Weaving the Web of People and Things for Intelligent Cities

Weaving the Web of People
and Things for Intelligent Cities
Alessandro Bozzon

@aleboz

www.alessandrobozzon.com

Data Science with Humans in the loop

Amsterdam, September 14th 2017

Alessandro Bozzon HComp-NL Symposium
Outline
Taming the complexity of contemporary cities
Humans in the Urban Knowledge Loop: the building blocks
Ongoing Work: Ideas and Open Challenges
3

Take-Home Messages
Cities crave new ways to ﬁll their knowledge gaps
Social data are a goldmine of knowledge about cities
Urban data science can unlock the potential of social data
Human intelligence in-the-loop is essential for urban data processing
A comprehensive theory of human-enhanced (urban) data
management is needed
4

Taming the complexity of contemporary
cities

The cost of information search
6
๏ Knowledge gaps can
be expensive to ﬁll in
๏ Especially knowledge
about the real world
๏ Especially for complex
systems like cities
"The Macroscope" by Joël de Rosnay. 1979, Harper & Row, (New York)

Geo-social data
Sources of geo-social data
๏ Census
๏ GPS
๏ Geo-portals
๏ Spatial data infrastructures
๏ Cell phones
๏ IoT sensing devices
๏ Location-based social networks (e.g.
Foursquare)
๏ Geo-enabled social media (e.g. Twitter,
Instagram etc.)
๏ Geo-enabled human computation
7
Traditional New

Geo-social Data
8
High data quality
High levels of accuracy, completeness,
and validity
Generally truthful
Semantic-by-design
Social Urban Data Sensor & Mobile Phone Data Social Web Data
+
– Low refresh rate
Costly & laborious collection methods
Non-scalable
Limited or no temporal variability (static,
semi-static)
Census Records, Demographics, Spatial
Statistics, Economic Data, Real-Estate Data etc.
High levels of accuracy
High spatio-temporal resolution
High technology penetration
Generally truthful
Scalable & dynamic
Mostly proprietary
Very expensive to acquire
(CDRs)
Very expensive to deploy at the city-scale
No semantics
High speed & refresh rate
Created by people
Enriched with annotations about places
and human activities
Scalable
Mismatch between the platform’s scope
and the application domain
“Noisy”
Biased (tech, social)
Generally untrustworthy
Physical Sensor Data, Mobile Phone Logs
(CDRs), Transport Data, Energy Data etc.
Geo-localized Social Media Data
from web platforms (e.g. Twitter, Instagram,
Sina Weiboo 4SQ etc.)

The past
๏ Data scarcity
๏ Limited ofﬁcial resources (e.g.
censuses, surveys)
๏ Large volumes, yet infrequently
updated
๏ Limited storage and processing
๏ [+] Structured datasets
Geo-social data
9

…and the present
๏ Data richness
๏ Variety of sources
๏ Near real-time updates
๏ Abundant storage and processing
๏ [—] Spontaneous unstructured datasets
๏ [+] Crowdsourced (structured) datasets
Geo-social data
10
Need for an update to the methodological toolbox

New Knowledge Search Paradigms
11
People
Data
System
Decision
maker
ACTION
AD HOC
People
Data
System
Decision
maker
SPONTANEOUS
People
Data
System
Decision
maker
CROWDSOURCING
ACTION ACTION
APP / WEBSITE
ACTION

New methods & tools
An updated toolbox needs to capitalise on…
๏ High spatial & temporal resolution
๏ Ease of access (e.g. through APIs)
๏ Multiple information layers (e.g. spatial, temporal, social etc.)
12

New methods & tools
… and tackle …
๏ Biases (representational, contextual, functional
etc.)
๏ Complexity, diversity & multidimensionality
๏ Very large volumes
13
How?
Human Computation
Machine Learning
Distributed Computing

The SocialGlass System
๏ Scalable web-based system*
๏ (Real-time) urban analytics & geo-
visualisation**
๏ Used by IBM and the Municipalities of
Amsterdam, Paris, Adelaide
๏ 10+ Research Exhibitions &
Demonstrations
15
*Bocconi et al.(2015) **Psyllidis et al. (2015a, b)
www.socialglass.org

Social Media Data: Anatomy of a Tweet
16
Age
Gender
Nationality
User info
Place of
residence
Date
Type of
activity
Place of activity (Location)
Social information

The SocialGlass System: Backend
17
Crowd-
sourcing

The SocialGlass System: Frontend
18

The SocialGlass System: IoT / 1
19
Attribution: Fluxedo S.r.l.
IN:
OUT:

20

๏ Low Power Long Range (LoRa)
network for IoT
๏ Based on LPWAN protocol,
supplements existing 2G, 3G and
4G networks
๏ Interfaced with KPN Nationwide
LoRa network for Internet of
Things (IoT) applications
21

SocialGlass: choropleth map of prevalent social activities, as inferred from Instagram (http://www.social-glass.org)
22

SocialGlass: activity patterns of Amsterdam residents (ALF 2015) (http://www.social-glass.org)
23

SocialGlass: activity patterns of Amsterdam foreign tourists (ALF 2015) (http://www.social-glass.org)
24

SocialGlass: Flows of residents (http://www.social-glass.org)
25

Applications
27
Time
Scale
SAIL 2015 ALF 2015
Regionalization (AMS, BOS, JAK)
FabCity
Music as a cultural proxy
Milan, Como, Trento
Human activity (RDAM, ShenZhen)
King’s day 2016
Regional flows
EuroPride 2016

SAIL 2015
28

SAIL 2015
How to measure human mobility and activity dynamics
during large-scale events in real time?
----------
How to enrich observations from various data sources with
information on the demographics and sentiments of people?
29

SAIL 2015
30
8 cameras 100 GPS devices 20 WiFi sensors Social media
Headcounts on site Route tracking On-site count devices Route tracking /
User behavior
High precision
No semantics
Low density
Random distribution
Precise semantics
Low density
Fixed position
No semantics
Low density
Biased distribution
Inferred semantics
Higher density

SAIL 2015
32
Cameras WiFi sensors Social mediavs vs

SAIL 2015
33
Social media GPSvs

SAIl 2015
34
Geo-demographic analysis of social activity dynamics

SAIl 2015
35
Foreign Tourists
Residents
Druk, vol, gedrang, bomvol, boordevol, afgeladen, volgepakt, crowded, busy, jam

SAIL 2015
36
StadinBalans.KarlaGutierrez,EricvanderKooij

Applications
37
Time
Scale
SAIL 2015 ALF 2015
Regionalization (AMS, BOS, JAK)
FabCity
Music as a cultural proxy
Milan, Como, Trento
Human activity (RDAM, ShenZhen)
King’s day 2016
Regional flows
EuroPride 2016

Regionalisation & POI location prediction
How could we detect neighbourhoods of uniform
social interaction and predict new POI locations?
38

39
Age Gender Hour Topic
Social Category Venue Category Weekday All Clusters
*Psyllidis et al. (2017)
Multidimensional
clusters of social
interaction in
Amsterdam
(Component planes
and Hierarchical
GeoSOM)*.

40
Residents’ clusterLeisure cluster Tourists’ cluster
Arabic language clusterTransport / Nightlife cluster Work-related activities cluster

41
Event spaceCafé Gym
RestaurantHotel Tram stop

Humans in the Urban Knowledge Loop
The Building Blocks

Crowdsourcing in Cities: Why?
๏ Gather (ground truth) data
๏ Monitoring
๏ Situation Assessment
๏ Veriﬁcation
๏ Gather opinions
๏ Instruct citizens
43

Crowdsourcing in Cities: What
clean verify try change sign wait imagine invent invest
decide bend turn gain equip defend return fight protect
divide hide act install break pass repass order fix mark
compute measure master look weigh paint connect find
adjust shine dry appreciate add weave wash evaluate
count smile support subtract multiply buy acquire receive
gather frame observe dream deepen complete classify tag
insist reduce crosscheck explain walk approach organise
isolate restart intersect search inhabit live
44
Georges Perec, Espèces d’espaces (Species of Spaces), 1974

Crowdsourcing in Cities: How?
๏ Participatory Sensing
๏ Collective sensing
๏ Crowd sensing
๏ (IoT) Sensors
๏ GPS, Audio, Temperature
๏ Social Media
๏ Microtasking
45
Micromappers

Building Blocks
46
Social'Data'Source
Task
Modeling
Crowd
Modeling
Knowledge
Workflow
Modeling
Control3&
Optimization
The$right crowdKnowledge
need
Routing
Crowd
Skills
Personality
Expertise
Availability
Creation
Sense5
making
Analysis
Interpretation
Money
Duty
Fun
Glory
Motivations

Crowdsearcher: An Infrastructure for hybrid computation
๏ Human-enhanced Data Management
with Social Networks and Q&A systems
as crowdsourcing platforms
๏ Speciﬁcation paradigm
๏ Reactive execution and control
environment
๏ Hybrid computation ﬂows
๏ Crowds from heterogeneous systems
๏ Framework (with API)
47
http://crowdsearcher.search-computing.org
Search Execution
Engine
HumanInteraction
Management
SE Access
Interface
Human
Access
Interface
Query Interface
Local
Source
Access
Interface
Social
Networks
Q&A
Crowd-
source
platforms
Query AnswerUS PATENT US 8825701 B2 - Method and system of management of
queries for crowd searching

Reactive Crowdsourcing
๏ Crowd-sourcing should be dynamically
adapted
๏ The best way to do so is through active
rules
๏ Four kinds of rules:
๏ execution / object / performer / task control
๏ Guaranteed termination
๏ Extensibility
48
Control Data Mart
Rules Graph
Object

Expert Finding in Social Networks
Problem
๏ Ranking the members of
a social group according
to the level of knowledge
that they have about a
given topic
Available data
๏ User proﬁle (Distance 1)
๏ behavioural trace that
users leave behind them
through their social
activities (Distance 2)
49
๏ Proﬁles (Distance 1) are less effective than level-1 resources (e.g. posts)
๏ Resources produced by others help in describing each individual’s expertise
๏ Twitter is the most effective social network for expertise matching – sometimes it
outperforms the other social networks
๏ Twitter most effective in Computer Engineering, Science, Technology & Games, Sport
๏ Facebook effective in Locations, Sport, Movies & TV, Music
๏ Linked-in never very helpful in locating expertise

User Modelling: Expertise-Driven Rec. for Q&A Systems
50
• Answer Utility

• 1/(rank position) of an answer
• measure the usefulness of answer to a question
• Question Debatableness
• #answers to a question
• consider “difﬁculty” of the question
20
Mean Debatableness
Mean Answering Quality
AnsweringQuality
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Question Debatableness
1 3 6 10 15 20 30 45
Active
Activeness (#answers)
Owls
log(#Users)
1
10
2
10
4
log(MEC)
0.5 1 2 5
#users
1
102
104
1
Activeness != Expertise New Expertise Metric
Expertise
Extrinsic
Intrinsic
Expertise, Intrinsic and
Extrinsic Motivations are
Topic dependant
Optimally weighting (IM, EM,
EX) differently for different
topic can improve
recommendation
Conﬁg. NDCG@1 NDCG
Pearson
Corr.
Kendall
tau Corr.
Random 578 838 4 3
Expertise 679 879 234 226
Intrinsic M. 624 857 104 100
Extrinsic M. 679 878 225 217
Combined 689 883 255 245
Topic: Web x 0.001
×
How can expertise be
measured?
๏ Novel Expertise
Metrics (MEC)
Do all questions need
experts?
๏ Question Routing
Based on topics
Expertise and
Motivations

The importance of task design
51
Task Arrival
vs Completion Time
Panos Ipeirotis.
Batch Size
vs Error Rate
Eickhoff, Carsten, and Arjen P. de Vries.
Increasing cheat robustness of crowdsourcing
tasks. Information retrieval 2013.
Task design and clear instructions have an impact on the selection/
duration/quality of work
Eickhoff, Carsten, and Arjen
P. de Vries. Increasing cheat
robustness of crowdsourcing
tasks. Information retrieval
2013.Panos Ipeirotis. Analyzing the amazon mechanical turk
marketplace. XRDS 2010.
Batch Size vs. Error RateTask Arrival vs. Completion Time

Task Modelling: complexity & clarity
There is neither a model that describes good
task design, nor a measure to quantify it
We investigated:
๏ Complexity: the real cognitive effort that
performers need to put into the completion of
tasks
๏ Clarity: quality of task description and
instructions in terms of comprehensibility for
workers
๏ What needs to be done (goal clarity), how can it
be performed (role clarity)
52
TASK
µTaskTask UX
Input
Objects
Output
Objects
Design
Interface
Operations
Output
Aggregation And
Quality Control
Task
Routing
Incentives
Advertisement
(Requester) Reputation Management

Complexity of Human Computation Tasks /1
61 real-world AMT tasks, 12
workers per task, NASA-TLX
๏ Which task features can
characterise crowdsourcing
task complexity?
๏ How to predict task
complexity in an automatic
way?
53
Feature
Set
Regression Model
Linear Lasso MFLR Random Forest
Metadata 13.37±4.18 13.16±4.24 – 9.94±1.68
Visual 14.86±4.01 12.50±2.07 9.97±1.28 10.21±1.15
Content 12.87±1.64 9.97±1.27 9.18±1.83 10.00±1.47
Content LDA 10.34±1.84 9.23±1.44 – 11.80±1.18
Table 3: Subjective complexity estimation, measured by mean absolute error (MAE). The best predicting mo
Subjective Complexity Estimation: Mean
Absolute Error. GT = 63.78 ± 11.46
on the training data, we set the parameters of MFLR as fol-
lows: X = 1, = Y = 0.01. To account for the Content
features dimensionality variance, we set the number of top-
ics to be extracted by LDA to 10, so as the number of latent
complexity dimensions of MFLR.
Visual Feature Imp. Semantic Feature Imp.
visualAreaCount 3.25 linkCount 2.42
hueAvg 0.09 wordCount 1.37
keyword: audio 0.09
keyword: transcribe 0.07
keyword: writing 0.06
imageAreaCount -0.27 unigram: clear -0.06
colorfulness1 -0.63 unigram: identify -0.07
scriptCount -1.52 uigram: date -0.09
valAvg -1.71 keyword: easy -0.10
cssCount -1.82 imageCount -1.01
Table 4: Features more correlated (positively and nega-
tively) with subjective complexity.
5
We refer the reader to our companion page for the detailed
optimization algorithm.
6
http://scikit-learn.org/stable/modules/generated/sklearn.linear_
model.Lasso.html
ture
item
The
Java
ity,
and
plex
fact
desi
poin
(e.g
ter (
link
I
As
tive
are
repo
set
task
1) e
for
yiel
the
Task Features More Correlated with
Complexity

Complexity of Human Computation Tasks /2
~8700 tasks from AMT
๏ How do task complexity
features affect task
performance?
54
Feature
Set
[1,10) (GT: 3.01±2.35) [10,100) (GT: 29.92±21.63) [100,1000) (GT: 265.45±185.97)
Linear Lasso MFLR RForest Linear Lasso MFLR RForest Linear Lasso MFLR RForest
Metadata 3.88 3.78 – 3.65 18.35 18.11 – 17.45 126.81 126.45 – 107.60
Dynamic 3.73 3.61 – 3.93 20.31 18.95 – 20.47 126.76 131.30 – 138.55
Content 518.29 3.42 2.72 4.50 576.44 16.86 15.65 20.53 265.45 110.14 112.48 116.33
Content LDA 4.11 3.42 – 4.55 18.92 17.88 – 19.75 123.11 118.15 – 128.43
Table 5: Throughput prediction performance of different feature classes and regression models, measured by MAE. Results
are reported for three batch groups: throughput within the range of [1, 10), [10, 100), and [100, 1000). GT is the ground truth
throughput. In every group, the best performance among different regression models for each class of features is highlighted in
bold; the best performance among all feature classes for every group is underlined.
Impo.
LPD’s
and
Their
main
unigrams
LPD1 LPD2 LPD3 LPD4
text find search expressions
clip company relevance faces
copy online google emotions
easy fast internet mimicking
quick entry information camera
Impo.
unigrams
bonus copy easy categorization
image search instances indentification
Table 6: Main LPD’s and unigrams that contribute the most
to throughput prediction.
- TP + TP
- CPX
know, words, exact photo, picture, text
guidelines, shown item, identify, type
free, ask, based thank, best, easy
change, load, tell address, job, pay
notes, number, need accept, right
+ CPX
questions, answer transcribe, audio, images
file, provide review, feedback, search
create, options, listed read, article, try
save, issues, result code, writing, sentence
personal, send, unable carefully, follow, instructions
tion: -0.1308; p < 0.05) is present only for low through-
put ([1,10)) tasks. This result supports the findings from
the previous section, where low throughput tasks were the
ones more likely to benefit for content features for predic-
tion purposes. The negative correlation indicates the pres-
ence of features that, while hinting to higher task complex-
ity, can signal low completion performance, and viceversa.
We show these features in Table 7. The weak – yet signif-
icant – correlation is due to the existence of features hav-
ing consistent correlation, i.e. both associated positively
(respectively, negatively) with complexity and performance.
Unigrams describing task actions and task type are again the
ones more likely to be associated with consistent complex-
ity and performance prediction. For instance transcribe au-
dio unigrams are predictive of high complexity7
and higher
throughput in the low throughput ([1,10)) batches. This is
an unexpected result, that we can explain only by looking at
the MTurk market as a whole (Difallah et al. 2015), where
the presence of large batches devoted to audio transcription
might influence workers’ task selection strategy. Clearly,
further investigation focusing on the relationship between
task complexity, market dynamics, and execution perfor-
Throughput prediction performance: Mean Absolute Error
Content features are more informative than metadata features in low throughput
Table 5: Throughput prediction performance of different featu
reported for three batch groups: throughput within the range
throughput. In every group, the best performance among differe
bold; the best performance among all feature classes for every g
Impo.
LPD’s
and
Their
main
unigrams
LPD1 LPD2 LPD3 LPD4
Impo.
unigrams
to throughput prediction.
- TP + TP
- CPX
know, words, exact photo, picture, text
guidelines, shown, appear item, identify, type
free, ask, based thank, best, easy
change, load, tell address, job, pay
notes, number, need browser, accept, right
+ CPX
comment, questions, answer transcribe, audio, images
file, provide, information review, feedback, search
create, options, listed read, article, try
save, issues, result code, writing, sentence
personal, send, unable carefully, follow, instructions
Top-15 unigrams associated with positive (+)
and negative (-) correlation with complexity
(CPX) and throughput (TP) prediction.
Feature
Set
[1,10) (GT: 3.01±2.35) [10,100) (GT: 29.92±21.63) [100,1000) (GT: 265.45±185.97)
Linear Lasso MFLR RForest Linear Lasso MFLR RForest Linear Lasso MFLR RForest
Metadata 3.88 3.78 – 3.65 18.35 18.11 – 17.45 126.81 126.45 – 107.60
Dynamic 3.73 3.61 – 3.93 20.31 18.95 – 20.47 126.76 131.30 – 138.55
Content 518.29 3.42 2.72 4.50 576.44 16.86 15.65 20.53 265.45 110.14 112.48 116.33
Content LDA 4.11 3.42 – 4.55 18.92 17.88 – 19.75 123.11 118.15 – 128.43
Table 5: Throughput prediction performance of different feature sets and regression models, measured by MAE. Results are
reported for three batch groups: throughput within the range of [1, 10), [10, 100), and [100, 1000). GT is the ground truth
throughput. In every group, the best performance among different regression models for each class of features is highlighted in
bold; the best performance among all feature classes for every group is underlined.
Impo.
LPD’s
and
Their
main
unigrams
LPD1 LPD2 LPD3 LPD4
Impo.
unigrams
importance of features in complexity prediction and in per-
formance prediction. Results show a weak negative corre-
lation of feature importance. Significance (Pearson correla-
tion: -0.1308; p < 0.05) is present only for low through-
put ([1,10)) tasks. This result supports the findings from
the previous section, where low throughput tasks were the
ones more likely to benefit for content features for predic-
tion purposes. The negative correlation indicates the pres-
ence of features that, while hinting to higher task complex-
ity, can signal low completion performance, and viceversa.
Main Latent Performance Dimensions unigrams that
contribute the most to throughput prediction
Action Task Type Type of Annotation

Clarity of Human Computation Tasks
Surveyed 100 workers from
CrowdFlower
What makes tasks unclear to crowd
workers? How do workers deal with
such unclear tasks?
๏ Instructions & task description,
language
๏ Workers confront unclear tasks
regularly
๏ Use dictionaries, translators,
external
55
Factors cited by workers
that make tasks unclear
0
20
40
None Low Fair Moderate High
Degree of Influence
No. of Workers (in %)
Degree of inﬂuence of task
clarity on performance

7100 tasks from AMT and acquired
task clarity labels from
CrowdFlower
How is the clarity of crowdsourcing
tasks perceived by workers, and
distributed over tasks?
๏ Task clarity is coherently
perceived, and is affected by
task type
๏ Task clarity is orthogonal to
complexity
56
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Relationship of Task Clarity with
Goal Clarity, and Role Clarity.
r=.001
30
60
90
2 3 4 5
Task Clarity
TaskComplexity
Overall relationship
between task clarity
and task complexity
there is no observable
correlation between the two
variables across the diﬀerent
types of tasks.

Which features can characterise the
goal and role clarity of a task? To
what extent can task clarity be
predicted?
๏ We tested features based on
metadata of tasks, task type, task
content and task readability
๏ Good clarity prediction performance
(MAE = .4, SD=.003)
57
On the Role of Task Clarity in Microtask Crowdsourcing HT ’17, July 04-07, 2017, Prague, Czech Republic
Content Features capture the semantics of a task. ese features
se the high-dimensional bag of words (BOW) representation. To
maximize the informativeness of the content features while mini-
mizing the amount of noise, one-hot (i.e. binary) coding was applied
o the BOW feature of task title and keywords, while TF-IDF weight-
ng was applied to the BOW feature of task description. It has been
hown by research in related domains (e.g., community QA sys-
ems [37]) that the use of words is indicative of the quality of task
ormulation, therefore we are interested in understanding the eect
f language use on workers’ perception of task clarity.
Readability Features are by nature correlated with task clarity:
asks with higher readability are beer formulated, and are thereby
xpected to have a higher clarity. We experiment with several
widely used readability metrics in our clarity prediction task to un-
erstand their predictive power of task clarity. ese include the use
f long words (long words), long sentence (words per sentence),
he use of preposition, nominalization, and more compre-
ensive readability metrics such as ARI, LIX, and in particular,
oleman Liau, which approximates the U.S. grade level necessary
o comprehend a piece of text.
5.2 Prediction Results
Table 6: Predictive features for task clarity prediction.
Feat. Class
Feat. w. Positive Coef. Feat. w. Negative Coef.
Feature Coef.* Feature Coef.*
Metadata
number keywords 0.719 external links -0.598
description length 0.295
number images 0.071
total approved 0.011
Task Type
VV 0.434 IA -0.922
SU 0.413
Content
keyword: audio 2.673 keyword: id -2.658
keyword: transcription 1.548
keyword: survey 1.178
Readability
preposition 1.748 ARI -1.982
GunningFogIndex 1.467 long words -0.671
Coleman Liau 0.855 syllables -0.478
words per sentence 0.620 nominalization -0.136
characters 0.237 pronoun -0.104
LIX 0.150 FleschReadingEase -0.075
RIX -0.038
(all about title) (all about title)
* For the sake of comparison, each value is shown with original coecient ⇥102.
With regard to task type features, we nd that tasks of type SU
and VV are in general of higher clarity, while tasks of type IA are

Ongoing Work
Ideas and Open Challenges

The tyranny of geography
๏ Location is everything
๏ Even in the online world
๏ How can we exploit this?
59

Chatbot with Human in the Loop /1
Why chatbots?
๏ No friction
๏ Universal interaction interface
๏ Support for different typed of needs
๏ Conversational
๏ Informational
๏ Transactional
60
Design and Implementation of a Hybrid Chatbot System 3.1 System Design
Figure 3.1: High-Level View of a Hybrid Chatbot SystemHybrid Chatbot Architecture

Why humans in the loop?
๏ Training
๏ Coping with uncertainty and
lack of data
๏ Understanding
61
This is how our system components function at run time phase for understandi
user request.
Figure 3.5: Run Time Phase of a Hybrid Chatbot System for Understanding User Re

Why humans in the loop?
๏ Activate Engage
Recommender Systems for
Citizens
๏ CitRec 2017 (citrec.org)
๏ Run by citizens, for citizens
๏ Shared value creation as utility
function
๏ Fairness and transparency by
design
62

Relevant Publications
๏ Yang J., Redi J., DeMartini G., Bozzon A. (2016) “Modeling Task Complexity in Crowd- sourcing”, Proceedings of The Fourth AAAI Conference on Human Computation
and Crowdsourcing (HCOMP 2016). AAAI, Austin, Texas, USA. October 31 - November 2. Volume 9891, pp 249–258.
๏ Gadiraju U., Yang J., Bozzon A. (2017) “Clarity is a Worthwhile Quality – On the Role of Task Clarity in Microtask Crowdsourcing“ Proceedings of the 28th ACM
Conference on Hypertext and Social Media (HT2017). Prague, Czech Republic. ACM. Best Paper Award
๏ Psyllidis, A., Yang., J., Bozzon, A. (2017). Using Machine Learning on Twitter Data to Regionalize Social Interactions and Predict New POI Locations. PLoS ONE (under
review)
๏ Psyllidis, A. (2016). Revisiting Urban Dynamics through Social Urban Data: Methods and tools for data integration, visualization, and exploratory analysis to understand
the spatiotemporal dynamics of human activity in cities. PhD dissertation. A+BE | Architecture and the Built Environment, Delft. doi: http://dx.doi.org/10.7480/abe.
2016.18
๏ Yang J., Bozzon A., Houben G-J. (2015) “Harnessing Engagement for Knowledge Creation Acceleration in Collaborative QA Systems”, Proceedings of 23rd
International Conference on User Modelling, Adaption and Personalisation. (UMAP 2015). Dublin, Ireland, June 29 – July 3, 2015. pp 315-327
๏ Psyllidis, A., Bozzon, A., Bocconi, S., Bolivar, C. T. (2015a). Harnessing Heterogeneous Social Data to Explore, Monitor, and Visualize Urban Dynamics. In: Ferreira J
Jr, Goodspeed R (eds) Planning Support Systems and Smart Cities: Proceedings of the 14th International Conference on Computers in Urban Planning and Urban
Management (CUPUM 2015). MIT, Cambridgre, MA, USA, pp. 239-1 — 239-22.
๏ Psyllidis, A., Bozzon, A., Bocconi, S., Bolivar, C. T. (2015b). A Platform for Urban Analytics and Semantic Integration in City Planning. In: Celani G, Moreno Sperling D,
Franco JMS (eds) Computer-Aided Architectural Design Futures – New Technologies and the Future of the Built Environment: 16th International Conference (CAAD
Futures 2015) – Selected Papers. LNCS, CCIS 527, Springer, Berlin Heidelberg, pp. 21—36. doi: http://dx.doi.org/10.1007/978-3-662-47386-3_2
๏ Bocconi, S., Bozzon, A., Psyllidis, A., Bolivar, C. T. (2015). SocialGlass: A Platform for Urban Analytics and Decision-making Through Heterogeneous Social Data. In:
Gangemi A, Leonardi S, Panconesi A (eds) 24th International Word Wide Web Conference (WWW 2015). ACM, New York, NY, pp. 175—178. doi: http://dx.doi.org/
10.1145/2740908.2742826
๏ Yang J., Tao K., Bozzon A., Houben G-J. (2014) “Sparrows and Owls: Characterisation of Expert Be- haviour in StackOverﬂow”, Proceedings of 22nd International
Conference on User Modeling, Adaption and Personalization. (UMAP 2014), Aalborg, Denmark, July 7-11, 2014. Pages 266-277
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