An enhanced SIR dynamic model : the timing and changes in public opinion in the process of information diffusion
Gespeichert in:
| Autor*in: |
Yan, Zhen [verfasserIn] Zhou, Xiao [verfasserIn] Du, Rong - 1968- [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Electronic commerce research - Dordrecht : Springer Science Business Media B.V., 2001, 24(2024), 3 vom: Sept., Seite 2021-2044 |
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| Übergeordnetes Werk: |
volume:24 ; year:2024 ; number:3 ; month:09 ; pages:2021-2044 |
| Links: |
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| DOI / URN: |
10.1007/s10660-022-09608-x |
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| Katalog-ID: |
1906799806 |
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| 245 | 1 | 3 | |a An enhanced SIR dynamic model |b the timing and changes in public opinion in the process of information diffusion |c Zhen Yan, Xiao Zhou, Rong Du |
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| 982 | |2 26 |1 00 |x DE-206 |b Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion. | ||
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10.1007/s10660-022-09608-x doi (DE-627)1906799806 (DE-599)KXP1906799806 DE-627 ger DE-627 rda eng Yan, Zhen verfasserin aut An enhanced SIR dynamic model the timing and changes in public opinion in the process of information diffusion Zhen Yan, Xiao Zhou, Rong Du 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Enhanced SIR dynamic model (dpeaa)DE-206 Information diffusion process (dpeaa)DE-206 Simulation (dpeaa)DE-206 Spreading rate (dpeaa)DE-206 Weight of intervening act (dpeaa)DE-206 Zhou, Xiao verfasserin (DE-588)1115257447 (DE-627)869644467 (DE-576)477690823 aut Du, Rong 1968- verfasserin (DE-588)141668946 (DE-627)630215049 (DE-576)325259186 aut Enthalten in Electronic commerce research Dordrecht : Springer Science Business Media B.V., 2001 24(2024), 3 vom: Sept., Seite 2021-2044 Online-Ressource (DE-627)325615047 (DE-600)2038488-9 (DE-576)121192482 1572-9362 nnns volume:24 year:2024 number:3 month:09 pages:2021-2044 https://link.springer.com/content/pdf/10.1007/s10660-022-09608-x.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10660-022-09608-x Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 24 2024 3 9 2021-2044 26 01 0206 4602416519 x1z 25-10-24 26 00 DE-206 Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion. |
| spelling |
10.1007/s10660-022-09608-x doi (DE-627)1906799806 (DE-599)KXP1906799806 DE-627 ger DE-627 rda eng Yan, Zhen verfasserin aut An enhanced SIR dynamic model the timing and changes in public opinion in the process of information diffusion Zhen Yan, Xiao Zhou, Rong Du 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Enhanced SIR dynamic model (dpeaa)DE-206 Information diffusion process (dpeaa)DE-206 Simulation (dpeaa)DE-206 Spreading rate (dpeaa)DE-206 Weight of intervening act (dpeaa)DE-206 Zhou, Xiao verfasserin (DE-588)1115257447 (DE-627)869644467 (DE-576)477690823 aut Du, Rong 1968- verfasserin (DE-588)141668946 (DE-627)630215049 (DE-576)325259186 aut Enthalten in Electronic commerce research Dordrecht : Springer Science Business Media B.V., 2001 24(2024), 3 vom: Sept., Seite 2021-2044 Online-Ressource (DE-627)325615047 (DE-600)2038488-9 (DE-576)121192482 1572-9362 nnns volume:24 year:2024 number:3 month:09 pages:2021-2044 https://link.springer.com/content/pdf/10.1007/s10660-022-09608-x.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10660-022-09608-x Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 24 2024 3 9 2021-2044 26 01 0206 4602416519 x1z 25-10-24 26 00 DE-206 Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion. |
| allfields_unstemmed |
10.1007/s10660-022-09608-x doi (DE-627)1906799806 (DE-599)KXP1906799806 DE-627 ger DE-627 rda eng Yan, Zhen verfasserin aut An enhanced SIR dynamic model the timing and changes in public opinion in the process of information diffusion Zhen Yan, Xiao Zhou, Rong Du 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Enhanced SIR dynamic model (dpeaa)DE-206 Information diffusion process (dpeaa)DE-206 Simulation (dpeaa)DE-206 Spreading rate (dpeaa)DE-206 Weight of intervening act (dpeaa)DE-206 Zhou, Xiao verfasserin (DE-588)1115257447 (DE-627)869644467 (DE-576)477690823 aut Du, Rong 1968- verfasserin (DE-588)141668946 (DE-627)630215049 (DE-576)325259186 aut Enthalten in Electronic commerce research Dordrecht : Springer Science Business Media B.V., 2001 24(2024), 3 vom: Sept., Seite 2021-2044 Online-Ressource (DE-627)325615047 (DE-600)2038488-9 (DE-576)121192482 1572-9362 nnns volume:24 year:2024 number:3 month:09 pages:2021-2044 https://link.springer.com/content/pdf/10.1007/s10660-022-09608-x.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10660-022-09608-x Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 24 2024 3 9 2021-2044 26 01 0206 4602416519 x1z 25-10-24 26 00 DE-206 Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion. |
| allfieldsGer |
10.1007/s10660-022-09608-x doi (DE-627)1906799806 (DE-599)KXP1906799806 DE-627 ger DE-627 rda eng Yan, Zhen verfasserin aut An enhanced SIR dynamic model the timing and changes in public opinion in the process of information diffusion Zhen Yan, Xiao Zhou, Rong Du 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Enhanced SIR dynamic model (dpeaa)DE-206 Information diffusion process (dpeaa)DE-206 Simulation (dpeaa)DE-206 Spreading rate (dpeaa)DE-206 Weight of intervening act (dpeaa)DE-206 Zhou, Xiao verfasserin (DE-588)1115257447 (DE-627)869644467 (DE-576)477690823 aut Du, Rong 1968- verfasserin (DE-588)141668946 (DE-627)630215049 (DE-576)325259186 aut Enthalten in Electronic commerce research Dordrecht : Springer Science Business Media B.V., 2001 24(2024), 3 vom: Sept., Seite 2021-2044 Online-Ressource (DE-627)325615047 (DE-600)2038488-9 (DE-576)121192482 1572-9362 nnns volume:24 year:2024 number:3 month:09 pages:2021-2044 https://link.springer.com/content/pdf/10.1007/s10660-022-09608-x.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10660-022-09608-x Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 24 2024 3 9 2021-2044 26 01 0206 4602416519 x1z 25-10-24 26 00 DE-206 Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion. |
| allfieldsSound |
10.1007/s10660-022-09608-x doi (DE-627)1906799806 (DE-599)KXP1906799806 DE-627 ger DE-627 rda eng Yan, Zhen verfasserin aut An enhanced SIR dynamic model the timing and changes in public opinion in the process of information diffusion Zhen Yan, Xiao Zhou, Rong Du 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Enhanced SIR dynamic model (dpeaa)DE-206 Information diffusion process (dpeaa)DE-206 Simulation (dpeaa)DE-206 Spreading rate (dpeaa)DE-206 Weight of intervening act (dpeaa)DE-206 Zhou, Xiao verfasserin (DE-588)1115257447 (DE-627)869644467 (DE-576)477690823 aut Du, Rong 1968- verfasserin (DE-588)141668946 (DE-627)630215049 (DE-576)325259186 aut Enthalten in Electronic commerce research Dordrecht : Springer Science Business Media B.V., 2001 24(2024), 3 vom: Sept., Seite 2021-2044 Online-Ressource (DE-627)325615047 (DE-600)2038488-9 (DE-576)121192482 1572-9362 nnns volume:24 year:2024 number:3 month:09 pages:2021-2044 https://link.springer.com/content/pdf/10.1007/s10660-022-09608-x.pdf Verlag lizenzpflichtig https://doi.org/10.1007/s10660-022-09608-x Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_2574 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4317 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 24 2024 3 9 2021-2044 26 01 0206 4602416519 x1z 25-10-24 26 00 DE-206 Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion. |
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Enthalten in Electronic commerce research 24(2024), 3 vom: Sept., Seite 2021-2044 volume:24 year:2024 number:3 month:09 pages:2021-2044 |
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code="x">DE-206</subfield><subfield code="b">Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion.</subfield></datafield></record></collection>
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| author |
Yan, Zhen |
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Yan, Zhen misc Enhanced SIR dynamic model misc Information diffusion process misc Simulation misc Spreading rate misc Weight of intervening act An enhanced SIR dynamic model the timing and changes in public opinion in the process of information diffusion |
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Yan, Zhen |
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26 00 DE-206 Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion An enhanced SIR dynamic model the timing and changes in public opinion in the process of information diffusion Zhen Yan, Xiao Zhou, Rong Du Enhanced SIR dynamic model (dpeaa)DE-206 Information diffusion process (dpeaa)DE-206 Simulation (dpeaa)DE-206 Spreading rate (dpeaa)DE-206 Weight of intervening act (dpeaa)DE-206 |
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misc Enhanced SIR dynamic model misc Information diffusion process misc Simulation misc Spreading rate misc Weight of intervening act |
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misc Enhanced SIR dynamic model misc Information diffusion process misc Simulation misc Spreading rate misc Weight of intervening act |
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misc Enhanced SIR dynamic model misc Information diffusion process misc Simulation misc Spreading rate misc Weight of intervening act |
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code="x">DE-206</subfield><subfield code="b">Social media platforms provide great convenience for public share and get access to various information, which bring challenges to both companies and company to manage negative impacts of public opinion. When an event happens, truth and rumors are intertwined in the process. To figure out how rumor influences the information diffusion process timely and identify points when necessary actions should be taken to control the impacts of rumors, an enhanced S-I-R (susceptible-infectious-recovered) dynamic model, involving rumors occurring sequentially in information diffusion process, is proposed. Based on the proposed model, we develop dynamic equations with the spreading probability and weight of the intervening acts. Then, we simulate how the process works theoretically where simulation in two different group of real-world datasets is conducted to demonstrate the validity of the proposed model. Besides, the present study illustrates that different spreading rates and weights of intervening acts could result in different diffusion situations, especially when the weight induces an increase in the peak point (?2 = 0.5) and secondary climax(?4 = 0.6). Finally, the present study provides suggestions practically for company in terms of managing rumor in online public opinion from different aspects. As our paper extend the research process of online public opinion to take rumor into consideration at two time periods, it not only enriches models of online public opinion diffusion process, but also shed lights on further studies concerning on online public opinion diffusion.</subfield></datafield></record></collection>
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| score |
7.40042 |