nach oben

Social Network Analysis and Mining

Erschienen in:

01.12.2023 | Original Article

DEES: a real-time system for event extraction from disaster-related web text

verfasst von: Nilani Algiriyage, Raj Prasanna, Kristin Stock, Emma E. H. Doyle, David Johnston

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The rapid growth of Internet-based communication technologies in the form of Social Media (SM) and associated mobile applications has enabled people to share information related to disaster events in “real-time” as they unfold. People are increasingly using SM platforms to report situational information during disasters, such as critical needs, dead or injured people, and property damage. Despite their usefulness, the majority of this pertinent data is not available to humanitarian organisations during emergencies, mainly due to several data processing and data quality issues. The proliferation of online news media has also led to the exchange of a massive amount of information during disasters, mostly validated by official sources. The integration of SM data with online news reports can provide filtered information while adding more details on the progress of an event than is already published in online news. This research project introduces Disaster Event Extraction System (DEES), a real-time system for extracting disaster events from both online news and tweets. DEES is evaluated on a dataset collected during the Nepal earthquake in 2015. Our results suggest that integrating both SM and news text data improves the event extraction system’s performance compared to using SM data alone. A demonstration of DEES is available at: https://mu-clab.github.io/.

Vorheriger Artikel Link prediction using betweenness centrality and graph neural networks

Nächster Artikel A reliable sentiment analysis for classification of tweets in social networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

rnz news, https://www.rnz.co.nz/.

nzherald news, https://www.nzherald.co.nz/.

stuff news, https://www.stuff.co.nz/.

Python tweepy library version 3.9.0, https://pypi.org/project/tweepy/.

Python Spacy version 2.3.2, https://spacy.io/.

DBSCAN algorithm, https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html.

Python newspaper3k, https://newspaper.readthedocs.io/en/latest/.

NER implementation in Spacy, https://spacy.io/api/annotation#named-entities.

Nominatim version 3.5.1, https://github.com/osm-search/Nominatim.

OpenStreetMap,https://www.openstreetmap.org/#map=2/-41.2/-6.6.

geopy 2.2.0 https://pypi.org/project/geopy/.

CrisisNLPdataset, https://crisisnlp.qcri.org/.

Abel F, Hauff C, Houben G-J, Stronkman R, Tao K (2012) Twitcident: fighting fire with information from social web streams. In: Proceedings of the 21st international conference on World Wide Web, pp 305–308. https://doi.org/10.1145/2187980.2188035

Ahmad T, Ramsay A (2016) Linking tweets to news: is all news of interest? In: International conference on artificial intelligence: methodology, systems, and applications, Springer, pp 151–161 . https://doi.org/10.1007/978-3-319-44748-3_15

Alam F, Joty SR, Imran M (2018) Graph based semi-supervised learning with convolution neural networks to classify crisis related tweets. In: Proceedings of the twelfth international conference on Web and Social Media, ICWSM 2018, Stanford, California, USA, June 25–28, 2018, pp 556–559. AAAI Press. https://aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17815

Alam F, Joty S, Imran M (2018) Domain adaptation with adversarial training and graph embeddings. arXiv preprint arXiv:1805.05151. https://doi.org/10.18653/v1/P18-1099

Algiriyage N, Sampath R, Prasanna R, Doyle EE, Stock K, Johnston D (2021) Identifying disaster-related tweets: a large-scale detection model comparison

Alomari E, Mehmood R, Katib I (2019) Road traffic event detection using twitter data, machine learning, and apache spark. In: 2019 IEEE SmartWorld, ubiquitous intelligence & computing, advanced & trusted computing, scalable computing & communications, cloud & big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), IEEE, pp 1888–1895 . https://doi.org/10.1109/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00332

Alsaedi N, Burnap P, Rana O (2017) Can we predict a riot? disruptive event detection using twitter. ACM Trans Internet Technol (TOIT) 17(2):1–26. https://doi.org/10.1145/2996183CrossRef

Ashktorab Z, Brown C, Nandi M, Culotta A (2014) Tweedr: mining twitter to inform disaster response. In: ISCRAM, pp 269–272

Bhoi A, Pujari SP, Balabantaray RC (2020) A deep learning-based social media text analysis framework for disaster resource management. Soc Netw Anal Min 10(1):78. https://doi.org/10.1007/s13278-020-00692-1CrossRef

Caragea C, McNeese NJ, Jaiswal AR, Traylor G, Kim H-W, Mitra P, Wu D, Tapia AH, Giles CL, Jansen BJ et al (2011) Classifying text messages for the haiti earthquake. In: ISCRAM . Citeseer

Colic N, Rinaldi F (2019) Improving spacy dependency annotation and pos tagging web service using independent NER services. Genom Inform. https://doi.org/10.5808/GI.2019.17.2.e21CrossRef

Cretulescu RG, Morariu D, Breazu M, Volovici D (2019) Dbscan algorithm for document clustering. Int J Adv Stat IT &C Econ Life Sci. https://doi.org/10.2478/ijasitels-2019-0007CrossRef

Danovitch J (2020) Linking social media posts to news with siamese transformers. CoRR arXiv:abs/2001.03303

Dhavase N, Bagade A (2014) Location identification for crime & disaster events by geoparsing twitter. In: International conference for convergence for technology-2014, IEEE, pp 1–3

Endsley MR (1995) Toward a theory of situation awareness in dynamic systems. Hum Fact 37(1):32–64. https://doi.org/10.1518/001872095779049543CrossRef

Ester M, Kriegel H-P, Sander J, Xu X, et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, pp 226–231. http://www.aaai.org/Library/KDD/1996/kdd96-037.php

Guo W, Li H, Ji H, Diab M (2013) Linking tweets to news: a framework to enrich short text data in social media. In: Proceedings of the 51st annual meeting of the association for computational linguistics, Vol. 1, Long Papers, pp 239–249

Gupta D, Strötgen J, Berberich K (2016) Eventminer: mining events from annotated documents. In: Proceedings of the 2016 ACM international conference on the theory of information retrieval, pp 261–270. https://doi.org/10.1145/2970398.2970411

Ha H, Hwang B-Y (2016) Keyword filtering about disaster and the method of detecting area in detecting real-time event using twitter. KIPS Trans Softw Data Eng 5(7):345–350CrossRef

Hagras M, Hassan G, Farag N (2017) Towards natural disasters detection from twitter using topic modelling. In: 2017 European conference on electrical engineering and computer science (EECS), IEEE, pp 272–279 . https://doi.org/10.1109/EECS.2017.57

Hamborg F, Breitinger C, Gipp B (2019) Giveme5w1h: A universal system for extracting main events from news articles. In: Özgöbek, Ö., Kille, B., Gulla, J.A., Lommatzsch, A. (eds.) Proceedings of the 7th International workshop on news recommendation and analytics in conjunction with 13th ACM conference on recommender systems, INRA@RecSys 2019, Copenhagen, Denmark, September 20, 2019. CEUR Workshop Proceedings, vol. 2554, pp 35–43. CEUR-WS.org. http://ceur-ws.org/Vol-2554/paper_06.pdf

Hamborg F, Lachnit S, Schubotz M, Hepp T, Gipp B (2018) Giveme5w: main event retrieval from news articles by extraction of the five journalistic w questions. In: International conference on information, Springer, pp 356–366 . https://doi.org/10.1007/978-3-319-78105-1_39

Han X, Wang J (2019) Earthquake information extraction and comparison from different sources based on web text. ISPRS Int J Geo-Inf 8(6):252. https://doi.org/10.3390/ijgi8060252CrossRef

Han X, Wang J (2019) Using social media to mine and analyze public sentiment during a disaster: a case study of the 2018 Shouguang city flood in china. ISPRS Int J Geo-Inf 8(4):185. https://doi.org/10.3390/ijgi8040185CrossRef

Imran M, Castillo C, Diaz F, Vieweg S (2015) Processing social media messages in mass emergency: a survey. ACM Comput Surv (CSUR) 47(4):67. https://doi.org/10.1145/2771588CrossRef

Imran M, Alam F, Qazi U, Peterson S, Ofli F (2020) Rapid damage assessment using social media images by combining human and machine intelligence. CoRR arXiv:2004.06675

Imran M, Castillo C, Lucas J, Meier P, Vieweg S (2014) Aidr: artificial intelligence for disaster response. In: 23rd international World Wide Web conference, WWW ’14, Seoul, Republic of Korea, April 7–11, 2014, Companion Volume, pp 159–162 . https://doi.org/10.1145/2567948.2577034

Imran M, Elbassuoni S, Castillo C, Diaz F, Meier P (2013) Extracting information nuggets from disaster-related messages in social media. In: 10th proceedings of the international conference on information systems for crisis response and management, Baden-Baden, Germany, May 12–15, 2013. ISCRAM Association. http://idl.iscram.org/files/imran/2013/613_Imran_etal2013.pdf

Imran M, Elbassuoni S, Castillo C, Diaz F, Meier P (2013) Practical extraction of disaster-relevant information from social media. In: Proceedings of the 22nd international conference on World Wide Web, pp 1021–1024 . https://doi.org/10.1145/2487788.2488109

Interdonato R, Guillaume J-L, Doucet A (2019) A lightweight and multilingual framework for crisis information extraction from twitter data. Soc Netw Anal Min 9(1):65. https://doi.org/10.1007/s13278-019-0608-4CrossRef

Jang B, Kim I, Kim JW (2019) Word2vec convolutional neural networks for classification of news articles and tweets. PloS One 14(8):0220976CrossRef

Kalyanam J, Quezada M, Poblete B, Lanckriet G (2016) Prediction and characterization of high-activity events in social media triggered by real-world news. PloS One 11(12):0166694CrossRef

Karami A, Shah V, Vaezi R, Bansal A (2020) Twitter speaks: a case of national disaster situational awareness. J Inf Sci 46(3):313–324. https://doi.org/10.1177/0165551519828620CrossRef

Kekäläinen J, Järvelin K (2002) Using graded relevance assessments in IR evaluation. J Assoc Inf Sci Technol 53(13):1120–1129. https://doi.org/10.1002/asi.10137CrossRef

Li H, Guevara N, Herndon N, Caragea D, Neppalli K, Caragea C, Squicciarini AC, Tapia AH (2015) Twitter mining for disaster response: a domain adaptation approach. In: ISCRAM. http://idl.iscram.org/files/hongminli/2015/1234_HongminLi_etal2015.pdf

Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2013. Proceedings of a meeting held December 5–8, 2013, Lake Tahoe, Nevada, United States, pp 3111–3119

Neppalli VK, Caragea C, Caragea D (2018) Deep neural networks versus naive bayes classifiers for identifying informative tweets during disasters. In: ISCRAM

Norambuena BK, Horning M, Mitra T (2020) Evaluating the inverted pyramid structure through automatic 5w1h extraction and summarization. In: Computational journalism symposium

Pandhare KR, Shah MA (2017) Real time road traffic event detection using twitter and spark. In: 2017 International conference on inventive communication and computational technologies (ICICCT), IEEE, pp 445–449

Petroni F, Raman N, Nugent T, Nourbakhsh A, Panić Ž, Shah S, Leidner J (2018) An extensible event extraction system with cross-media event resolution. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 626–635 . https://doi.org/10.1145/3219819.3219827

Piskorski J, Tanev H, Atkinson M, Van Der Goot E, Zavarella V (2011) Online news event extraction for global crisis surveillance. In: Transactions on computational collective intelligence V, Springer, pp 182–212. https://doi.org/10.1007/978-3-642-24016-4_10

Rhodan M (2017) Please send help. Hurricane harvey victims turn to twitter and facebook. https://time.com/4921961/hurricane-harvey-twitter-facebook-social-media/. Accessed 10 Nov 2020

Rogstadius J, Vukovic M, Teixeira CA, Kostakos V, Karapanos E, Laredo JA (2013) Crisistracker: crowdsourced social media curation for disaster awareness. IBM J Res Dev 57(5):4:1-4:13. https://doi.org/10.1147/JRD.2013.2260692CrossRef

Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World Wide Web, pp 851–860 . https://doi.org/10.1145/1772690.1772777

Sen A, Rudra K, Ghosh S (2015) Extracting situational awareness from microblogs during disaster events. In: 2015 7th international conference on communication systems and networks (COMSNETS), IEEE, pp 1–6. https://doi.org/10.1109/COMSNETS.2015.7098720

Shrestha P, Jacquin C, Daille B (2012) Clustering short text and its evaluation. In: International conference on intelligent text processing and computational linguistics, Springer, pp 169–180

Tanev H, Piskorski J, Atkinson M (2008) Real-time news event extraction for global monitoring systems. Jt Res Center Eur Comm Web Lang Technol Group IPSC, TP 267. https://www.researchgate.net/publication/221474287_Real-Time_News_Event_Extraction_for_Global_Crisis_Monitoring

Téllez-Valero A, Montes-y-Gómez M, Pineda LV (2009) Using machine learning for extracting information from natural disaster news reports. Computación y Sistemas 13(1):33–44

Verma R, Karimi S, Lee D, Gnawali O, Shakery A (2019) Newswire versus social media for disaster response and recovery. In: 2019 resilience week (RWS), IEEE, vol. 1, pp 132–141

Wang KSHW (2010) Representing dynamic phenomena based on spatiotemporal information extracted from web documents. In: Extended abstracts, GIScience conference 2010

Wang W, Stewart K (2015) Spatiotemporal and semantic information extraction from web news reports about natural hazards. Comput Environ Urban Syst 50:30–40. https://doi.org/10.1016/j.compenvurbsys.2014.11.001CrossRef

Wang Z, Ye X (2018) Social media analytics for natural disaster management. Int J Geogr Inf Sci 32(1):49–72. https://doi.org/10.1080/13658816.2017.1367003CrossRef

Wang Z, Ye X (2019) Space, time, and situational awareness in natural hazards: a case study of hurricane sandy with social media data. Cartogr Geogr Inf Sci 46(4):334–346MathSciNetCrossRef

Wang Z, Ye X, Tsou M-H (2016) Spatial, temporal, and content analysis of twitter for wildfire hazards. Nat Hazards 83(1):523–540CrossRef

Wanichayapong N, Pruthipunyaskul W, Pattara-Atikom W, Chaovalit P (2011) Social-based traffic information extraction and classification. In: 2011 11th international conference on ITS telecommunications, IEEE, pp 107–112

Wiegmann M, Kersten J, Klan F, Potthast M, Stein B (2020) Analysis of detection models for disaster-related tweets. Anal Detect Mod Disaster-Relat Tweets, 872–880

Yuan F, Liu R (2020) Mining social media data for rapid damage assessment during hurricane Matthew: feasibility study. J Comput Civil Eng 34(3):05020001CrossRef

Yun H (2011) Disaster events detection using twitter data. J Inf Commun converg Eng 9(1):69–73. https://doi.org/10.6109/jicce.2011.9.1.069CrossRef

Titel: DEES: a real-time system for event extraction from disaster-related web text
verfasst von: Nilani Algiriyage
Raj Prasanna
Kristin Stock
Emma E. H. Doyle
David Johnston
Publikationsdatum: 01.12.2023
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2023
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-022-01007-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2023

Old crimes reported in new bottles: the disclosure of child sexual abuse on Twitter through the case #MeTooInceste

Investigating the cyberbullying risk in digital media: protecting victims in school teenagers

FLACORM: fuzzy logic and ant colony optimization for rumor mitigation through stance prediction in online social networks

Nuclear energy: Twitter data mining for social listening analysis

Trust-aware spatial–temporal feature estimation for next POI recommendation in location-based social networks

Identifying influential nodes for influence maximization problem in social networks using an improved discrete particle swarm optimization

Premium Partner