1. Social Event Detection (SED):
Challenges, Dataset and Evaluation
Raphaël Troncy <raphael.troncy@eurecom.fr>
Vasileios Mezaris <bmezaris@iti.gr>
Symeon Papadopoulos <papadop@iti.gr>
Emmanouil Schinas <manosetro@iti.gr>
Ioannis Kompatsiaris <ikom@iti.gr>

2. What are Events?
Events are observable occurrences grouping
People Places Time
Experiences documented by Media
04/10/2012 - Social Event Detection (SED) Task - MediaEval 2012, Pisa, Italy -2

3. SED: bigger, longer, harder
 In 2011  In 2012
 2 challenges  3 challenges
 73k photos (2,43 Gb) 1 from SED 2011
 No training dataset  167k photos (5,5 Gb)
cc licence check
 18 teams interested
 7 teams submitted runs  Training dataset =
SED 2011
 Considered easy  21 teams interested
 F-measure = 85% … from 15 countries
(challenge 1)  5 teams submitted runs
 F-measure = 69%
(challenge 2)
 Much harder !
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4. Three challenges (type and venue)
1. Find all technical events that took place in Germany in
the test collection.
2. Find all soccer events taking place in Hamburg
(Germany) and Madrid (Spain) in the collection.
3. Find all demonstration and protest events of the
Indignados movement occurring in public places in
Madrid in the collection
 For each event, we provided relevant and non relevant
example photos
 Task = detect events and provide all illustrating photos
04/10/2012 - Social Event Detection (SED) Task - MediaEval 2012, Pisa, Italy -4

5. Dataset Construction
 Collect 167332 Flickr Photos (Jan 2009-Dec 2011)
 4,422 unique Flickr users, all in CC licence
 All geo-tagged in 5 cities: Barcelona (72255), Cologne
(15850), Hannover (2823), Hamburg (16958), Madrid
(59043) + 0,22 % (403) from EventMedia
 Altered metadata:
 geo-tags removed for 80% of the photos (random)
 33466 photos still geo-tagged
 Provide only metadata … but real media were
available to participants if they asked (5,5 Gb)
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6. Ground Truth and Evaluation Measures
 CrEve annotation tool: http://www.clusttour.gr/creve/
 For each of the 6 collections, review all photos and
associate them to events (that have to be created)
 Search by text, geo-coordinates, date and user
 Review annotations made by others
 Use EventMedia and machine tags (upcoming:event=xxx)
 Evaluation Measures:
 Harmonic mean (F-score of Precision and Recall)
 Normalized Mutual Information (NMI): jointly consider the
goodness of the photos retrieved and their correct
assignment to different events
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7. What ideally should be found
 Challenge 1:
 19 events, 2234 photos (avg = 117)
 Baseline precision (random): 0,01%
 Challenge 2:
 79 events, 1684 photos (avg = 21)
 Baseline precision (random): 0,01%
 Challenge 3:
 52 events, 3992 Photos (avg = 77)
 Baseline precision (random): 0,02%
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8. Who Has Participated ?
 21 Teams registered (18 in 2011)
 5 Teams cross the lines (7 in 2011, 2 overlaps)
 One participant missing at the workshop!
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9. Quick Summary of Approaches
 2011: all but 1 participants use background knowledge
 Last.fm (all), Fbleague (EURECOM), PlayerHistory (QMUL)
 DBpedia, Freebase, Geonames, WordNet
 2012: all but 2 participants use a generic approach
 IR approach: query matching clusters (metadata, temporal, spatial):
MISIMIS
 Classification approach:
 Topic detection with LDA, city classification with TF-IDF, event detection using
peaks in timeline using the query topics: AUTH-ISSEL
 Learning model using the training data and SVM: CERTH-ITI
 Background knowledge: QMUL, DISI
 2012: all approaches are NOT fully automatic
 Manual selection of some parameters (e.g. topics)
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10. Results – Challenge 1 (Technical Events)
Precision Recall F-score NMI
AUTHISSEL_4 76,29 94,9 84,58 0,7238
CERTH_1 43,11 11,91 18,66 0,1877
DISI_1 86,23 59,13 70,15 0,6011
MISIMS_2 2,52 1,88 2,15 0,0236
QMUL_4 3,86 12,85 5,93 0,0475
90 84.58
80
70.15
70
60
50
40
30
18.66
20
10 5.93
2.15
0
Runs
AUTHISSEL_4 CERTHITI_1 DISI_1 MISIMS_2 QMUL_4
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11. Results – Challenge 2 (Soccer Events)
Precision Recall F-score NMI
AUTHISSEL_4 88,18 93,49 90,76 0,8499
CERTH_1 85,57 66,19 74,64 0,6745
DISI_1
MISIMS_2 34,49 17,25 22,99 0,1993
QMUL_4 79,04 67,12 72,59 0,6493
100
90.76
90
80 74.64 72.59
70
60
50
40
30 22.99
20
10
0
Runs
AUTHISSEL_4 CERTHITI_3 DISI_1 MISIMS_2 QMUL_1
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12. Results – Challenge 3 (Indignados Events)
Precision Recall F-score NMI
AUTHISSEL_4 88,91 90,78 89,83 0,738
CERTH_1 86,24 54,61 66,87 0,4654
DISI_1 86,15 47,17 60,96 0,4465
MISIMS_2 48,3 46,87 47,58 0,3088
QMUL_4 22,88 33,48 27,19 0,1988
100
89.83
90
80
70 66.87
60.96
60
47.58
50
40
27.19
30
20
10
0
Runs
AUTHISSEL_4 CERTHITI_3 DISI_1 MISIMS_2 QMUL_4
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13. Conclusion
 Lessons learned
 Clear winner for all tasks: generic approach but manual
selection of the topics
 Use of background knowledge still useful if well-used
 Looking at next year SED
 Shlomo Geva (Queensland University of Technology) +
Philipp Cimiano (University of Bielefeld)
 Dataset: bigger, more diverse
 Media: photos and videos ? (at least 10% videos?)
 Metadata: include some social network relationships,
participation at events
 Evaluation measures: event granularity? Time/CPU?
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