Big Data and the Study of Communities and Crime

2018 ◽  
Author(s):  
Daniel T. O'Brien
Keyword(s):  
Social Media ◽  
Big Data ◽  
Computational Social Science ◽  
Data Sources ◽  
Digital Data ◽  
Data Sets ◽  
Popular Imagination ◽  
Administrative Records ◽  
Communities And Crime ◽  
Criminology And Criminal Justice

In recent years, a variety of novel digital data sources, colloquially referred to as “big data,” have taken the popular imagination by storm. These data sources include, but are not limited to, digitized administrative records, activity on and contents of social media and internet platforms, and readings from sensors that track physical and environmental conditions. Some have argued that such data sets have the potential to transform our understanding of human behavior and society, constituting a meta-field known as computational social science. Criminology and criminal justice are no exception to this excitement. Although researchers in these areas have long used administrative records, in recent years they have increasingly looked to the most recent versions of these data, as well as other novel resources, to pursue new questions and tools.

scholarly journals Benchmarking Crisis in Social Media Analytics: A Solution for the Data-Sharing Problem

2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  
Keyword(s):  
Social Media ◽  
Data Sharing ◽  
Algorithm Design ◽  
Computational Social Science ◽  
Evaluation Framework ◽  
Social Media Analytics ◽  
Data Sets ◽  
The Public ◽  
Research Areas ◽  
Media Data

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.

scholarly journals Introduction to the Special Issue on New Longitudinal Data for Retirement Analysis and Policy

2021 ◽  
pp. 1-5
Author(s):  
Marco Angrisani ◽  
Anya Samek ◽  
Arie Kapteyn
Keyword(s):  
Big Data ◽  
Academic Research ◽  
Cost Effective ◽  
Research Data ◽  
Data Sources ◽  
Special Issue ◽  
Administrative Records ◽  
The Past ◽  
Survey Questionnaires ◽  
Internet Panels

The number of data sources available for academic research on retirement economics and policy has increased rapidly in the past two decades. Data quality and comparability across studies have also improved considerably, with survey questionnaires progressively converging towards common ways of eliciting the same measurable concepts. Probability-based Internet panels have become a more accepted and recognized tool to obtain research data, allowing for fast, flexible, and cost-effective data collection compared to more traditional modes such as in-person and phone interviews. In an era of big data, academic research has also increasingly been able to access administrative records (e.g., Kostøl and Mogstad, 2014; Cesarini et al., 2016), private-sector financial records (e.g., Gelman et al., 2014), and administrative data married with surveys (Ameriks et al., 2020), to answer questions that could not be successfully tackled otherwise.

scholarly journals (Re)framing Big Data: Activating Situated Knowledges and a Feminist Ethics of Care in Social Media Research

2018 ◽  
Vol 4 (2) ◽  
pp. 205630511876829 ◽  
Author(s):  
Mary Elizabeth Luka ◽  
Mélanie Millette
Keyword(s):  
Social Media ◽  
Big Data ◽  
Ethics Of Care ◽  
Digital Data ◽  
Future Research ◽  
Feminist Ethics ◽  
Feminist Methods ◽  
Media Research ◽  
Social Media Research ◽  
Political Commitments

In this article, we seek to problematize assumptions and trends in “big data” digital methods and research through an intersectional feminist lens. This is articulated through a commitment to understand how a feminist ethics of care and Donna Haraway’s ideas about “situated knowledge” could work methodologically for social media research. Taking up current debates within feminist materialism and digital data, including big, small, thick, and “lively” data, the argument addresses how a set of coherent feminist methods and a corollary epistemology is being rethought in the field today. We consider how the “queering” of Hannah Arendt’s concept of “action” could contribute to a critically optimistic and inclusive reflection on the role of ethical political commitments to the subjects/objects of study imbricated in big data. Finally, we use our recent research to pose a number of practical questions about practices of care in social media research, pointing toward future research directions.

Jihadist Propaganda on Social Media

2020 ◽  
pp. 1442-1457
Author(s):  
Ahmed Al-Rawi ◽  
Jacob Groshek
Keyword(s):  
Social Media ◽  
Big Data ◽  
Research Method ◽  
Research Study ◽  
Al Qaeda ◽  
Data Sets ◽  
Gradual Decline ◽  
Online Interactions ◽  
Terrorist Groups ◽  
Insight Into

This article focuses on ISIS followers on Twitter in an effort to understand the nature of their social media propaganda. The research study provides unique insight into one of the largest data sets that investigates ISIS propaganda efforts on Twitter by examining over 50 million tweets posted by more than 8 million unique users that referenced the keywords “ISIS” or “ISIL.” The authors then searched this corpus for eight keywords in Arabic that included terms of support for ISIS and the names of different Al-Qaeda leaders. A mixed research method was used, and the findings indicate that ISIS activity on Twitter witnessed a gradual decline, but the group was still able to post different types of tweets to maintain its online presence. Also, the feud between ISIS and Al-Qaeda was intense, ongoing, and prevalent in online interactions among ISIS followers. The study provides an understanding of using big data to better grasp the propaganda activities of terrorist groups.

Big Data and Digital Analytics

2022 ◽  
pp. 571-589
Author(s):  
Sumathi Doraikannan ◽  
Prabha Selvaraj
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Digital Data ◽  
Detailed Knowledge ◽  
Data Sets ◽  
Business Values ◽  
Business Operations ◽  
It Systems ◽  
Basic Concepts ◽  
And Storage

Data becomes big data when then the size of data exceeds the ability of our IT systems in terms of 3Vs (volume, velocity, and variety). When the data sets are large and complex, it becomes a great difficult task for handling such voluminous data. This chapter will provide a detailed knowledge of the major concepts and components of big data and also the transformation of big data in to business operations. Collection and storage of big data will not help out in creation of business values. Values and importance are created once when the action starts on data by performing an analysis. Hence, this chapter provides a view on various kinds of analysis that can be done with big data and also the differences between traditional analytics and big data analytics. The transformation of digital data into business values could be in terms of reports, research analyses, recommendations, predictions, and optimizations. In addition to the concept of big data, this chapter discuss about the basic concepts of digital analytics, methods, and techniques for digital analysis.

scholarly journals Mitigating Bias in Big Data for Transportation

2020 ◽  
Author(s):  
Greg Phillip Griffin ◽  
Megan Mulhall ◽  
Chris Simek ◽  
William W. Riggs
Keyword(s):  
Social Media ◽  
Big Data ◽  
Mobile Phones ◽  
Qualitative Data ◽  
Data Sources ◽  
Transportation Safety ◽  
Safety Planning ◽  
Vehicle Systems

Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

scholarly journals Caring for (Big) Data: An Introduction to Research Methodologies and Ethical Challenges in Digital Migration Studies

2021 ◽  
pp. 1-21
Author(s):  
Marie Sandberg ◽  
Luca Rossi
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Social Activity ◽  
Data Access ◽  
Digital Data ◽  
Data Sets ◽  
Ethical Challenges ◽  
Migration Studies ◽  
Critical Approach ◽  
Migration Research

AbstractDigital technologies present new methodological and ethical challenges for migration studies: from ensuring data access in ethically viable ways to privacy protection, ensuring autonomy, and security of research participants. This Introductory chapter argues that the growing field of digital migration research requires new modes of caring for (big) data. Besides from methodological and ethical reflexivity such care work implies the establishing of analytically sustainable and viable environments for the respective data sets—from large-scale data sets (“big data”) to ethnographic materials. Further, it is argued that approaching migrants’ digital data “with care” means pursuing a critical approach to the use of big data in migration research where the data is not an unquestionable proxy for social activity but rather a complex construct of which the underlying social practices (and vulnerabilities) need to be fully understood. Finally, it is presented how the contributions of this book offer an in-depth analysis of the most crucial methodological and ethical challenges in digital migration studies and reflect on ways to move this field forward.

Citizenship, Social Media, and Big Data

2016 ◽  
Vol 35 (1) ◽  
pp. 3-9 ◽  
Author(s):  
Homero Gil de Zúñiga ◽  
Trevor Diehl
Keyword(s):  
Social Media ◽  
Big Data ◽  
Social Science ◽  
Political Communication ◽  
High Performance ◽  
Large Data ◽  
Computational Social Science ◽  
Communication Research ◽  
Ethical Practices ◽  
Storage Technologies

This special issue of the Social Science Computer Review provides a sample of the latest strategies employing large data sets in social media and political communication research. The proliferation of information communication technologies, social media, and the Internet, alongside the ubiquity of high-performance computing and storage technologies, has ushered in the era of computational social science. However, in no way does the use of “big data” represent a standardized area of inquiry in any field. This article briefly summarizes pressing issues when employing big data for political communication research. Major challenges remain to ensure the validity and generalizability of findings. Strong theoretical arguments are still a central part of conducting meaningful research. In addition, ethical practices concerning how data are collected remain an area of open discussion. The article surveys studies that offer unique and creative ways to combine methods and introduce new tools while at the same time address some solutions to ethical questions.

Educational Big Data and the Promise of Social Media

2018 ◽  
pp. 76-96
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Community Of Inquiry ◽  
Digital Communications ◽  
Data Type ◽  
Data Sources ◽  
Data Context

In Chapter 4, the authors focused on some tools and mindsets that are beneficial for conducting analysis and research in a big data context. In this chapter, they turn their focus to the “data” part of big data and examine some interesting sources to begin to work with. Social Media and related digital communications are the most prominently featured exemplars, but they also discuss other sources of data that can be analyzed. Finally, the authors also survey some interesting recent research being done both in Community of Inquiry and elsewhere that highlights strong data analytics approaches, interesting data sources, and novel conceptualizations of big data-type questions.

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