Weaving the Web of People and Things for Intelligent Cities

1. 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

3. 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

4. Alessandro Bozzon HComp-NL Symposium 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

5. Taming the complexity of contemporary cities

6. Alessandro Bozzon HComp-NL Symposium 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)

7. Alessandro Bozzon HComp-NL Symposium 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

8. Alessandro Bozzon HComp-NL Symposium 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.)

9. Alessandro Bozzon HComp-NL Symposium 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

10. Alessandro Bozzon HComp-NL Symposium …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

11. Alessandro Bozzon HComp-NL Symposium 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

12. Alessandro Bozzon HComp-NL Symposium 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

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

15. Alessandro Bozzon HComp-NL Symposium 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

16. Alessandro Bozzon HComp-NL Symposium 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

17. Alessandro Bozzon HComp-NL Symposium The SocialGlass System: Backend 17 Crowd- sourcing

18. Alessandro Bozzon HComp-NL Symposium The SocialGlass System: Frontend 18

19. Alessandro Bozzon HComp-NL Symposium The SocialGlass System: IoT / 1 19 Attribution: Fluxedo S.r.l. IN: OUT:

20. Alessandro Bozzon HComp-NL Symposium The SocialGlass System: IoT / 2 20

21. Alessandro Bozzon HComp-NL Symposium The SocialGlass System: IoT / 3 ๏ 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

22. Alessandro Bozzon HComp-NL Symposium The SocialGlass System SocialGlass: choropleth map of prevalent social activities, as inferred from Instagram (http://www.social-glass.org) 22

23. Alessandro Bozzon HComp-NL Symposium The SocialGlass System SocialGlass: activity patterns of Amsterdam residents (ALF 2015) (http://www.social-glass.org) 23

24. Alessandro Bozzon HComp-NL Symposium The SocialGlass System SocialGlass: activity patterns of Amsterdam foreign tourists (ALF 2015) (http://www.social-glass.org) 24

25. Alessandro Bozzon HComp-NL Symposium The SocialGlass System SocialGlass: Flows of residents (http://www.social-glass.org) 25

27. Alessandro Bozzon HComp-NL Symposium 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

28. Alessandro Bozzon HComp-NL Symposium SAIL 2015 28

29. Alessandro Bozzon HComp-NL Symposium 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

30. Alessandro Bozzon HComp-NL Symposium 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

32. Alessandro Bozzon HComp-NL Symposium SAIL 2015 32 Cameras WiFi sensors Social mediavs vs

33. Alessandro Bozzon HComp-NL Symposium SAIL 2015 33 Social media GPSvs

34. Alessandro Bozzon HComp-NL Symposium SAIl 2015 34 Geo-demographic analysis of social activity dynamics

35. Alessandro Bozzon HComp-NL Symposium SAIl 2015 35 Foreign Tourists Residents Druk, vol, gedrang, bomvol, boordevol, afgeladen, volgepakt, crowded, busy, jam

36. Alessandro Bozzon HComp-NL Symposium SAIL 2015 36 StadinBalans.KarlaGutierrez,EricvanderKooij

37. Alessandro Bozzon HComp-NL Symposium 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

38. Alessandro Bozzon HComp-NL Symposium Regionalisation & POI location prediction How could we detect neighbourhoods of uniform social interaction and predict new POI locations? 38

39. Alessandro Bozzon HComp-NL Symposium Regionalisation & POI location prediction 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. Alessandro Bozzon HComp-NL Symposium Regionalisation & POI location prediction 40 *Psyllidis et al. (2017) Residents’ clusterLeisure cluster Tourists’ cluster Arabic language clusterTransport / Nightlife cluster Work-related activities cluster

41. Alessandro Bozzon HComp-NL Symposium Regionalisation & POI location prediction 41 *Psyllidis et al. (2017) Event spaceCafé Gym RestaurantHotel Tram stop

42. Humans in the Urban Knowledge Loop The Building Blocks

43. Alessandro Bozzon HComp-NL Symposium Crowdsourcing in Cities: Why? ๏ Gather (ground truth) data ๏ Monitoring ๏ Situation Assessment ๏ Veriﬁcation ๏ Gather opinions ๏ Instruct citizens 43

44. Alessandro Bozzon HComp-NL Symposium 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

45. Alessandro Bozzon HComp-NL Symposium Crowdsourcing in Cities: How? ๏ Participatory Sensing ๏ Collective sensing ๏ Crowd sensing ๏ (IoT) Sensors ๏ GPS, Audio, Temperature ๏ Social Media ๏ Microtasking 45 Micromappers

46. Alessandro Bozzon HComp-NL Symposium 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

47. Alessandro Bozzon HComp-NL Symposium 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

48. Alessandro Bozzon HComp-NL Symposium 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

49. Alessandro Bozzon HComp-NL Symposium 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

50. Alessandro Bozzon HComp-NL Symposium 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

51. Alessandro Bozzon HComp-NL Symposium 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

52. Alessandro Bozzon HComp-NL Symposium 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

53. Alessandro Bozzon HComp-NL Symposium 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 topics 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 negatively) 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

54. Alessandro Bozzon HComp-NL Symposium 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 throughput ([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 prediction purposes. The negative correlation indicates the presence of features that, while hinting to higher task complexity, 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 complexity and performance prediction. For instance transcribe audio 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 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, 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 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 importance of features in complexity prediction and in performance prediction. Results show a weak negative correlation of feature importance. Significance (Pearson correlation: -0.1308; p < 0.05) is present only for low throughput ([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 prediction purposes. The negative correlation indicates the presence of features that, while hinting to higher task complexity, 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

55. Alessandro Bozzon HComp-NL Symposium 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

56. Alessandro Bozzon HComp-NL Symposium Clarity of Human Computation Tasks 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 ● ● ● ● ●● ● ● ●● ● ● ●●●●● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●●●●●● ● ●●● ●●●●●●●● ●●●●●● ●●● ●● ●● ●●● ● ●●●● ●●●●● ●●●● 1 2 3 4 5 12345 Goal Clarity TaskClarity ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● 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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.

57. Alessandro Bozzon HComp-NL Symposium Clarity of Human Computation 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

58. Ongoing Work Ideas and Open Challenges

59. Alessandro Bozzon HComp-NL Symposium The tyranny of geography ๏ Location is everything ๏ Even in the online world ๏ How can we exploit this? 59

60. Alessandro Bozzon HComp-NL Symposium 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

61. Alessandro Bozzon HComp-NL Symposium Chatbot with Human in the Loop /2 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

62. Alessandro Bozzon HComp-NL Symposium Chatbot with Human in the Loop /3 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

63. Alessandro Bozzon HComp-NL Symposium 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 63

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Weaving the Web of People and Things for Intelligent Cities