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Connecting People to Avail the Resources During Crisis Through Twitter Using Machine Learning
Author(s) -
Shaikh Areeba Abdul Hakeem,
Manjulata Singh,
Pooja Nandukumar Shingewad,
Shruti Shrikant Nikam
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-3258
Subject(s) - social media , crisis communication , crisis management , public relations , spark (programming language) , resource (disambiguation) , political science , business , internet privacy , computer science , world wide web , computer network , law , programming language
Although social media has become the most widely utilized and active form of communication, research on its usage in crisis management is still in its early stages. As a result, this research examines the rising body of knowledge on social media and crisis management. [1] Between October 2017 and January 2018, a review was conducted, which included locating and retrieving records from an electronic database. The outcomes of this study indicated that the rise of social media has altered the landscape of crisis communication by allowing for greater engagement. However, due to its nature, social media might also be used to spark a crisis. This means that the crisis can be both produced and disseminated through social media. Nonetheless, social media's promise as a crisis-resolution tool is undeniable. It has the capability of proving a claim, dispelling false rumors, or just demonstrating a fact. As a result, practitioners should understand how social media works and how to best use it to interact with their stakeholders. This study also includes other findings, limits, and useful suggestions for scholars and practitioners interested in learning more about the role of social media on crisis communication and management. As most of the crisis problem were reported via twitter. However, most of the problem reported and corresponding responses via twitter were not successfully exchanged between victim’s and resource organization. As a result, most of the tweets were not getting help. Thus, we designed a platform where people can avail the resources of crisis through tweets matching concept using machine learning.

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