Reasoning human emotional responses from large-scale social and public media
The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Rec...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Li, Xianghua [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Systematik: |
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| Umfang: |
12 |
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| Übergeordnetes Werk: |
Enthalten in: Geodesic synchrotron radiation in black hole spacetimes: Analytical investigation - Moreira, Zeus S. ELSEVIER, 2021, New York, NY |
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| Übergeordnetes Werk: |
volume:310 ; year:2017 ; day:1 ; month:10 ; pages:182-193 ; extent:12 |
| Links: |
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| DOI / URN: |
10.1016/j.amc.2017.03.031 |
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ELV035667974 |
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| 520 | |a The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. | ||
| 520 | |a The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. | ||
| 700 | 1 | |a Wang, Zhen |4 oth | |
| 700 | 1 | |a Gao, Chao |4 oth | |
| 700 | 1 | |a Shi, Lei |4 oth | |
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10.1016/j.amc.2017.03.031 doi GBVA2017002000005.pica (DE-627)ELV035667974 (ELSEVIER)S0096-3003(17)30215-1 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ UA 1000 VZ rvk (DE-625)rvk/145215: 33.40 bkl 33.50 bkl 39.22 bkl Li, Xianghua verfasserin aut Reasoning human emotional responses from large-scale social and public media 2017transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. Wang, Zhen oth Gao, Chao oth Shi, Lei oth Enthalten in Elsevier Moreira, Zeus S. ELSEVIER Geodesic synchrotron radiation in black hole spacetimes: Analytical investigation 2021 New York, NY (DE-627)ELV006733727 volume:310 year:2017 day:1 month:10 pages:182-193 extent:12 https://doi.org/10.1016/j.amc.2017.03.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHY SSG-OPC-AST UA 1000 Referateblätter und Zeitschriften Physik Referateblätter und Zeitschriften (DE-625)rvk/145215: (DE-576)329175343 33.40 Kernphysik VZ 33.50 Physik der Elementarteilchen und Felder: Allgemeines VZ 39.22 Astrophysik VZ AR 310 2017 1 1001 182-193 12 045F 510 |
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10.1016/j.amc.2017.03.031 doi GBVA2017002000005.pica (DE-627)ELV035667974 (ELSEVIER)S0096-3003(17)30215-1 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ UA 1000 VZ rvk (DE-625)rvk/145215: 33.40 bkl 33.50 bkl 39.22 bkl Li, Xianghua verfasserin aut Reasoning human emotional responses from large-scale social and public media 2017transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. Wang, Zhen oth Gao, Chao oth Shi, Lei oth Enthalten in Elsevier Moreira, Zeus S. ELSEVIER Geodesic synchrotron radiation in black hole spacetimes: Analytical investigation 2021 New York, NY (DE-627)ELV006733727 volume:310 year:2017 day:1 month:10 pages:182-193 extent:12 https://doi.org/10.1016/j.amc.2017.03.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHY SSG-OPC-AST UA 1000 Referateblätter und Zeitschriften Physik Referateblätter und Zeitschriften (DE-625)rvk/145215: (DE-576)329175343 33.40 Kernphysik VZ 33.50 Physik der Elementarteilchen und Felder: Allgemeines VZ 39.22 Astrophysik VZ AR 310 2017 1 1001 182-193 12 045F 510 |
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10.1016/j.amc.2017.03.031 doi GBVA2017002000005.pica (DE-627)ELV035667974 (ELSEVIER)S0096-3003(17)30215-1 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ UA 1000 VZ rvk (DE-625)rvk/145215: 33.40 bkl 33.50 bkl 39.22 bkl Li, Xianghua verfasserin aut Reasoning human emotional responses from large-scale social and public media 2017transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. Wang, Zhen oth Gao, Chao oth Shi, Lei oth Enthalten in Elsevier Moreira, Zeus S. ELSEVIER Geodesic synchrotron radiation in black hole spacetimes: Analytical investigation 2021 New York, NY (DE-627)ELV006733727 volume:310 year:2017 day:1 month:10 pages:182-193 extent:12 https://doi.org/10.1016/j.amc.2017.03.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHY SSG-OPC-AST UA 1000 Referateblätter und Zeitschriften Physik Referateblätter und Zeitschriften (DE-625)rvk/145215: (DE-576)329175343 33.40 Kernphysik VZ 33.50 Physik der Elementarteilchen und Felder: Allgemeines VZ 39.22 Astrophysik VZ AR 310 2017 1 1001 182-193 12 045F 510 |
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10.1016/j.amc.2017.03.031 doi GBVA2017002000005.pica (DE-627)ELV035667974 (ELSEVIER)S0096-3003(17)30215-1 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ UA 1000 VZ rvk (DE-625)rvk/145215: 33.40 bkl 33.50 bkl 39.22 bkl Li, Xianghua verfasserin aut Reasoning human emotional responses from large-scale social and public media 2017transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. Wang, Zhen oth Gao, Chao oth Shi, Lei oth Enthalten in Elsevier Moreira, Zeus S. ELSEVIER Geodesic synchrotron radiation in black hole spacetimes: Analytical investigation 2021 New York, NY (DE-627)ELV006733727 volume:310 year:2017 day:1 month:10 pages:182-193 extent:12 https://doi.org/10.1016/j.amc.2017.03.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHY SSG-OPC-AST UA 1000 Referateblätter und Zeitschriften Physik Referateblätter und Zeitschriften (DE-625)rvk/145215: (DE-576)329175343 33.40 Kernphysik VZ 33.50 Physik der Elementarteilchen und Felder: Allgemeines VZ 39.22 Astrophysik VZ AR 310 2017 1 1001 182-193 12 045F 510 |
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10.1016/j.amc.2017.03.031 doi GBVA2017002000005.pica (DE-627)ELV035667974 (ELSEVIER)S0096-3003(17)30215-1 DE-627 ger DE-627 rakwb eng 510 510 DE-600 530 VZ UA 1000 VZ rvk (DE-625)rvk/145215: 33.40 bkl 33.50 bkl 39.22 bkl Li, Xianghua verfasserin aut Reasoning human emotional responses from large-scale social and public media 2017transfer abstract 12 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. Wang, Zhen oth Gao, Chao oth Shi, Lei oth Enthalten in Elsevier Moreira, Zeus S. ELSEVIER Geodesic synchrotron radiation in black hole spacetimes: Analytical investigation 2021 New York, NY (DE-627)ELV006733727 volume:310 year:2017 day:1 month:10 pages:182-193 extent:12 https://doi.org/10.1016/j.amc.2017.03.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHY SSG-OPC-AST UA 1000 Referateblätter und Zeitschriften Physik Referateblätter und Zeitschriften (DE-625)rvk/145215: (DE-576)329175343 33.40 Kernphysik VZ 33.50 Physik der Elementarteilchen und Felder: Allgemeines VZ 39.22 Astrophysik VZ AR 310 2017 1 1001 182-193 12 045F 510 |
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Reasoning human emotional responses from large-scale social and public media |
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The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. |
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The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. |
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The basic characteristics of extreme events are their infrequence and potential damages to the human–nature system. It is difficult for people to design comprehensive policies for dealing with such events due to time pressure and their limit knowledge about rare and uncertain sequential impacts. Recently, online media provides digital source of individual and public information to study collective human responses to extreme events, which can help us reduce the damages of an extreme event and improve the efficiency of disaster relief. More specifically, there are different emotional responses (e.g., anxiety and anger) to an event and its subevents during a whole event, which can be reflected in the contents of public news and social media to a certain degree. Therefore, an online computational method for extracting these contents can help us better understand human emotional states at different stages of an event, reveal underlying reasons, and improve the efficiency of event relief. Here, we first employ tweets and reports extracted from Twitter and ReliefWeb for text analysis on three distinct events. Then, we detect textual contents by sentiment lexicon to find out human emotional responses over time. Moreover, a clustering-based method is proposed to detect emotional responses to a certain episode during events based on the co-occurrences of words as used in tweets and/or articles. Taking Japanese earthquake in 2011, Haiti earthquake in 2010 and Swine influenza A (H1N1) pandemic in 2009 as case studies, we reveal the underlying reasons of distinct patterns of human emotional responses to the whole events and their episodes. |
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