Symeon Papadopoulos (CERTH)
David Corney (RGU)
Luca Aiello (Yahoo! Labs)
WWW 2014
Seoul, April 8 th

• Goal: Detection of newsworthy topics in a large and noisy set of tweets
• Topic: a news story represented by a headline + tags
+ representative tweets + representative images
(optional)
• Newsworthy: A topic that ends up being covered by at least some major online news sources
• Topics are detected per timeslot (small equally-sized time intervals)
• We want a maximum number of topics per timeslot
#2

• Challenge definition (Dec 2013)
• Challenge toolkit and registration (Jan 20, 2014)
• Development dataset collection (Feb 3, 2014)
• Rehearsal dataset collection (Feb 17, 2014)
• Test dataset collection (Feb 25, 2014)
• Results submission (Mar 4, 2014)
• Paper submission (Mar 9, 2014)
• Results evaluation (Mar 5-18, 2014)
• Workshop (Apr 7, 2014)
#3

• Registered participants: 25
– India: 4, Belgium: 3, Germany: 3, UK: 3, Greece: 3,
Ireland: 2, USA: 2, France: 2, Italy: 1, Spain: 1, Russia: 1
• Participants that signed the Challenge agreement: 19
• Participants that submitted results: 11
• Participants that submitted papers: 9
#4

• Defined several evaluation criteria:
– Newsworthiness  Precision/Recall, F-score
– Readability  scale [1-5]
– Coherence  scale [1-5]
– Diversity  scale [1-5]
• List of reference topics
• Set up precise evaluation guidelines
• Blind evaluation (i.e. evaluator not aware of which method a topic comes from) based on Web UI
• Participants submitted topics for 96 timeslots, but manual evaluation happened for 5 sample timeslots.
• Result validation and analysis
#5

Team
A
B
J
K
G
H
I
C
D
E
F
Recall (%)
0.44
0.58
0.24
0.6
0.17
0.24
0.14
0.32
0.63
0.66
0.39
Rank
5
4
10
8
8
3
11
7
2
1
6
Recall computed with respect to 59 reference topics.
Those were partitioned in three groups (20, 20, 19) and each of the three evaluators manually matched the topics of participants to the topics assigned to him.
Eval. Pair
Eval. 1 – Eval. 2
Eval. 1 – Eval. 3
Eval. 2 – Eval. 3
Correlation
0.894913
0.930247
0.811976
#6

• Each evaluator independently evaluated the topics of each participant as newsworthy or not
• Selected all topics that were marked as newsworthy by at least two evaluators
• Manually extracted the unique topics (70 in total, partially overlapping with reference topic list)
• Manually matched correct topics of each participants to the list of newsworthy topics
• Computed precision, recall and F-score
#7

G
H
I
J
Team Matched Unique Total Prec Rec F-score Rank
A 13 13 27 0.481
0.186
0.268
6
B
C
12
22
12
15
23
50
0.522
0.44
0.171
0.214
0.258
0.288
7
4
D
E
F
18
28
4
14
25
2
39 0.462
0.2
0.279
50 0.56
0.357
0.436
15 0.267
0.029
0.052
5
1
10
4
19
36
1
4
17
15
1
10
49
45
8
0.4
0.057
0.099
0.388
0.243
0.299
0.8
0.214
0.338
0.125
0.014
0.027
9
3
2
11
K 8 7 10 0.8
0.1
0.178
8
#8

J
I
F
G
H
K
Team
A
B
C
D
E
Readability
4.29
4.92
4.49
4.59
4.74
4.18
4.93
4.71
4.8
3.38
4.32
10
1
5
3
11
8
Rank
9
2
7
6
4
#9
Eval. Pair
Eval. 1 – Eval. 2
Eval. 1 – Eval. 3
Eval. 2 – Eval. 3
Correlation
0.902124
0.357733
0.278632

Team
A
B
J
I
K
G
H
C
D
E
F
Coherence
4.4
4.08
4.68
4.91
4.97
4.78
4.83
4.22
3.95
3.75
4.36
Rank
6
9
10
11
7
3
8
5
2
1
4
#10
Eval. Pair
Eval. 1 – Eval. 2
Eval. 1 – Eval. 3
Eval. 2 – Eval. 3
Correlation
0.549512
0.730684
0.684426

Team
A
D
E
B
C
F
G
H
I
J
K
Diversity
2.12
2.36
2.31
2.11
2.11
2
1.92
3.27
2.36
2.5
3.47
Rank
7
8
8
10
4
6
11
2
4
3
1
#11
Eval. Pair
Eval. 1 – Eval. 2
Eval. 1 – Eval. 3
Eval. 2 – Eval. 3
Correlation
0.873365
0.890415
0.905915

J
K
F
G
H
I
Team
A
B
C
D
E
Precision (%)
54.19
31.75
58.09
52.04
27.39
0
0
58.82
0
0
18.45
8
1
8
8
7
Rank
3
5
2
4
6
8
Eval. Pair
Eval. 1 – Eval. 2
Eval. 1 – Eval. 3
Eval. 2 – Eval. 3
Correlation
0.944946
0.919469
0.79596
#12

• For each criterion C i
, we computed the score of each team relative to the best team for this criterion:
C i
* (team) = C i
(team) / max(C i
(team j
))
• We then aggregated over the different norm. scores:
C tot
= 0.25*C ref
*C pool
+ 0.25*C read
+ 0.25*C coh
+ 0.25*C div where C ref topics, C pool is computed from the recall of reference from the F-score of the pooled topics, and C read
, C coh and C div from readability, coherence and diversity respectively.
#13

Team
A
B
C
D
E
F
G
H
I
J
K
Precision (%)
0.694
0.755
0.7099
0.785
0.892
0.614
0.652
0.842
0.662
0.546
0.70987
Rank
7
4
5
3
1
10
9
2
8
11
6
We tried several other alternative aggregation scores. The top three teams were the same!
#14

15:20-15:30: Carlos Martin-Dancausa and Ayse Goker: Real-time topic detection with bursty n-grams.
16:00-16:20: Gopi Chand Nuttaki, Olfa Nasraoui, Behnoush Abdollahi, Mahsa Badami,
Wenlong Sun: Distributed LDA based topic modelling and topic agglomeration in a latent space.
16:20-16:40: Steven van Canneyt, Matthias Feys, Steven Schockaert, Thomas
Demeester, Chris Develder, Bart Dhoedt: Detecting newsworthy topics in Twitter.
16:40-17:00: Georgiana Ifrim, Bichen Shi, Igor Brigadir: Event detection in Twitter using aggressive filtering and hierarchical tweet clustering.
17:00-17:20: Gerard Burnside, Dimitrios Milioris, Philippe Jacquet: One day in Twitter:
Topic detection via joint complexity.
17:20-17:30: Georgios Petkos, Symeon Papadopoulos, Yiannis Kompatsiaris: Two-level message clustering for topic detection in Twitter.
17:30-17:40: Winners’ announcement!
#15

• Did not take into account time
– However, methods that produce a newsworthy topic earlier should be rewarded
• Did not take into account image relevance
– since we considered it an optional field
• Coherence and diversity had extreme values in numerous cases
– e.g. when a single relevant tweet was provided as representative
• Evaluation turned out to be a very complex task!
• Assessing only five slots (out of the 96) is definitely a compromise: (a) consider use of more evaluators/AMT,
(b) consider simpler evaluation tasks
#16

• Release evaluation resources
– list of reference topics
– list of pooled newsworthy topics
– evaluation scores
• Papers
– SNOW Data Challenge paper
– Resubmission of participants’ papers with CEUR style
– Submission to CEUR-ws.org
• Open-source implementations?
• Further plans?
#17

#18