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Extracting City Traffic Events from Social Streams


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Cities are composed of complex systems with physical, cyber, and social components. Current works on extracting and understanding city events mainly rely on technology enabled infrastructure to observe and record events. In this work, we propose an approach to leverage citizen observations of various city systems and services such as traffic, public transport, water supply, weather, sewage, and public safety as a source of city events. We investigate the feasibility of using such textual streams for extracting city events from annotated text. We formalize the problem of annotating social streams such as microblogs as a sequence labeling problem. We present a novel training data creation process for training sequence labeling models. Our automatic training data creation process utilizes instance level domain knowledge (e.g., locations in a city, possible event terms). We compare this automated annotation process to a state-of-the-art tool that needs manually created training data and show that it has comparable performance in annotation tasks. An aggregation algorithm is then presented for event extraction from annotated text. We carry out a comprehensive evaluation of the event annotation and event extraction on a real-world dataset consisting of event reports and tweets collected over four months from San Francisco Bay Area. The evaluation results are promising and provide insights into the utility of social stream for extracting city events.

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Extracting City Traffic Events from Social Streams

  1. 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 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
  2. 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 1 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. 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. 4. Image credit: Image credit: Life in a City
  5. 5. Image credit: Public Safety Urban planning Gov. & agency admin. Energy & water Environmental Transportation Social Programs Healthcare Education Pulse of a City (CityPulse)
  6. 6. What are People Talking About City Infrastructure on Twitter?
  7. 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. 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. 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. 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. 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., 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. 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. 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. 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. 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 hierarchy City Event Extraction City Event Extraction Solution Architecture City Event Annotation
  16. 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. 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 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 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 O B-LOCATION I-LOCATION B-EVENT I-EVENT O Tags used in our approach:
  18. 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. 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. 20. City Event Extraction – Metadata Population Algorithm
  21. 21. City Event Extraction – Event Aggregation Algorithm Event metadata inference Spatial filtering Grouping Events by types
  22. 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. 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. 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. 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. 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. 27. Evaluation – Use Aggregation Algorithm for Event Extraction
  28. 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. 29. Evaluation – Extracted Events AND Ground Truth Verification Complementary Events Complementary Events
  30. 30. Evaluation – Extracted Events AND Ground Truth Verification Corroborative Events Corroborative Events
  31. 31. Evaluation – Extracted Events AND Ground Truth Verification Corroborative Events
  32. 32. Evaluation – Extracted Events AND Ground Truth Verification Corroborative Events Complementary Events
  33. 33. Evaluation – Extracted Events AND Ground Truth Verification Timeliness Timeliness
  34. 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. 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). [Commentz-Walter 1979] Beate Commentz-Walter. 1979. A string matching algorithm fast on the average. Springer. References