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Towards Deep Learning from Twitter for Improved Tsunami Alerts and Advisories

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Slides corresponding to an oral presentation made at the 2017 Fall Meeting of the American Geophysical Union in New Orleans, Louisiana. The full abstract can be found at https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/279699. The gist? Use is made of Natural Language Processing (NLP) to explore semantic similarities in word use for data extracted via Twitter.

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Towards Deep Learning from Twitter for Improved Tsunami Alerts and Advisories

  1. 1. Towards Deep Learning from Twitter for Improved Tsunami Alerts and Advisories L. I. Lumb1 & J. R. Freemantle2 1York University & 2Independent NH14A-03, 2017 AGU Fall Meeting New Orleans, LA; December 11, 2017
  2. 2. Outline ● Motivation ● Previous Work ○ Text Classification ● Current Work ○ Natural Language Processing via Word Embeddings ○ Reanalysis of 2 Event Pairs ● Discussion
  3. 3. Geist, E.L., Titov, V.V., and Synolakis, C.E., 2006, Tsunami: wave of change: Scientific American, v. 294, p. 56-63
  4. 4. Data extracted from Twitter via a Perl script that targets #earthquake Lumb & Freemantle, http://credit.pvamu.edu/MCBDA2016/Slides/Day2_Lumb_MCBDA1_Twitter_Tsunami.pdf
  5. 5. ● Twitter metadata (handles, hashtags and URLs) contributes equally to Twitter data (unstructured text that comprises the body of a Tweet) in constructing feature vectors - i.e., the semantic value of Twitter metadata is ignored ● Curation of training data is extremely important (e.g., accuracy), but also extremely time consuming as this supervised learning is a manual process ● “earthquake” can be used in different contexts (e.g., geophysics vs. movies vs. politics …) and have a ‘subtly’ different meanings 5 Key Takeaways of “earthquake” Spam Classification Lumb & Freemantle, http://credit.pvamu.edu/MCBDA2016/Slides/Day2_Lumb_MCBDA1_Twitter_Tsunami.pdf
  6. 6. Word Vectors https://adriancolyer.files.wordpress.com/2016/04/word2vec-distributed-representation.png?w=600 "... a word is characterized by the company it keeps ..." Firth (1957) Firth, J.R. (1957). "A synopsis of linguistic theory 1930-1955". Studies in Linguistic Analysis. Oxford: Philological Society: 1–32. Reprinted in F.R. Palmer, ed. (1968). Selected Papers of J.R. Firth 1952-1959. London: Longman.
  7. 7. “earthquake” and its ‘closest’ 20 words Lumb & Freemantle, HPCS 2017, http://2017.hpcs.ca/ (accepted).
  8. 8. Word-Vector Workflow: NLP via GloVe + PyTorch http://pytorch.org https://nlp.stanford.edu/projects/glove/ Lumb & Freemantle, HPCS 2017, http://2017.hpcs.ca/ (accepted).
  9. 9. Pre-Trained Vectors Hammy Tweets Spammy Tweets GloVe 6B 0.1182 0.0097481 Twitter 27B -0.033930 -0.064906 Preliminary Results: “earthquake” Cosine Similarities GloVe 6B = Wikipedia 2014 + Gigaword 5, 6B tokens, 400K vocab, uncased, 50d Twitter 27B = 2B tweets, 27B tokens, 1.2M vocab, uncased, 50d Lumb & Freemantle, HPCS 2017, http://2017.hpcs.ca/ (accepted).
  10. 10. Event Pairs Selected for Reanalysis Tohoku 05:46 UTC, 11 March 2011 29 km, ~9 Mw earthquake & tsunami Miyagi 14:32 UTC, 7 April 2011 49 km, 7.1 Mw earthquake only Chiapas 04:49 UTC, 8 September 2017 50 km, 8.2 Mw earthquake & tsunami Central Mexico 18:14 UTC, 19 September 2017 51 km, 7.1 Mw earthquake only Curated according to start time ONLY
  11. 11. Pre-Trained Vectors Tohoku 3/11/2011 Miyagi 4/7/2011 GloVe 6B -0.2289 0.06455 Twitter 27B -0.05655 -0.03156 # tweets / # words 1374 / 715 146 / 328 Re-analysis Results: “earthquake” Cosine Similarities GloVe 6B = Wikipedia 2014 + Gigaword 5, 6B tokens, 400K vocab, uncased, 50d Twitter 27B = 2B tweets, 27B tokens, 1.2M vocab, uncased, 50d Pre-Trained Vectors Chiapas 9/8/2017 Central Mexico 9/19/2017 GloVe 6B -0.1306 -0.01169 Twitter 27B 0.1050 0.1273 # tweets / # words 304 / 468 415 / 759
  12. 12. “earthquake-tsunami” Similarity 0 1 GloVe 6B 0.8255 Twitter 27B 0.009244 Tohoku 0.7161 Miyagi -0.2540 Chiapas 0.3156 Central Mexico -0.001964 Vector size = 50
  13. 13. Discussion ● Embedded word vectors superior to text classification in isolating geophysically relevant content ○ Embeddings convey significantly enhanced semantic value over bland features ○ Unsupervised learning replaces manually intensive requirement for close supervision ● Using NLP via embedded word vectors ○ Closest word and inter-corpora cosine similarities prove inconclusive in isolation ○ Intra-corpora cosine similarities (e.g., “earthquake-tsunami”) appear more promising in isolating tsunami-producing earthquakes ○ Word-vector analogies require additional consideration ● Steps towards operationalization ○ Enable shift from offline, reanalysis to online, real-time streaming ○ Focus efforts on the time interval between the earthquake and (potential) arrival of the tsunami ● Applicable in other disaster scenarios - e.g., hurricanes, wildfires, ...
  14. 14. www.univa.com 14 Tsunami Advisories
  15. 15. if ( EARTHQUAKE ) then { TSUNAMI }
  16. 16. if ( Mw > 8.0 and TRENCH and DISPLACEMENT and DEEP WATER ) then { TSUNAMI }
  17. 17. Q&A L. I. Lumb1 & J. R. Freemantle2 1ianlumb@yorku.ca & 2james.freemantle@rogers.com
  18. 18. Additional Content
  19. 19. Motivation ● Non-deterministic cause ○ Uncertainty inherent in any attempt to predict earthquakes ■ In situ measurements may reduce uncertainty ● Lead times ○ Availability of actionable observations ○ Communication of situation - advisories, warnings, etc. ● Cause-effect relationship ○ Energy transfer - inputs ... coupling ... outputs ■ ‘Geometry’ - bathymetry and topography ○ Other factors - e.g., tides ● Established effect ○ Far-field estimates of tsunami propagation (pre-computed) and coastal inundation (real-time) have proven to be extremely accurate ... requires ● Distributed array of deep-ocean tsunami detection buoys + forecasting model
  20. 20. After Karau et al., Learning Spark, O’Reilly, 2015 “earthquake” Spam Classification via Apache Spark
  21. 21. The Opportunity for Semantics ● A feature vector is a feature vector - it is devoid of semantics ● Ignores inherent, overall credibility of a Tweet - e.g., as quantified by TweetCred ● Twitter metadata (handles, hashtags and URLs) contributes equally to Twitter data (unstructured text that comprises the body of a Tweet) in constructing feature vectors - i.e., the semantic value of Twitter metadata is also ignored by Deep Learning ● The W3C’s Resource Description Framework (RDF) facilitates the representation of metadata and thus exposes semantics ● The W3C’s Web Ontology Language (OWL) accounts for domain specifics - disambiguates use of overloaded terms (e.g., “earthquake”) in different contexts (e.g., geophysics vs. movies vs. …) ● Deep Learning in combination with RDF/OWL semantics has the potential to produce learned models with knowledge represented
  22. 22. 23 http://pytorch.org/about/ www.univa.com PyTorch ● Python package that provides ○ Tensor computation – strong GPU acceleration, efficient memory usage ■ Integrated with NVIDIA CuDNN and NCCL libraries ○ Deep Neural Networks built on a tape-based autograd system ● Can leverage numpy, scipy and Cython as needed ● Available tutorials include Natural Language Processing (NLP)
  23. 23. Big Data’s 6Vs 24 http://credit.pvamu.edu/MCBDA2016/Slides/Day2 _Lumb_MCBDA1_Twitter_Tsunami.pdf www.univa.com

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