Processing Big Data for Emergency Management

2018 ◽  
pp. 144-166 ◽  
Author(s):  
Rajendra Akerkar
Keyword(s):  
Social Media ◽  
Big Data ◽  
Emergency Management ◽  
Emergency Situation ◽  
Emergency Personnel ◽  
Cost Reductions ◽  
Data Points ◽  
Rich Content ◽  
Media Data ◽  
Reduced Risk

Emergencies are typically complex problems with serious consequences that must be solved in a limited amount of time to reduce any possible damage. Big data analysis leads to more assured decision making and better decisions can mean greater operational efficiencies, cost reductions and reduced risk. In this chapter, we discuss some issues on tackling emergency situation from the perspective of big data processing and management, including our approach for processing social media content. Communications during emergencies are so plentiful that it is necessary to sift through enormous data points to find information that is most useful during a given event. The chapter also presents our ongoing IT-system that processes and analyses social media data to transform the excessive volume of low information content into small volume but rich content that is useful to emergency personnel.

Processing Big Data for Emergency Management

2019 ◽  
pp. 980-1000
Author(s):  
Rajendra Akerkar
Keyword(s):  
Social Media ◽  
Big Data ◽  
Emergency Management ◽  
Emergency Situation ◽  
Emergency Personnel ◽  
Cost Reductions ◽  
Data Points ◽  
Rich Content ◽  
Media Data ◽  
Reduced Risk

Emergencies are typically complex problems with serious consequences that must be solved in a limited amount of time to reduce any possible damage. Big data analysis leads to more assured decision making and better decisions can mean greater operational efficiencies, cost reductions and reduced risk. In this chapter, we discuss some issues on tackling emergency situation from the perspective of big data processing and management, including our approach for processing social media content. Communications during emergencies are so plentiful that it is necessary to sift through enormous data points to find information that is most useful during a given event. The chapter also presents our ongoing IT-system that processes and analyses social media data to transform the excessive volume of low information content into small volume but rich content that is useful to emergency personnel.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

A Survey of Social Media, Big Data, Data Mining, and Analytics

2018 ◽  
Vol 03 (03) ◽  
pp. 1850003 ◽  
Author(s):  
Jared Oliverio
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Environmental Education ◽  
Family Businesses ◽  
Large Percentage ◽  
Specific Behavior ◽  
Social Media Data ◽  
Media Applications ◽  
Media Data

Big Data is a very popular term today. Everywhere you turn companies and organizations are talking about their Big Data solutions and Analytic applications. The source of the data used in these applications varies. However, one type of data is of great interest to most organizations, Social Media Data. Social Media applications are used by a large percentage of the world’s population. The ability to instantly connect and reach other people and companies over distributed distances is an important part of today’s society. Social Media applications allow users to share comments, opinions, ideas, and media with friends, family, businesses, and organizations. The data contained in these comments, ideas, and media are valuable to many types of organizations. Through Data Mining and Analysis, it is possible to predict specific behavior in users of the applications. Currently, several technologies aid in collecting, analyzing, and displaying this data. These technologies allow users to apply this data to solve different problems, in different organizations, including the finance, medicine, environmental, education, and advertising industries. This paper aims to highlight the current technologies used in Data Mining and Analyzing Social Media data, the industries using this data, as well as the future of this field.

scholarly journals Navigating Big Data dilemmas: Feminist holistic reflexivity in social media research

2018 ◽  
Vol 5 (2) ◽  
pp. 205395171880773 ◽  
Author(s):  
Cheryl Cooky ◽  
Jasmine R Linabary ◽  
Danielle J Corple
Keyword(s):  
Social Media ◽  
Big Data ◽  
Institutional Review Boards ◽  
Social Media Data ◽  
Institutional Review ◽  
Slow Adaptation ◽  
Private Research ◽  
Media Research ◽  
Social Media Research ◽  
Media Data

Social media offers an attractive site for Big Data research. Access to big social media data, however, is controlled by companies that privilege corporate, governmental, and private research firms. Additionally, Institutional Review Boards’ regulative practices and slow adaptation to emerging ethical dilemmas in online contexts creates challenges for Big Data researchers. We examine these challenges in the context of a feminist qualitative Big Data analysis of the hashtag event #WhyIStayed. We argue power, context, and subjugated knowledges must each be central considerations in conducting Big Data social media research. In doing so, this paper offers a feminist practice of holistic reflexivity in order to help social media researchers navigate and negotiate this terrain.

scholarly journals A Robust Noise Resistant Algorithm for POI Identification from Flickr Data

2017 ◽  
Author(s):  
Yiyang Yang ◽  
Zhiguo Gong ◽  
Qing Li ◽  
Leong Hou U ◽  
Ruichu Cai ◽  
...  
Keyword(s):  
Social Media ◽  
Joint Space ◽  
Zero Crossing ◽  
Social Media Data ◽  
Local Maxima ◽  
Gradient Ascent ◽  
Data Density ◽  
Density Values ◽  
Data Points ◽  
Media Data

Point of Interests (POI) identification using social media data (e.g. Flickr, Microblog) is one of the most popular research topics in recent years. However, there exist large amounts of noises (POI irrelevant data) in such crowd-contributed collections. Traditional solutions to this problem is to set a global density threshold and remove the data point as noise if its density is lower than the threshold. However, the density values vary significantly among POIs. As the result, some POIs with relatively lower density could not be identified. To solve the problem, we propose a technique based on the local drastic changes of the data density. First we define the local maxima of the density function as the Urban POIs, and the gradient ascent algorithm is exploited to assign data points into different clusters. To remove noises, we incorporate the Laplacian Zero-Crossing points along the gradient ascent process as the boundaries of the POI. Points located outside the POI region are regarded as noises. Then the technique is extended into the geographical and textual joint space so that it can make use of the heterogeneous features of social media. The experimental results show the significance of the proposed approach in removing noises.

scholarly journals Making Inferences Using Incidentally Collected Data

2016 ◽  
Author(s):  
Jonathan Mellon
Keyword(s):  
Social Media ◽  
Big Data ◽  
The Internet ◽  
User Interactions ◽  
Social Media Data ◽  
Broad Definition ◽  
User Data ◽  
The Cost ◽  
Media Data

This chapter discusses the use of large quantities of incidentallycollected data (ICD) to make inferences about politics. This type of datais sometimes referred to as “big data” but I avoid this term because of itsconflicting definitions (Monroe, 2012; Ward & Barker, 2013). ICD is datathat was created or collected primarily for a purpose other than analysis.Within this broad definition, this chapter focuses particularly on datagenerated through user interactions with websites. While ICD has beenaround for at least half a century, the Internet greatly expanded theavailability and reduced the cost of ICD. Examples of ICD include data onInternet searches, social media data, and user data from civic platforms.This chapter briefly explains some sources and uses of ICD and thendiscusses some of the potential issues of analysis and interpretation thatarise when using ICD, including the different approaches to inference thatresearchers can use.

A Visual Insight Into Tweeting Activity Before and During Natural Disasters

2021 ◽  
Vol 12 (3) ◽  
pp. 19-33
Author(s):  
Shadi Maleki ◽  
Milad Mohammadalizadehkorde
Keyword(s):  
Social Media ◽  
Big Data ◽  
Spatial Distribution ◽  
Emergency Management ◽  
Data Visualization ◽  
High Dimensionality ◽  
High Complexity ◽  
Big Data Visualization ◽  
Disaster Studies ◽  
Insight Into

Big data provided by social media has been increasingly used in various fields of research including disaster studies and emergency management. Effective data visualization plays a central role in generating meaningful insight from big data. However, big data visualization has been a challenge due to the high complexity and high dimensionality of it. The purpose of this study is to examine how the number and spatial distribution of tweets changed on the day Hurricane Harvey made landfall near Houston, Texas. For this purpose, this study analyzed the change in tweeting activity between the Friday of Hurricane Harvey and a typical Friday before the event.

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