The document discusses the complex interrelationship between digital and physical ecosystems, emphasizing the importance of data-driven decision-making and insights derived from various data sources. It highlights challenges such as data quality issues and the 'data divide' while proposing tools and models to enhance understanding and strategic planning in urban environments. The text also references significant academic works and case studies related to social media sentiment analysis and public opinion.

1. Exploring the Bi-verse
A trip across the digital and physical ecospheres
Marco Brambilla
marco.brambilla@polimi.it
@marcobrambi

2. Scope and Purpose
measure
analyse
build
value

3. Taming
Complexity

4. There are more things
In heaven and earth, Horatio,
Than are dreamt of in your philosophy.
Shakespeare (Hamlet Act 1, scene 5)

5. The Answer to the Great Question...
Of Life, the Universe and Everything
Data
Information
Knowledge
Wisdom
Context
independence
Understanding
Understanding relations
Understanding patterns
Understanding principles

6. The Digital “Heaven”
Vs.
The “physical” Earth

7. [photo: http://hoglundassociates.com/Images/Cloud_Gate.jpg]
The digital reflection
of our life is
sharpening

8. Twitter sentiment vs. Gallup Poll of
Consumer Confidence
Brendan O'Connor, Ramnath Balasubramanyan, Bryan R. Routledge, and Noah A. Smith. 2010. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. In ICWSM-2010

9. Stance in time on the Brexit process
Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.

10. Stance drift on Twitter
after the Brexit referendum
Brambilla, Calisir. Analysis of On-line Debate on Long-Running Political Phenomena. The Brexit Case.

11. Vaccination vs. Disinformation
http://genomic.elet.polimi.it/vaccinitaly/
https://vaccineu.herokuapp.com/
https://periscopeproject.eu/

12. TOOL 1. MODELS. Abstraction and decisions
Model Reality
Purposes:
• descriptive
• predictive
• diagnostic
• prescriptive

13. Cities

14. A Model

15. TOOL 2. PERSPECTIVES. The MacroScope
Joël de Rosnay, The Macroscope, 1979

16. TOOL 2. PERSPECTIVES. The MacroScope
Joël de Rosnay, The Macroscope, 1979

17. Sentiment, anyone?

18. • Which locations do people visit from where?
Provenance and visits

19. Semantic Analysis
• Entity Linking
• Semantics and relations
• Users vs. topics

20. Travel
Lovers
Art
Lovers
Internet &
Tech Lovers
Users’ Biography Word Clouds
User segmentation and profiling

21. Age Sex
50.4% female
49.6% male
Demographics
Race
Bias of the medium?

22. Struggling against
the Obvious

23. New knowledge is hard
Only high frequency emerges
The long tail challenge

25. the streetlamp effect

26. Improving
Quality

27. Data Quality Issue
Gartner Report
33% of the largest global companies experienced an information crisis due
to their inability to adequately value, govern and trust their enterprise
information.
If you torture the data long enough,
it will confess to anything
– Darrell Huff

28. Participation to Public Events
0
10
20
30
40
50
60
70
Week
end
Week
end
Week
end

29. 29
• The places where the people in the city want to be seen.
(the dynamic of the hotels between Expo and the Furniture Fair)

30. 30
• The places where the people in the city want to be seen
(the dynamic of the hotels between Expo and the Furniture Fair)

31. Data vs. Question
• Are they aligned?
• The usual problem of representativeness of the sample…
• At a different scale
• With much less control

32. Foursquare
Checkins
Copyright
©
Milano-Hub
project
@Politecnico
di
Milano

33. Flickr
Copyright
©
Milano-Hub
project
@Politecnico
di
Milano

34. Instagram
Copyright
©
Milano-Hub
project
@Politecnico
di
Milano

35. Adapting
Granularity

36. Time, Space, and multiple (Web) sources
Milano Fashion Week – Social Media and Events

37. Spatial and Temporal Data Integration

39. Space Granularity: the Grid
• Regular squared grid
• Irregular grid with official business-driven meaning
• Irregular grid with data-driven definition
12/4

40. Cities into cities
http://urbanscope.polimi.it

41. Multi-granularity and Multi-source
City-scale: mobile telephone and (gross-grain geo-located)
social media data
Street/square: people counting & profiling IoT
sensors
Point of Interest:
people counting
sensor, WiFi log analysis,
beacons and (fine grain geo-
located)
social media

42. Measuring …

43. … and Communicating

44. IoT Use Cases
Source: GSMA Intelligence - IoT: the next wave of connectivity and services

45. Assessing
The Consumer
Perspective

46. HOME
AUTOMATION

47. BTICINO – LeGrand Home+Control

48. PHILIPS HUE

49. Alexa / Google Assistant / Siri

50. Arlo / Eufy / Nest / Ring …

51. Actuators

52. How Many apps?
• Data integration
• Compatibility
• Programming and planning
• Inconsistencies - duplications

53. Granting
Access and VAlue

54. “The Data Divide”
Separation between the ones that have (or can access) data
and the ones who don’t.

55. Specific: target a specific area for improvement
Measurable: quantify or at least suggest an indicator of progress
Assignable: specify who will do it
Realistic: state what results can realistically be achieved, given available resources
Time-related: specify when the result(s) can be achieve
S.M.A.R.T. Goals!

56. Data-driven decisions
Action
Data
Descriptive
What happen?
Diagnostic
Why did it happen?
Predictive
What will happen?
Decision
Prescriptive
What should I do?
Decision Support
Decision Automation
Analytics Human-Centered & Machine-Centered
[Gartner]

57. A new role: the Business Translator
• Understands the business needs
• Is able to discuss with the stakeholders
• Is able to negotiate with the technical roles
TRANSFORMS BUSINESS REQUIREMENTS
INTO ACTIONABLE DATA STRATEGIES

58. GOAL: SMART Objective!
USER QUESTIONS BUSINESS METRICS TECHNICAL & QUALITY
METRICS
ACTION
USE CASE
REACTION
TIME
•Who is the
user?
•How is the
data product
used?
•Which questions do
the user ask to
reach the Goal?
•Which question do
the user ask during
that Use Case?
•Which are the dimensions that cover this
question?
•Are there any targets for that metric?
•What is the time frame to be taken into
consideration for that metric?
•Are there any relationships between the
metrics?
•Are there quality metrics to achieve in
models?
•What kind of
reasoning is
stimulated by
reading the
metric?
•What are the
actions that derive
from the
reasoning?
•When? How
often?
The Goal Poster

59. Thanks!
Any Questions?

60. References
• Marco Di Giovanni, Francesco Pierri, Christopher Torres-Lugo, Marco Brambilla:
VaccinEU: COVID-19 Vaccine Conversations on Twitter in French, German and Italian. ICWSM 2022: 1236-1244
• Alireza Javadian Sabet, Marco Brambilla, Marjan Hosseini: A multi-perspective approach for analyzing long-running live events on social
media. A case study on the "Big Four" international fashion weeks. Online Soc. Networks Media 24: 100140 (2021)
• Marco Di Giovanni, Marco Brambilla:
Content-based Stance Classification of Tweets about the 2020 Italian Constitutional Referendum. SocialNLP@NAACL 2021: 14-23
• Marco Brambilla, Alireza Javadian Sabet, Amin Endah Sulistiawati:
Conversation Graphs in Online Social Media. ICWE 2021: 97-112
• Giorgia Ramponi, Marco Brambilla, Stefano Ceri, Florian Daniel, Marco Di Giovanni:
Content-based characterization of online social communities. Inf. Process. Manag. 57(6): 102133 (2020)
• Emre Calisir, Marco Brambilla:
The Long-Running Debate about Brexit on Social Media. ICWSM 2020: 848-852
• Marco Balduini, Marco Brambilla, Emanuele Della Valle, Christian Marazzi, Tahereh Arabghalizi, Behnam Rahdari, Michele Vescovi:
Models and Practices in Urban Data Science at Scale. Big Data Res. 17: 66-84 (2019)
• …

61. Marco Brambilla, @marcobrambi, marco.brambilla@polimi.it
http://datascience.deib.polimi.it
http://www.marco-brambilla.com