a signal X1 G-causes X2, then
• past values of X1 should contain information that helps predict X2
• above and beyond the information contained in past values of X2 alone
• Advantage over QED:
• Does not require comparable products that differ in the treatment
• Causality is defined in the form of a bivariate autoregressive model
Whether the social perception of Samsung Galaxy phones has g-causal effect on the
social perception of Apple iPhone products.

• The curious case of cold-entities
• They often lack reviews and ratings
• Makes it hard to appropriately position them in the entity
space
Generating and Predicting Social Content
Can we not only predict ratings for such entities
but also
estimate/generate accurate reviews for them?

• There is already work in the literature for estimating ratings
• Similarity of specifications
• Brand value and sentiment
• Trust propagation
• Rating estimation from social content is yet to be explored
• Cold entities lack social presence
• Our objective is to:
• Predict ratings for “warm products”
• Produce time series representation of ratings as opposed to aggregate ratings
• Predict time series ratings for cold items
Estimating Ratings

• Provide understanding of causal/correlational relation
between:
• Entity specifications and social perception
• Social perception and entity ratings (over time)
Predict time series ratings for cold entities
Does transitivity hold between specs, social content and ratings?
Do changes in social perception consistently translate onto
changes in ratings?

• Existing work has looked at transferring reviews for similar
entities
• Similar products do not necessarily have identical features/issues
• Review sentences might include multiple issues
• We investigate generative models aot extractive ones
• Our objective is to learn:
• Aspect-based
• Sentiment-Driven
• Generative models (e.g. RNN, LTSM)
Generating/Estimating Reviews
Generate aspect-
oriented review for a
cold product

Mining User Communities
• Latent communities of users who share similar interests, sentiments, and
inclination towards:
• A given entity
• Competing entities,
• Active topics that pertain to the entity of interest,
• Share similar deep social features such as demographics, geographical
location, and life stages and events.
• Enhances decision making at macro-level
• Shift of focus from
• entity representation to
• user profile and interest modeling

Mining User Communities
• An accurate representation of a user in our context would have to be
multidimensional:
• Quantitative passion: the degree of user’s engagement with the relevant
social feedback content
• Qualitative perception: user’s sentiment/polarity with regards to entities,
entity types, and brands
• Demographic and geographical information
• Deep social features e.g., life events like frequency and type of travels,
change of house, etc, as well as life stage indicators

• Who is more likely to be influenced?
• High Degrees of Social Conformity
• Who would you target for advertising?
• High Degrees of Convincibility
Targeted Advertising on Social Media
Mirlohi, Bagheri et al, The reflection of offline activities on users’ online social behavior,
Information Processing and Management Journal, 2019 (IF: 3.892).

• Who is more likely to be influenced?
• High Degrees of Social Conformity

• Who would you target for advertising?
• High Degrees of Convincibility

• About 8% of U.S women are prescribed antidepressant
medication around the time of pregnancy.
• Decisions about medication use in pregnancy can be swayed
by the opinion of family, friends and online media, sometimes
beyond the advice offered by healthcare providers.
• How do pregnant women react to academic publications?
Antidepressant Usage
Vigod, Bagheri et al, Online social network response to studies on antidepressant
use in pregnancy, Journal of Psychosomatic Research 106: 70-72, 2018 (IF: 2.722).

Churn Prediction based on Social Content
• Customer churn undermines the profitability of telco
operators, facing annual churn rates up to 20% and higher.
• Retaining existing customers is considerably less expensive
than winning new customers
• We have proposed a social network-based model for churn
prediction.
• It predicts churn based on:
• Directed brand sentiment, neighborhood sentiment, life events, and over
200 other features.

Creativity,
Simplicity, and
Collective Wisdom

Is Technology a Solution to Our Problems?

• Genpact Research Institute recently found that, of nearly $600
billion spent on digital projects
• almost $400 billion of it was failed investment in projects that did not
align with the business KPI
• Bernstein Research analyzed historical tech spending as a
percentage of sale on 68 large-cap technology companies
• Most tech investment led to decline in share prices after five years
Some Statistics

My Hypothesis
The main solutions to problems go through:
+ Understanding the problem in detail in an obsessive way
+ Thinking about solutions:
+ Without regard for existing technology
+ With respect for simplicity (Occam’s razor)
+ Through collective engagement

Research vs Innovation
• USF lands $20.2 million grant for BP oil spill research
• Anything possible with 1/20th of this amount?

XPrize Foundation
• A rate of 4,670 gallons (17,677 liters) per minute, with an
efficiency of 89.5 percent.

• Complex AI based baggage tracking and routing
• The delay added approximately $560M USD to the cost of the
airport
• Added a maintenance cost of $1M a month
• If the budget for the Automated Baggage System could have
covered costs of a manual system for 1000 years!
Denver Baggage Handling System

• Over speeding is a very common traffic violation
• Based on stats in Sweden, it has been observed that about
112,000 drivers on average are ticketed for going too fast per
day
• 800 people killed in a year (this number is over 16,000 in Iran)
Road Safety

• What the speed camera lottery does is it checks when you are
below or at the speed limit. These people are then
automatically entered in a lottery for a chance to win money.
• This idea was adopted by Stockholm, Sweden
• Resulted in 22% speed reduction
Road Safety
Zero-Sum Game

• The library of congress decided to digitize a large number of
printed old archival books
• The OCR software can't recognize the words. In fact, about
30% of the words are deciphered incorrectly
Library of Congress

Deciphering illegible words
• Google alone shows over 100 Million reCapthas are shown
every day
• 100 million ReCaptcha words per day results in ~500 digitized
books each day

Mechanism Design
Technology
Experience

Mechanism Design

My Hypothesis
The main solutions to problems go through:
+ Understanding the problem in detail in an obsessive way
+ Thinking about solutions:
+ Without regard for existing technology
+ With respect for simplicity (Occam’s razor)
+ Through collective engagement

the fan who replaced scales.
The Tale of Devotion to Technology

the man who became rich.
The Tale of Complex Thinking

Knowledge Development
Problem Solving through
‘Mechanism Design’
but not as
Technology Development or Deployment (bi-products)

Creativity, simplicity, and

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