The document discusses the extraction of city traffic events from social media, particularly Twitter, utilizing various techniques such as sequence labeling and clustering to identify events related to city infrastructure. It outlines the challenges of analyzing informal data and presents solutions for event extraction, emphasizing the importance of domain knowledge for robust analysis. The findings indicate that extracted events can complement official reports, providing timely and corroborative insights into urban conditions.

1. Extracting City Events from Social Streams
Pramod Anantharam
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA
http://www.ict-citypulse.eu/page/
Collaborator: Dr. Payam Barnaghi
Advisors: Prof. Amit Sheth, Prof. Krishnaprasad Thirunarayan
Pramod Anantharam, Payam Barnaghi, Krishnaprasad Thirunarayan, and Amit Sheth. 2015. Extracting City Traffic Events
from Social Streams. ACM Trans. Intell. Syst. Technol. 6, 4, Article 43 (July 2015), 27 pages. DOI=10.1145/2717317
http://doi.acm.org/10.1145/2717317
http://wiki.knoesis.org/index.php/Citypulse

2. A Historical Perspective on Cities and its Inhabitants
“kings, emperors and other rulers benefited from being on the front lines with their
people when it came to making decisions.”1
1http://gicoaches.com/what-we-can-learn-from-kings-of-the-past-who-disguised-themselves-as-ordinary-men/
http://en.wikipedia.org/wiki/Qianlong_Emperor
Qianlong Emperor (8 October 1735 – 9 February 1796)
Qing Dynasty (1644–1912)
Disguised as a commoner, Qianlong visited
cities to understand a common man’s life
This is popularly known as “Management by
Walking Around” since the 1980’s

3. A Modern Perspective on Cities and its Inhabitants
City authorities, government and other humanitarian agencies are benefited from
being on the front lines with their people when it comes to making decisions.
We want to be connected to citizens to
understand and prioritize decisions

4. Image credit: http://www-03.ibm.com/software/products/us/en/intelligent-operations-center
Image credit: http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/index.html
Life in a City

5. Image credit: http://www.ibm.com/smarterplanet/us/en/smarter_cities/overview/index.html
Public Safety Urban planning Gov. & agency
admin.
Energy &
water
Environmental Transportation Social Programs Healthcare Education
Pulse of a City (CityPulse)

6. What are People Talking About City Infrastructure on Twitter?

7. • What are people talking about city
infrastructure on twitter?
• How do we extract city infrastructure related
events from twitter?
• How can we leverage event and location
knowledge bases for event extraction?
• How well can we extract city events?
Research Questions

8. Some Challenges in Extracting Events from Tweets
• No well accepted definition of ‘events related to a
city’
• Tweets are short (140 characters) and its informal
nature make it hard to analyze
– Entity, location, time, and type of an event
• Multiple reports of the same event and sparse
report of some events (biased sample)
– Numbers don’t necessarily indicate intensity
• Validation of the solution is hard due to the open
domain nature of the problem

9. Formal Text Informal Text
Closed Domain
Open Domain [Roitman et al. 2012][Kumaran and Allan 2004]
[Lampos and Cristianini 2012]
[Becker et al. 2011]
[Wang et al. 2012]
[Ritter et al. 2012]
Related Work on Event Extraction

10. • N-grams + Regression
– Text analysis to extract uni- and bi-grams (event markers)
– Feature selection to select best possible event markers
– Apply regression to estimate conditional probability P(Y|X) to enable
prediction where Y is the target (e.g., rainfall) and X is the input (e.g., traffic
jam event).
• Clustering
– Create event clusters incrementally over time
– Identify clusters of interest based on its relevance (manual inspection)
– Granularity remains at the tweet/cluster level (tweets are assigned to clusters
of interest)
• Sequence Labeling (CRFs)
– Text analysis to extract features such as named entities, POS1 tagging
– Each event indicator is modeled as a mixture of event types that are latent
variables
– Each type corresponds to a distribution over named entities n (labels assigned
to event types by manual inspection) and other features
Event Extraction -- Techniques
1Part Of Speech

11. • Event extraction should be open domain (no a
priori event types) preferably with event
metadata (e.g., event duration, impact).
• Incorporate background knowledge related to
city events e.g., 511.org hierarchy, SCRIBE
ontology, city location names.
• Assess the intensity of an event using content and
network cues.
• Be robust w.r.t. to noise, informal nature, and
variability of data.
City Event Extraction -- Desiderata

12. • N-grams + Regression
– Open domain: works best when there is a reference
corpus to extract n-grams
– Event metadata: cannot distinguish between entities
and hence hard to extract event metadata
– Background knowledge: incorporating domain
vocabulary (e.g., subsumption) is not natural
– Event intensity: regression maps the event indicators
to some quantified values
– Robustness: quite robust if there is a reference corpus
Techniques -- Desiderata

13. • Clustering
– Open domain: works well for domains with no a priori
knowledge of events (may need human inspection)
– Event metadata: too coarse grained (document level)
and event metadata extraction is not natural
– Background knowledge: incorporating domain
vocabulary is not natural
– Event intensity: not captured
– Robustness: quite robust for twitter data with enough
data for each cluster
Techniques -- Desiderata

14. • Sequence Labeling (CRFs)
– Open domain: works well for domains with no a priori
knowledge of events (may need human inspection)
– Event metadata: event metadata extraction is
captured naturally with the named entities
– Background knowledge: incorporating domain
vocabulary is quite natural
– Event intensity: part-of-speech tag may indirectly
capture intensity
– Robustness: with a deeper model for capturing
context, quite robust for twitter data
Techniques -- Desiderata

15. City Infrastructure
Tweets from a city
POS
Tagging
Hybrid NER+
Event term
extraction
Geohashing
Temporal
Estimation
Impact
Assessment
Event
Aggregation
OSM
Locations
SCRIBE
ontology
511.org hierarchy
City Event Extraction
City Event Extraction Solution Architecture
City Event Annotation

16. tag
1
tag
2
tag
3
token
1
token
2
token
3
Φ1
1(tag1,tag2) Φ1
2(tag2,tag3)
Φ2
1(tag1,token1) Φ2
2(tag2,token2) Φ2
3(tag3, token3)
t1 t2
T1 T2 T3
Training data with tokens and tags
A General CRF Model Regression Based Implementation of CRF Model
t2 t2
T4 T5 T6
t3
t1
City Event Annotation – CRF Formalization
The global normalization distinguishes CRFs from
other models allowing for factoring in long distance dependencies

17. City Event Annotation – CRF Annotation Examples
Last O night O in O CA... O (@ O Half B-LOCATION Moon I-LOCATION Bay B-LOCATION
Brewing I-LOCATION Company O w/ O 8 O others) O http://t.co/w0eGEJjApY O
#Manteca O accident. B-EVENT two O lanes O blocked B-EVENT on O Hwy O 99 O NB
O at O Austin O Rd O #traffic O http://t.co/YehsHpD7aC O
#Fontana O accident. B-EVENT three O lanes O blocked B-EVENT on O I-10 O WB O
between O Cherry B-LOCATION Ave I-LOCATION and O Etiwanda O Ave O in O
#Ontario O #LAtraffic O http://t.co/e2e6MW3d78 O
B-LOCATION
I-LOCATION
B-EVENT
I-EVENT
O
Tags used in our approach:

18. a) Space: events reported within a grid (gi ∈G
where G is a set of all grids in a city)at a certain
time are most likely reporting the same event
b) Time: events reported within a time ∆t in a grid
gi are most likely to be reporting the same event
c) Theme: events with similar entities within a grid
gi and time ∆t are most likely reporting the same
event
City Event Extraction -- Key Insights
We will utilize these principles in the event aggregation algorithm

19. 0.6 miles
Max-lat
Min-lat
Min-long
Max-long
0.38 miles
37.7545166015625, -122.40966796875
37.7490234375, -122.40966796875
37.7545166015625, -122.420654296875
37.7490234375, -122.420654296875
4
37.74933, -122.4106711
Hierarchical spatial structure of geohash for
representing locations with variable precision.
Here the location string is 5H34
0 1 2 3 4 5 6
7 8 9 B C D E
F G H I J K L
0 1
7
2 3 4
5 6 8 9
0 1 2 3 4
5 6 7
0 1 2
3 4 5
6 7 8
City Event Extraction – Geohashing

20. City Event Extraction – Metadata Population Algorithm

21. City Event Extraction
– Event Aggregation Algorithm
Event metadata inference
Spatial filtering
Grouping Events by types

22. • <traffic, 5889, 2013-10-22 19:24:39, 2013-10-23 18:54:08, 19>
• <concert, 5889, 2013-10-20 19:46:06, 2013-10-21 19:06:29, 35>
• <accident, 32400, 2013-10-20 19:51:10, 2013-10-21 15:53:08, 11>
• <parade, 8672, 2013-08-10 12:57:17, 2013-08-10 18:57:21, 11>
City Event Extraction – A Sample of Extracted Events
Location refers to the geohash number which is mapped to lat-long

23. • City Event Annotation
– Automated creation of training data
– Annotation task (our CRF model vs. baseline CRF model)
• City Event Extraction
– Use aggregation algorithm for event extraction
– Extracted events AND ground truth
• Dataset (Aug – Nov 2013) ~ 8 GB of data on disk
– Over 8 million tweets (extract events using Alg. 1 and 2)
– Over 162 million sensor data points (find delays by looking
at change in travel time for links serving as ground truth)
– 311 active events and 170 scheduled events (readily
available events as ground truth)
Evaluation

24. Evaluation – Automated Creation of Training Data (to train CRF model)
Evaluation over 500 randomly chosen tweets from around 8,000 annotated tweets
Aho-Corasick [Commentz-Walter 1979] string matching algorithm implemented by LingPipe [Alias-i 2008]

25. Evaluation – Annotation Task (our CRF model vs. baseline CRF model)
Baseline CRF Model
Our CRF Model
Baseline CRF model (trained on a huge manually
created data) works well on generic tasks.
Our CRF model trained on automatically generated
training data performs on par with the baseline.
Our CRF model does better on the event extraction
task due to the availability of event related
knowledge

26. Ground Truth Data (only incident reports) -- City Event Extraction
We have around 162 million data records from sensors monitoring over 3,700 links in San Franciso Bay Area
<link_id, link_speed, link_volume, link_travel_time,time_stamp>  a data record
GREEN – Active Events
YELLOW – Scheduled Events
311 active events and 170 scheduled events

27. Evaluation – Use Aggregation Algorithm for Event Extraction

28. Evaluation – Extracted Events AND Ground Truth Verification
Evaluation Metric For Comparing Events with Ground Truth:
• Complementary Events
• Additional information
• e.g., slow traffic from sensor data and accident from textual data
• Corroborative Events
• Additional confidence
• e.g., accident event supporting a accident report from ground truth
• Timeliness
• Additional insight
• e.g., knowing poor visibility before formal report from ground truth

29. Evaluation – Extracted Events AND Ground Truth Verification
Complementary Events
Complementary Events

30. Evaluation – Extracted Events AND Ground Truth Verification
Corroborative Events
Corroborative Events

31. Evaluation – Extracted Events AND Ground Truth Verification
Corroborative Events

32. Evaluation – Extracted Events AND Ground Truth Verification
Corroborative Events Complementary Events

33. Evaluation – Extracted Events AND Ground Truth Verification
Timeliness
Timeliness

34. • People in a city indeed talk about various
infrastructure related specifically, traffic.
• City traffic related events can be extracted from
tweets using sequence labeling techniques and
spatial aggregation algorithms.
• Domain knowledge of events and locations can
be utilized to create large training datasets to
train sequence labeling algorithms.
• Traffic events extracted from twitter can be
complementary, corroborative, and timely
compared to formal reports of traffic events.
Conclusion

35. [Kumaran and Allan 2004] Giridhar Kumaran and James Allan. 2004. Text classification and named entities for new
event detection. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development
in information retrieval. ACM, 297–304.
[Lampos and Cristianini 2012] Vasileios Lampos and Nello Cristianini. 2012. Nowcasting events from the social web with
statistical learn- ing. ACM Transactions on Intelligent Systems and Technology (TIST) 3, 4 (2012), 72.
[Roitman et al. 2012] Haggai Roitman, Jonathan Mamou, Sameep Mehta, Aharon Satt, and LV Subramaniam. 2012.
Harnessing the Crowds for smart city sensing. In Proceedings of the 1st international workshop on Multimodal crowd
sensing. ACM, 17–18.
[Ritter et al. 2012] Alan Ritter, Oren Etzioni, Sam Clark, and others. 2012. Open domain event extraction from twitter. In
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1104–
1112.
[Wang et al. 2012] Xiaofeng Wang, Matthew S Gerber, and Donald E Brown. 2012. Automatic crime prediction using
events extracted from twitter posts. In Social Computing, Behavioral-Cultural Modeling and Prediction. Springer, 231–
238.
[Becker et al. 2011] Hila Becker, Mor Naaman, and Luis Gravano. 2011. Beyond Trending Topics: Real-World Event
Identification on Twitter.. In ICWSM.
[Alias-i 2008] Alias-i. 2008. LingPipe 4.1.0. (2008). http://alias-i.com/lingpipe
[Commentz-Walter 1979] Beate Commentz-Walter. 1979. A string matching algorithm fast on the average. Springer.
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