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.
ViewegSHughesALStarbirdK, et al. Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the SIGCHI conference on human factors in computing systems, Atlanta, GA, 10–15 April 2010, pp. 1079–1088. New York: ACM.
11.
NeppalliVKCarageaCSquicciariniA, et al. Sentiment analysis during hurricane sandy in emergency response. Int J Dis Risk Reduct2017; 21: 213–222.
12.
CallaghanCW.Disaster management, crowdsourced R&D and probabilistic innovation theory: toward real time disaster response capability. Int J Dis Risk Reduct2016; 17: 238–250.
TumasjanASprengerTOSandnerPG, et al. Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Proceedings of the ICWSM 10Washington, DC, 17 May 23–26, 2010.
15.
SzomszorMKostkovaPDe QuinceyE.#Swineflu: twitter predicts swine flu outbreak in 2009. In: Proceedings of the international conference on electronic healthcare, Casablanca, Morocco, 13–15 December 2010, pp. 18–26. Berlin: Springer.
16.
AbelFHauffCHoubenGJ, et al. Twitcident: fighting fire with information from social web streams. In: Proceedings of the 21st international conference on World Wide Web, Lyon, 16–20 April 2012, pp. 305–308. New York: ACM.
17.
SinnappanSFarrellCStewartE. Priceless tweets! A study on twitter messages posted during crisis: black saturday. In: ACIS 2010 proceedings, 2010, p. 39, https://aisel.aisnet.org/acis2010/39
18.
AvvenutiMCresciSMarchettiA, et al. Ears (earthquake alert and report system): a real time decision support system for earthquake crisis management. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, New York, 24–27 August 2014, pp. 1749–1758. New York: ACM.
19.
SakakiTOkazakiMMatsuoY.Tweet analysis for real-time event detection and earthquake reporting system development. IEEE T Knowl Data Eng2013; 25(4): 919–931.
20.
EarlePSBowdenDCGuyM.Twitter earthquake detection: earthquake monitoring in a social world. Ann Geophys2012; 54(6): 708–715.
21.
SakakiTOkazakiMMatsuoY. Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World Wide Web, Raleigh, NC, 26–30 April 2010, pp. 851–860. New York: ACM.
22.
RobinsonBPowerRCameronM.A sensitive twitter earthquake detector. In: Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro, Brazil, 13–17 May 2013, pp. 999–1002. New York: ACM.
23.
MiyabeMMiuraAAramakiE. Use trend analysis of twitter after the great east japan earthquake. In: Proceedings of the conference on computer supported cooperative work companion, Seattle, WA, 11–15 February 2012, pp. 175–178. New York: ACM.
24.
DoanSVoBKHCollierN. An analysis of twitter messages in the 2011 Tohoku earthquake. In: Proceedings of the international conference on electronic healthcare, Málaga, 21–23 November 2011, pp. 58–66. Berlin: Springer.
25.
OhOKwonKHRaoHR. An exploration of social media in extreme events: rumor theory and twitter during the Haiti earthquake 2010. In: Proceedings of the international conference on information systems.
26.
HughesALSt. DenisLAPalenL, et al. Online public communications by police & fire services during the 2012 Hurricane Sandy. In: Proceedings of the 32nd annual ACM conference on human factors in computing systems, Toronto, ON, 26 April–1 May 2014, pp. 1505–1514. New York: ACM.
27.
GuptaALambaHKumaraguruP, et al. Faking sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy. In: Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro, Brazil, 13–17 May 2013, pp. 729–736. New York: ACM.
28.
TakahashiBTandocECCarmichaelC.Communicating on Twitter during a disaster: an analysis of tweets during Typhoon Haiyan in the Philippines. Comput Hum Behav2015; 50: 392–398.
29.
SreenivasanNDLeeCSGohDHL. Tweet me home: exploring information use on twitter in crisis situations. In: Proceedings of the international conference on online communities and social computing, Orlando, FL, 9–14 July 2011, pp. 120–129. New York: ACM.
30.
PalenLStarbirdKViewegS, et al. Twitter-based information distribution during the 2009 Red River Valley flood threat. Bull Am Soc Inform Sci Technol2010; 36(5): 13–17.
31.
De AlbuquerqueJPHerfortBBrenningA, et al. A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. Int J Geograph Inform Sci2015; 29(4): 667–689.
32.
AshktorabZBrownCNandiM, et al. Tweedr: mining twitter to inform disaster response. In: Proceedings of the 11th international conference on information systems for crisis response and management, University Park, PA, 18–21 May 2014, pp. 77–80. New York: ACM.
33.
DashtiSPalenLHerisMP, et al. Supporting disaster reconnaissance with social media data: a design-oriented case study of the 2013 Colorado floods. In: Proceedings of the 11th international ISCRAM conference, University Park, PA, 1 January 2014, pp. 18–21. New York: ACM.
34.
HerfortBSchelhornSJAlbuquerqueJPD, 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. In: Proceedings of the international conference on information systems for crisis response and management, University Park, PA, 18–21 May 2014.
35.
MurthyDLongwellSA.Twitter and disasters: the uses of Twitter during the 2010 Pakistan floods. Inform Commun Soc2013; 16(6): 837–855.
36.
ShawFBurgessJCrawfordK, et al. Sharing news, making sense, saying thanks. Aust J Commun2013; 40(1): 23–40.
37.
KongthonAHaruechaiyasakCPailaiJ, et al. The role of Twitter during a natural disaster: case study of 2011 Thai flood. In: Proceedings of the PICMET’12: technology management for emerging technologies, Vancouver, BC, 29 July–2 August 2012, pp. 2227–2232. New YORK: IEEE.
TaboadaMBrookeJTofiloskiM, et al. Lexicon-based methods for sentiment analysis. Comput Linguist2011; 37(2): 267–307.
41.
KhanAZAtiqueMThakareV.Combining lexicon-based and learning-based methods for twitter sentiment analysis. Int J Electron Commun Soft Comput Sci Eng2015; 4(4): 89–91.
42.
MedhatWHassanAKorashyH.Sentiment analysis algorithms and applications: a survey. Ain Shams Eng J2014; 5(4): 1093–1113.
FranklinE.Some theoretical considerations in off-the-shelf text analysis software. In: Proceedings of the Student Research Workshop associated with RANLP 2015, Hissar, 7–9 September 2015, pp. 8–15.
45.
GolderSAMacyMW.Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science2011; 333(6051): 1878–1881.
46.
GonçalvesPAraújoMBenevenutoF, et al. Comparing and combining sentiment analysis methods. In: Proceedings of the first ACM conference on online social networks, Boston, MA, 7–8 October 2013, pp. 27–38. New York: ACM.
47.
Nolen-HoeksemaSMorrowJ.A prospective study of depression and posttraumatic stress symptoms after a natural disaster: the 1989 Loma Prieta earthquake. J Pers Soc Psychol1991; 61(1): 115–121
48.
YinDBondSDZhangH.Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quart2014; 38(2): 539–560.
49.
ZhangDLuoTWangD.Learning from LDA using deep neural networks. In: LinCYXueNZhaoD, et al. (eds) Natural language understanding and intelligent applications. Berlin: Springer, 2016, pp. 657–664.
50.
MohammadiMAl-FuqahaASorourS, et al. Deep learning for iot big data and streaming analytics: a survey. IEEE Commun Surv Tut2018; 20: 2923–2960.
51.
ZhaoW.Research on the deep learning of the small sample data based on transfer learning. AIP Conf Proc2017; 1864: 020018.
52.
ChilimbiTMSuzueYApacibleJ, et al. Project Adam: building an efficient and scalable deep learning training system. OSDI2014; 14: 571–582.
53.
ErhanDBengioYCourvilleA, et al. Why does unsupervised pre-training help deep learning?J Mach Learn Res2010; 11: 625–660.
54.
SeverynAMoschittiA.Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, Santiago, Chile, 9–13 August 2015, pp. 959–962. New York: ACM.
55.
MahmudMKaiserMSHussainA, et al. Applications of deep learning and reinforcement learning to biological data. IEEE T Neur Net Learn Syst2018; 29(6): 2063–2079.
HongLDavisonBD. Empirical study of topic modeling in Twitter. In: Proceedings of the first workshop on social media analytics, Washington, DC, 25–28 July 2010, pp. 80–88. New York: ACM.
59.
LuYMeiQZhaiC.Investigating task performance of probabilistic topic models: an empirical study of PLSA and LDA. Inform Retr2011; 14(2): 178–203.
NajafabadiMM. A research agenda for distributed hashtag spoiling: tails of a survived trending hashtag. In: Proceedings of the 18th annual international conference on digital government research, Staten Island, NY, 7–9 June 2017, pp. 21–29. New York: ACM.
76.
FerraraEVarolODavisC, et al. The rise of social bots. Commun ACM2016; 59(7): 96–104.
77.
KaramiAElkouriA.Political popularity analysis in social media. Proceedings of the iConference. Washington, DC, 31 March – 3 April 2019.
78.
KaramiABennettLSHeX.Mining public opinion about economic issues: twitter and the US Presidential election. Int J Strat Deci Sci (IJSDS)2018; 9(1): 18–28.
79.
AlperinJPDumasCKaramiA, et al. Politicians & the public: The analysis of political communication in social media. Proceedings of the Association for Information Science and Technology, Vancouver, 10–14 November 2018; 55(1):646–9.
80.
KitzieVLMohammadiEKaramiA.Life never matters in the DEMOCRATS MIND: examining strategies of retweeted social bots during a mass shooting event. Proceedings of the Association for Information Science and Technology, Vancouver, 10–14 November 2018; 55(1):254–63.
81.
NajafabadiMMDomanskiRJ.Hacktivism and distributed hashtag spoiling on Twitter: Tales of the# IranTalks. First Monday2018; 23(4).
82.
ShawGKaramiA.An Exploratory Study of (#) Exercise in the Twittersphere. Proceedings of the iConference, Washington, D.C, 31 March–3 April 2019.
83.
WebbFKaramiAKitzieV.Characterizing Diseases and disorders in Gay Users’ tweets. Proceedings of the Southern Association for Information Systems (SAIS), Atlanta, GA, 23–24 March 2018.
84.
KaramiADahlAATurner-McGrievyG, et al. Characterizing diabetes, diet, exercise, and obesity comments on Twitter. Int J Inform Manage2018; 38(1): 1–6.
85.
KaramiAWebbFKitzieVL.Characterizing transgender health issues in twitter. Proceedings of the Association for Information Science and Technology, Vancouver, 10–14 November 2018; 55(1): 207–215.
86.
ShawGJrKaramiA.Computational content analysis of negative tweets for obesity, diet, diabetes, and exercise. Proceedings of the Association for Information Science and Technology, Washington, DC, 27 October–1 November 2017; 54(1): 357–365.
87.
KaramiAGangopadhyayA.Fftm: a fuzzy feature transformation method for medical documents. Proceedings of BioNLP, Baltimore, MD, 26 June-2 July 2014, pp.128–133.
88.
KaramiAGangopadhyayAZhouB, et al. A fuzzy approach model for uncovering hidden latent semantic structure in medical text collections. Proceedings of the iConference, Newport Beach, CA, 24–27 March 2015.
89.
KaramiAPendergraftNM.Computational analysis of insurance complaints: Geico case study. International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction and Behavior Representation in Modeling and Simulation, Washington DC, 10–13 July 2018.
90.
KaramiA. ProQuest Dissertations Publishing, University of Maryland, Baltimore County, 2015. 3721828.
91.
KaramiAAryyaGBinZ, et al. Fuzzy approach topic discovery in health and medical corpora. Int J Fuzzy Syst2018; 20: 1334–1345.
92.
KaramiAGangopadhyayAZhouB, et al. Flatm: A fuzzy logic approach topic model for medical documents. In: the 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC), Redmond, WA, 17–19 August 2015. Piscataway, NJ: IEEE.
93.
CollinsMKaramiA.Social Media Analysis For Organizations: Us NortheasternPublic And State Libraries Case Study. Proceedings of the Southern Association for Information Systems (SAIS), Atlanta, Georgia. March 23–24, 2018.
94.
KaramiACollinsM.What do the US West Coast public libraries post on Twitter?Proceedings of the Association for Information Science and Technology. Vancouver, Canada. 2018; 55(1): 216–225.
95.
KordzadehN. Exploring the use of twitter by leading medical centers in the United States. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, Grand Wailea, Maui, Hawaii. 7–10 January 2019.
96.
KordzadehNChenS.Investigating The Use Of Twitter By Three Major Us Medical Centers. Proceedings of the Southern Association for Information Systems (SAIS), Atlanta, Georgia, March 23 –24, 2018.
97.
KaramiAZhouB.Online review spam detection by new linguistic features. iConference 2015 Proceedings, 15 March 2015.
98.
KaramiAZhouL.Exploiting latent content based features for the detection of static SMS spams. Proceedings of the American Society for Information Science and Technology, Seattle, WA, 31 October-5 November 2014; 51(1): 1–4.
99.
KaramiAZhouL.Improving static SMS spam detection by using new content-based features. Proceedings of the 20th Americas Conference on Information Systems (AMCIS), Savannah, GA, 7–9 August 2014.
