Trondheim bigdata Talk

Geo-Temporal-(Social?) Data?!Social Events in Social Media!

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Massimiliano Ruocco!

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@ruoccoma!ruoccoma dot gmail dot com!Telenor Digital (SWEng), NTNU (PhD)!

Who am I?

Digital Footprint

Social

Geographical

Temporal

Scenario 1 User visiting a touristic spot. Takes a picture of it. Posts it (+ comments) on FB/Flickr/Twitter.

Scenario 2 User watching a football match at the stadium. Takes a picture of the match (+ comments). Posts it on FB/Flickr/Twitter

Scenario 3 User reading newspaper. Comments some trending facts (i.e.: crisis in Middle East). Posts it Twitter.

Scenario 1 User visiting a touristic spot. Takes a picture of it. Posts it (+ comments) on FB/Flickr/Twitter.

Scenario 2 User watching a football match at the stadium. Takes a picture of the match (+ comments). Posts it on FB/Flickr/Twitter

Scenario 3 User reading newspaper. Comments some trending facts Comments some trending facts (i.e.: crisis in Middle East). Posts it Twitter.

Event! <<my trip in Naples>>

Event! <<semifinal CL>>

Event! <<crisis in middle east>>

Events in Social Media From raw data to events

Flickr as data source +250M geotagged

3.5M uploaded/day 87M users

6.000M pics

POI-related Tag Extraction

POI-related Tag Extraction Tag Point Pattern Geo distribution of pictures tagged with a certain term

Point Process Theory Extended rigorous statistic


Point Pattern Analysis Objective Determine If a given set of spa1al points (Spa1al Point Pa6ern) exhibits clustering, regularity or are randomly distributed within an area A

POI-related Tag Extraction Ripley’s K-function Summarizing a spa1al point pa6ern over a scale h

CSR Test -‐  K(h) >πh2 clustering at scale h -‐  K(h) <πh2 dispersion at scale h

POI-related Tag Extraction Ripley’s K-function Summarizing a spa1al point pa6ern over a scale h

CSR Test -‐  D(h) >h clustering at scale h -‐  D(h) <h dispersion at scale h

POI-related Tag Extraction Ripley’s Cross-K-function Summarizing a spa1al correla1on over two tag point pa6ern over a scale h

Spa1al distribu1on of the Tag Point Pa6erns related to the tag Old Naval College and the tag University of Greenwich at two different zooming

POI-related Tag Extraction Ripley’s Cross-K-function Summarizing a spa1al correla1on over two tag point pa6ern over a scale h

Spa1al distribu1on of the Tag Point Pa6erns related to the tag Old Naval College and the tag University of Greenwich at two different zooming

POI-related Tag Extraction Ripley’s Cross-K-function Summarizing a spa1al correla1on over two tag point pa6ern over a scale h CSR Test

-‐  L12(h) >0 a6rac1on at scale h -‐  L12(h) <0 repulsion at scale h

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Spa1al distribu1on of the Tag Point Pa6erns related to the tag Old Naval College and the tag University of Greenwich !

POI-related Tag Extraction Ripley’s Cross-K-function Summarizing a spa1al correla1on over two tag point pa6ern over a scale h CSR Test

-‐  K12(h) >πh2 a6rac1on at scale h -‐  K12(h) <πh2 repulsion at scale h

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Spa1al distribu1on of the Tag Point Pa6erns related to the tag Old Naval College and the tag University of Greenwich !


Objective

Derive indicators es1ma1ng clustering tendency of Tag-‐point pa6ern

Applications

1 -‐ Extrac2ng/Ranking social tags indica1ng geographical POI 2 -‐Enhance query expansion in combina1on with other metadata

Size

Inhomogeneity

POI-related Tag Extraction Real Data: Challenges

Example of point pattern of the tag night !

Data Inhomogeneity

Data Inhomogeneity

Related underlying Picture Point Pattern !

Data Inhomogeneity

Example of point pattern of the tag night over the underlying distribution!

Size

1 -‐ Subsampling2-‐ bigmatrix* and biganalytics** (R)

Inhomogeneity

Case-‐Control Analysis

POI-related Tag Extraction Real Data: Challenges

*Kane M., Emerson J., “The R Package bigmemory: Suppor2ng Efficient Computa2on and Concurrent Programming with Large Data Sets” (2010). Journal of Sta1s1cal SoVware.

**Kane M. et al., “Scalable Strategies for Compu2ng with Massive Data” (2013), Journal of Sta1sc1cal SoVware.


2 -‐ Maximum func1on value K(h) over the scale

1 -‐ Area underlying K(h) in the considered scale

Derived Geo-Features

Set 1

Set 2

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wwt! grdstreeteatportlan! britishlibrary! astoria!


Table - Top-5 tags extracted ranked by MaxValue and Area

Event-related Image Search Geo(Temporal)-tagged resources supporting IR

Event-related Image Search Geo(Temporal)-tagged resources supporting IR

Event-related Image Search Expansion terms selection over three dimensions

Text Features (baseline) -‐  TF, IDF, DF

Time Features -‐  Kurtosis: Peakdness -‐  Autocorrela-on: Randomness -‐  Cross-‐Correla-on

Geo Features -‐  Good expansion = spa1ally correlated with qi

-‐  Calculated for each 1le Tqi ,e

Derived from q-‐point pa6er & e-‐point pa6ern & (q+e)-‐point pa6ern

Event-related Image Search Scalability?


Which Tile? 1 - Best tile + calculate confidence value 2 - Confidence values combination from different tiles:

Map Reduce fashion + Solr Search engine


Event-related Image Search Results

Table – Comparison of the classification performances. The best scores in each column are type-set boldface.

Event-related Image Search Results

Fig – Comparison of MAP improvements as function of number of feedback docs

Yes! But…BigData? •  Bigmatrix + Biganalytics in R •  Subsampling •  World map divided in tiles •  Map-Reduce fashion algorithm

Cool stuff! Increasing volume of Geo-Temporal Data from Social Media ++

Amazing things! –  Visualization –  Location-based recommendation –  Dicovering trends!

Thanks! Questions?

M. Ruocco and H. Ramampiaro, (2014), "Geo-‐Temporal Distribu2on of Tag Terms for Event-‐Related Image Retrieval". In Informa1on Processing & Management Journal (IPM). Elsevier.

M. Ruocco and H. Ramampiaro, (2014), "A Scalable Algorithm for Extrac2on and Clustering of Event-‐Related Pictures". In Mul1media Tools and Applica1ons Journal (MTAP), Springer.

M. Ruocco and H. Ramampiaro, (2013), "Exploring Temporal Proximity and Spa2al Distribu2on of Terms in Web-‐based Search of Event-‐Related Images". In Proc. of the 24th ACM Conference on Hypertext and Social Media (HT 2013). ACM Press.

M. Ruocco and H. Ramampiaro, (2012), "Exploratory Analysis on Heterogeneous Tag-‐Point PaQerns for Ranking and Extrac2ng Hot-‐Spot Related Tags". Proceedings of the 5th ACM SIGSPATIAL Interna1onal Workshop on Loca1on-‐Based Social Networks (LBSN 2012). ACM Press.

[email protected]!


Evaluation of top-100 extracted tags: P@n