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Towards Deep Learning from Twitter for Improved Tsunami Alerts and Advisories
This document discusses the use of deep learning and natural language processing (NLP) to enhance tsunami alerts through Twitter data analysis. It emphasizes the importance of accurate training data and explores the effectiveness of word embeddings in distinguishing relevant seismic events from unrelated content. The findings indicate a shift towards real-time analysis and applications in disaster scenarios beyond tsunamis.
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Towards Deep Learning from Twitter for Improved Tsunami Alerts and Advisories
- 1. Towards Deep Learningfrom 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. Outline ● Motivation ● PreviousWork ○ Text Classification ● Current Work ○ Natural Language Processing via Word Embeddings ○ Reanalysis of 2 Event Pairs ● Discussion
- 3. Geist, E.L., Titov,V.V., and Synolakis, C.E., 2006, Tsunami: wave of change: Scientific American, v. 294, p. 56-63
- 4. Data extracted fromTwitter via a Perl script that targets #earthquake Lumb & Freemantle, http://credit.pvamu.edu/MCBDA2016/Slides/Day2_Lumb_MCBDA1_Twitter_Tsunami.pdf
- 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. Word Vectors https://adriancolyer.files.wordpress.com/2016/04/word2vec-distributed-representation.png?w=600 "... aword 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. “earthquake” and its ‘closest’20 words Lumb & Freemantle, HPCS 2017, http://2017.hpcs.ca/ (accepted).
- 8. Word-Vector Workflow: NLPvia GloVe + PyTorch http://pytorch.org https://nlp.stanford.edu/projects/glove/ Lumb & Freemantle, HPCS 2017, http://2017.hpcs.ca/ (accepted).
- 9. Pre-Trained Vectors HammyTweets 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. Event Pairs Selectedfor 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. Pre-Trained Vectors Tohoku3/11/2011 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. “earthquake-tsunami” Similarity 0 1 GloVe6B 0.8255 Twitter 27B 0.009244 Tohoku 0.7161 Miyagi -0.2540 Chiapas 0.3156 Central Mexico -0.001964 Vector size = 50
- 13. Discussion ● Embedded wordvectors 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.
- 15. if ( EARTHQUAKE) then { TSUNAMI }
- 16. if ( Mw> 8.0 and TRENCH and DISPLACEMENT and DEEP WATER ) then { TSUNAMI }
- 18. Q&A L. I. Lumb1& J. R. Freemantle2 1ianlumb@yorku.ca & 2james.freemantle@rogers.com
- 19.
- 20. 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
- 21. After Karau etal., Learning Spark, O’Reilly, 2015 “earthquake” Spam Classification via Apache Spark
- 22. The Opportunity forSemantics ● 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
- 23. 23 http://pytorch.org/about/ www.univa.com PyTorch ● Python packagethat 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)
- 24.