互联网中危机信息传播规律及应对模式研究_图文

1、 AbstractABSTRACTWith the popularity of the network, the Internet plays an increasingly important role in information dissemination, opinion-forming and strengthening. Objectively government and enterprises can no longer follow the inertia of thinking to resolve the crisis, because the public opinio

2、n of network shows the features of main diversity, affecting broad, category diversity, the theme of concentration, focus sustainability, information bias, etc. What is worrisome is that number of government officials and business still don t understand clearly on the characteristics of information

3、on the network, if the online public opinion breaking out, first way of them taking into account is blockage of speech. But the network spread over a broad area, the approach of deletion is clearly not feasible which can only forms dissatisfy, so the government and corporate have to put forward the

4、capabilities of crisis management. Therefore, in the new situation, we urgently need to explore its inner mechanism through researching the dissemination of information laws in the crisis, so as to build an effectively management system to deal with crisis information.This article focuses on the cur

5、rent status of crisis management studies in domestic and international, analyses crisis through typical cases, and combines qualitative and quantitative research methods, researches the laws of crisis information in Internet in multi-level and multi-angle. The article first describes the background

6、and the significance of this study, and builds a basic framework from research methods and related concepts. Then we select the typical crisis events which have great influence at home and abroad between 2007 to 2009 to analyze, mainly using analysis of variance, correlation analysis, Tobin model, G

7、ARCH model and EGARCH models, and other research tools to explore its spread pattern. Finally this thesis uses the Duomaomao incident as an example, attempts to create a mode of crisis processing which marked by cyberizens joining in. So we can find a new way to deal with public opinion in crisis an

8、d enhance the governments or corporate crisis management capabilities.Key Words: Crisis Information, Crisis Management Netizen Participation, Communication, Response MethodPolicy Options 1 2001 6.5 7 2.9 5000 430 91.4% 3% 1 2 3 4 5 40 414 445 2 CNNIC( 2009 6 30 3.38 25.5% 2008 4000 2 13.4% 20 60 R S

9、 C 3 Fink 1986 Mitroff 1988 2001 4R Reduction Readiness Response Recovery 20 80Pearson 1998 Clavde Shannon 1949 (Mathematical Theory of CommunicationG .Ray.FunkHouser(19730.78 FunkHouser 1984 BOND(1999 I. Helsloot A. Ruitenberg(2004 Fiona DugganLinda Banwell (2004 4 Fiona DugganLinda Ban Porter 2000

10、 Sooyoung(2006 Netian Maureen CNN.com MSNBC.corn 20 90 90 20019 11 2003 2003 2004 11 18 2004 2003911 2005 5 2006 2003 2005 2008 2009 6 GARCHEGARCH GARCHEGARCH Krinei Hermann1972 1987 1989 1 2 3 5 3 google 11 17subprime lending crisis 5.7 2009 5 7 25 20 5 18 15 16 19 17 2009 7 26 18 2008 11 17 18 19

11、2009 7 8 4 4 4 20 6 6 2009 6 26 120 ( 81 39 2 5.7 20 3.2 21One-WayANOV A ScheffeDuncanScheffealpha = .05yN 1234 3032184.006 3062656.0016 3069646.6718 3075591.003 3088539.3320 30103783.3310 30149135.338 30157592.001 30187882.6711 30216600.0014 30240520.0013 30271742.6719 30305976.672 30330410.0012 30

12、542266.6717 30610711.679 30618353.337 301215026.671215026.675 302986105.3315306803536.00Scheffe(a.913.1591.000 22t D 1t D 11tttD D D 23 Scheffe weightT weight sum weight 24 SPSS sum Time1 Time2 Time1Time2Time11.524(* (.018N2020Time2.524(*1(.018N2020 25 OLS Probit Tobit Tobit James.Tobin*00 0i i ii y

13、 if y y if y *Ti iiy X 2(0,i N i X (k+1 Tobiti X i X i y i y 0*ii y y 26i y *i y i y =0i y Tobit i y i X n(nk 2 n i y 1 n i y 0i y (|0i i iif E y F i f i F Ti ii iif y X F i X i i f F 2Tobit (Y 2622625 27 10001 x 2 x 3 x 2 x 2 x 3 x 2 x 4 x * 00 0i i ii y if y y if y *Ti iiy X *ln(i y Y 2(0,i N 3 20

14、09 6 20 4 28 Eviews5.0 Tobit R R AIC SC Hannan-Quinn 4 30 34LR12. 0k14,.2log 2(loglog MAXL L 220.05(42RP1P 1x 1x 291 x 2 P 3 P 3 x 2 x 3 x 3 x 4 P 4 x 4 x Tobit GrangerGranger Gausality Testx yx y y y y x y x y 30 y x1t u 2t u y y x x0H 12. 0 q0H12. s x y x y x y x y y x y x (GrangerP 1 = 0.000160.0

15、5 31P 1 =0.048030.053x 4 x LnY3x 4 x LnY1 x LnY3 x 09/06/09 : 1 33 : 2 : F X3 lnY 3112.4067 0.00016 YX32.02591 0.15217LnY4x09/06/09 : 1 33: 2: FX4 lnY313.41971 0.04803 YX41.097130.34879134x x x 32 GARCH EGARCH TARCH Bollerslev 1986 GARCHt x ARMA(m n p q011221122.tt t p t mtt t p t nx xx xv vv vtt tv

16、 h pj jt jqi it i pt pt t qt qt t h h hh 12120221122110 2i v (t v E 1 (t v D p 0 iqi , 2, 1 jpj , 2, 1qi p j ji11 1 GARCH GARCH GARCH GARCH 33Pagan Schwert(1990 Nelson(1991 EGARCH EGARCH2(tLn = 0 +qi i t itiu 1 +qi it itiu 1 +pj jt j Ln 12( 5.3 EGARCHt 2 EGARCH= EGARCH TARCH EGARCHt 2 = 0 +qi i t i

17、u 12+ 211t tu d +pj jt j1 2 5.4 t d =,10, 0tt u u t u 0t u 0 TARCH 34 googlet r 11t t tt X X X r , 2,3,. t n t X t 52 35t r t r t r 5.1T -4.7224131%tr 36 5.3 AC 0.408 0.029 -0.109 P=0.0290.05 P 0.05 ( 5.4 Time Varying Clustering ARCH ARCH 5.2 F 10.237 P 0.0026560.05 R 8.5915 P 37 0.0033770.05 ARCH G

18、ARCH ARCH : F10.23733 0.002656 R8.591491 0.003377 GARCH 1 15.3RESID(-12=-0.13999P0.05GARCH(-1= 1.037907P00.05ARCHGARCH ARCHGARCH0.901GARCH(1,1MEAN-REVERSION 38 EGARCH 1 1 : X : ML - ARCH (Marquardt : 04/01/09 : 4 47 : 4458: ONLOG(GARCH = C(5 + C(6*ABS(RESID(-1/SQRT(GARCH(-1 + C(7*RESID(-1/SQRT(GARCH

19、(-1 + C(8*LOG(GARCH(-1Z0.0684971.2113680.22583.4764260.0005R-0.081088 0.021032R-0.2913000.087209 0.099100 AIC -2.123551 0.353549 SC -1.799153 54.71812 DW2.018241EGARCH C(6C(7EGARCH 39 GARCH(1,1 EGARCH 40 41 42 ARCH ARCH GARCH EGARCH GARCH EGARCH 2004c GARCHt x ARMA(m n p q011221122.tt t p t m tt t p t nx xxxv v v vtt tv h pj jt jqi it i pt pt t qt qt t h h hh 12120221122110 2i v (t v E 1(t v D p 0 iq i , 2, 1 jp j , 2, 1qi pj ji 11 1 (6.1 (6.2 GARCH 43 GARCH GARCH EGARCH 111ln ( ln t t t t h h t h = 0 googl