اطلاعات رسانه های اجتماعی و بازیابی پس از سانحه
ترجمه نشده

اطلاعات رسانه های اجتماعی و بازیابی پس از سانحه

عنوان فارسی مقاله: اطلاعات رسانه های اجتماعی و بازیابی پس از سانحه
عنوان انگلیسی مقاله: Social media data and post-disaster recovery
مجله/کنفرانس: مجله بین المللی مدیریت اطلاعات - International Journal of Information Management
رشته های تحصیلی مرتبط: مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: اینترنت و شبکه های گسترده
کلمات کلیدی فارسی: الگوهای زمانی-مکانی، بازیابی پس از فاجعه، رسانه اجتماعی توییتر
کلمات کلیدی انگلیسی: Temporal–spatial patterns، Post-disaster recovery، Social mediaTwitter
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ijinfomgt.2018.09.005
دانشگاه: Department of Civil, Environmental, and Construction Engineering, Texas Tech University, Office: 104, Lubbock, TX 79409, United States
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 5/579 در سال 2017
شاخص H_index: 82 در سال 2019
شاخص SJR: 1/373 در سال 2017
شناسه ISSN: 0268-4012
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 13
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E10939
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Literature review


3- Theoretical basis


4- Methodology


5- Discussion


6- Conclusion and future work


References

نمونه متن انگلیسی مقاله

Abstract


This study introduces a multi-step methodology for analyzing social media data during the post-disaster recovery phase of Hurricane Sandy. Its outputs include identification of the people who experienced the disaster, estimates of their physical location, assessments of the topics they discussed post-disaster, analysis of the tract-level relationships between the topics people discussed and tract-level internal attributes, and a comparison of these outputs to those of people who did not experience the disaster. Faith-based, community, assets, and financial topics emerged as major topics of discussion within the context of the disaster experience. The differences between predictors of these topics compared to those of people who did not experience the disaster were investigated in depth, revealing considerable differences among vulnerable populations. The use of this methodology as a new Machine Learning Algorithm to analyze large volumes of social media data is advocated in the conclusion.


Introduction


A natural disaster negatively impacts all aspects of one’s life. It can not only devastate the physical settings of a community by destroying infrastructure, the landscape, residential and businesses properties, it can also affect one’s emotional well being after witnessing loss of life and suffering the disruption of established social interactions. Aside from such immediate mental and physical harm, disasters also have long-term consequences, such as job losses, financially insurmountable property damage, and post-traumatic stress disorder (PTSD). Since the routines of daily life are tightly interwoven with the stability of both physical settings and social interactions, disasters upend the tranquility of people’s lives for short, and sometimes long periods of time. A return to normalcy is the ultimate goal of post-disaster recovery policies. A robust understanding of the patterns and types of damage common to disasters in general is crucial in the process of formulating effective post-disaster recovery policies and programs. Disasters are complex. They impact survivors’ quality of life through the damage they inflict on natural and manmade landscapes. The existing literature distinguishes five categories of impacts (Lindell & Prater, 2003): social impacts, such as the appearance of conflicts and the loss of social capital (Lindell & Prater, 2003); psychosocial impacts, such as post-traumatic stress disorder (PTSD) (Gleser, Green, & Winget, 2013; Steinglass & Gerrity, 1990); demographic impacts, such as changes in population distribution (Kaniasty & Norris, 1993; Smith & McCarty, 1996); socioeconomic impacts, such as job loss and business closures (Okuyama & Chang, 2013); and political impacts (Drury & Olson, 1998; Toya & Skidmore, 2014). These impacts are obviously interconnected. For instance, improvements in the socioeconomic condition of a community will influence psychosocial attributes (generally in positive ways), or, abrupt disruption of pre-established social and communal interactions may bring about adverse psychosocial and political reactions. Moreover, the relative importance of these categories can differ among communities and even individuals in the same community who experience the same disaster event, based on the innate characteristics of that community or individual. These characteristics can comprise a many different parameters, including job/income (Fothergill & Peek, 2004; Masozera, Bailey, & Kerchner, 2007), ethnicity (Bolin & Bolton, 1986), and age/gender (Nakagawa & Shaw, 2004), among others.

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