Twitter speaks: A case of national disaster situational awareness

In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental and human losses. The unpredictable nature of natural disasters behaviour makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyse public concerns during natural disasters; however, this approach is limited, expensive and time-consuming. Luckily, the advent of social media has provided scholars with an alternative means of analysing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasises the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modelling to create a better SA for disaster preparedness, response and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.

[1]  David Woods,et al.  Situation Awareness: A Critical But Ill-Defined Phenomenon , 1991 .

[2]  S. Nolen-Hoeksema,et al.  A prospective study of depression and posttraumatic stress symptoms after a natural disaster: the 1989 Loma Prieta Earthquake. , 1991, Journal of personality and social psychology.

[3]  Chantal Wouters,et al.  An exploratory study , 2003 .

[4]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[5]  Nitesh V. Chawla,et al.  Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management , 2007, International Conference on Computational Science.

[6]  David M. Blei,et al.  Supervised Topic Models , 2007, NIPS.

[7]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[8]  Suku Sinnappan,et al.  Priceless Tweets! A Study on Twitter Messages Posted During Crisis: Black Saturday , 2010, ICIS 2010.

[9]  Leysia Palen,et al.  Twitter‐based information distribution during the 2009 Red River Valley flood threat , 2010 .

[10]  Isabell M. Welpe,et al.  Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment , 2010, ICWSM.

[11]  Yutaka Matsuo,et al.  Earthquake shakes Twitter users: real-time event detection by social sensors , 2010, WWW '10.

[12]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[13]  Martin Szomszor,et al.  #Swineflu: Twitter Predicts Swine Flu Outbreak in 2009 , 2010, eHealth.

[14]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[15]  Brian D. Davison,et al.  Empirical study of topic modeling in Twitter , 2010, SOMA '10.

[16]  Yue Lu,et al.  Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA , 2011, Information Retrieval.

[17]  Kyounghee Hazel Kwon,et al.  An Exploration of Social Media in Extreme Events: Rumor Theory and Twitter during the Haiti Earthquake 2010 , 2010, ICIS.

[18]  Son Doan,et al.  An analysis of Twitter messages in the 2011 Tohoku Earthquake , 2011, eHealth.

[19]  Maite Taboada,et al.  Lexicon-Based Methods for Sentiment Analysis , 2011, CL.

[20]  Chei Sian Lee,et al.  Tweet Me Home: Exploring Information Use on Twitter in Crisis Situations , 2011, HCI.

[21]  Lei Zhang,et al.  Combining lexicon-based and learning-based methods for twitter sentiment analysis , 2011 .

[22]  Scott A. Golder,et al.  Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures , 2011 .

[23]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[24]  Mark Dredze,et al.  You Are What You Tweet: Analyzing Twitter for Public Health , 2011, ICWSM.

[25]  Geert-Jan Houben,et al.  Twitcident: fighting fire with information from social web streams , 2012, WWW.

[26]  C. Haruechaiyasak,et al.  The role of Twitter during a natural disaster: Case study of 2011 Thai Flood , 2012, 2012 Proceedings of PICMET '12: Technology Management for Emerging Technologies.

[27]  Michelle R. Guy,et al.  Twitter earthquake detection: earthquake monitoring in a social world , 2012 .

[28]  Eiji Aramaki,et al.  Use trend analysis of twitter after the great east japan earthquake , 2012, CSCW.

[29]  Scott A. Longwell,et al.  TWITTER AND DISASTERS , 2013 .

[30]  Fabrício Benevenuto,et al.  Comparing and combining sentiment analysis methods , 2013, COSN '13.

[31]  Yutaka Matsuo,et al.  Tweet Analysis for Real-Time Event Detection and Earthquake Reporting System Development , 2013, IEEE Transactions on Knowledge and Data Engineering.

[32]  Robert Power,et al.  A sensitive Twitter earthquake detector , 2013, WWW.

[33]  Anupam Joshi,et al.  Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy , 2013, WWW.

[34]  K. Crawford,et al.  Sharing news, making sense, saying thanks: patterns of talk on Twitter during the Queensland floods , 2013 .

[35]  Andrea Marchetti,et al.  EARS (earthquake alert and report system): a real time decision support system for earthquake crisis management , 2014, KDD.

[36]  Leysia Palen,et al.  Supporting disaster reconnaissance with social media data: A design-oriented case study of the 2013 Colorado floods , 2014, ISCRAM.

[37]  Walaa Medhat,et al.  Sentiment analysis algorithms and applications: A survey , 2014 .

[38]  Han Zhang,et al.  Anxious or Angry? Effects of Discrete Emotions on the Perceived Helpfulness of Online Reviews , 2014, MIS Q..

[39]  Aron Culotta,et al.  Tweedr: Mining twitter to inform disaster response , 2014, ISCRAM.

[40]  Amir Karami,et al.  FFTM: A Fuzzy Feature Transformation Method for Medical Documents , 2014, BioNLP@ACL.

[41]  Trishul M. Chilimbi,et al.  Project Adam: Building an Efficient and Scalable Deep Learning Training System , 2014, OSDI.

[42]  R. Kitchin The real-time city? Big data and smart urbanism , 2013 .

[43]  Lina Zhou,et al.  Improving Static SMS Spam Detection by Using New Content-based Features , 2014, AMCIS.

[44]  Leysia Palen,et al.  Online public communications by police & fire services during the 2012 Hurricane Sandy , 2014, CHI.

[45]  Lina Zhou,et al.  Exploiting latent content based features for the detection of static SMS spams , 2014, ASIST.

[46]  Alexander Zipf,et al.  Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013 , 2014, ISCRAM.

[47]  Bin Zhou,et al.  A Fuzzy Approach Model for Uncovering Hidden Latent Semantic Structure in Medical Text Collections , 2015 .

[48]  Amir Karami,et al.  Fuzzy Topic Modeling for Medical Corpora , 2015 .

[49]  Bin Zhou,et al.  Online Review Spam Detection by New Linguistic Features , 2015 .

[50]  Carlos Castillo,et al.  Big Crisis Data. , 2015, WebMedia 2015.

[51]  Ryan L. Boyd,et al.  The Development and Psychometric Properties of LIWC2015 , 2015 .

[52]  Emma Franklin,et al.  Some Theoretical Considerations in Off-the-Shelf Text Analysis Software , 2015, RANLP.

[53]  Hadi Kharrazi,et al.  FLATM: A fuzzy logic approach topic model for medical documents , 2015, 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC).

[54]  Alessandro Moschitti,et al.  Twitter Sentiment Analysis with Deep Convolutional Neural Networks , 2015, SIGIR.

[55]  Alexander Zipf,et al.  A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management , 2015, Int. J. Geogr. Inf. Sci..

[56]  Ingmar Weber,et al.  Twitter: A Digital Socioscope , 2015 .

[57]  Edson C. Tandoc,et al.  Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines , 2015, Comput. Hum. Behav..

[58]  Dong Wang,et al.  Learning from LDA Using Deep Neural Networks , 2015, NLPCC/ICCPOL.

[59]  Christian William Callaghan,et al.  Disaster management, crowdsourced R&D and probabilistic innovation theory: Toward real time disaster response capability , 2016, International Journal of Disaster Risk Reduction.

[60]  Filippo Menczer,et al.  The rise of social bots , 2014, Commun. ACM.

[61]  George Shaw,et al.  Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise , 2017, ASIST.

[62]  Wei Zhao,et al.  Research on the deep learning of the small sample data based on transfer learning , 2017 .

[63]  Cornelia Caragea,et al.  Sentiment analysis during Hurricane Sandy in emergency response , 2017 .

[64]  Mahdi M. Najafabadi,et al.  A Research Agenda for Distributed Hashtag Spoiling: Tails of a Survived Trending Hashtag , 2017, DG.O.

[65]  Nima Kordzadeh,et al.  INVESTIGATING THE USE OF TWITTER BY THREE MAJOR U.S. MEDICAL CENTERS , 2018 .

[66]  Hadi Kharrazi,et al.  Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter , 2017, Int. J. Inf. Manag..

[67]  Amir Karami,et al.  Characterizing transgender health issues in Twitter , 2018, ArXiv.

[68]  Ehsan Mohammadi,et al.  “Life never matters in the DEMOCRATS MIND”: Examining strategies of retweeted social bots during a mass shooting event , 2018, ASIST.

[69]  Amir Karami,et al.  Social Media Analysis For Organizations: Us Northeastern Public And State Libraries Case Study , 2018, ArXiv.

[70]  Amir Hussain,et al.  Applications of Deep Learning and Reinforcement Learning to Biological Data , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[71]  Bin Zhou,et al.  Fuzzy Approach Topic Discovery in Health and Medical Corpora , 2017, Int. J. Fuzzy Syst..

[72]  Amir Karami,et al.  What do the US West Coast public libraries post on Twitter? , 2018, ArXiv.

[73]  Xiaoyun He,et al.  Mining Public Opinion about Economic Issues: Twitter and the U.S. Presidential Election , 2018, Int. J. Strateg. Decis. Sci..

[74]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[75]  Juan Pablo Alperin,et al.  Politicians & the public: The analysis of political communication in social media , 2018, ASIST.

[76]  Amir Karami,et al.  Computational Analysis of Insurance Complaints: GEICO Case Study , 2018, ArXiv.

[77]  Amir Karami,et al.  Characterizing Diseases and disorders in Gay Users' tweets , 2018, ArXiv.

[78]  Robert J. Domanski,et al.  Hacktivism and distributed hashtag spoiling on Twitter: Tales of the #IranTalks , 2018, First Monday.

[79]  Amir Karami,et al.  Political Popularity Analysis in Social Media , 2018, iConference.

[80]  George Shaw,et al.  An Exploratory Study of (#)Exercise in the Twittersphere , 2018, iConference 2019 Proceedings.

[81]  Nima Kordzadeh Exploring the Use of Twitter by Leading Medical Centers in the United States , 2019, HICSS.