On the research for big data uses for public good purposes

Decuyper, Adeline

doi:10.4000/netcom.2556

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…From a technical perspective, the process from generation to the utilization of big data can be roughly divided into four stages, aggregation, collection, retention, and personalization. Besides the general definition of big data, big data also has the characteristics of velocity, volume, veracity, and variety [1]. Big data and its value can be applied through different means to various fields.…”

Section: Introductionmentioning

confidence: 99%

Big Data: Quasi-public Goods Correlating with National Security and Social Stability

Zhang¹,

Wang²,

Liu³

et al. 2022

Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)

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In the Internet era, data is occupying an increasingly important position, and some important data may even affect the country. More and more people demand that big data correlating with national security and social stability be treated as public goods which hold the characteristics of non-excludable and nonrivalrous. However, due to the economic attribute of big data, we proposes that this type of big data is excludable and non-rivalrous, and should be treated as quasi-public goods. To support this point of view, we quoted two examples, DiDi Chuxing and Cambridge analytics, and give solutions respectively. We believes that the government, as a public service provider, should supervises the use of big data rather than the content of big data. It not only protects the private rights of enterprises to data, but also avoids big data leaks that threaten national security.

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Section: Introductionmentioning

confidence: 99%

Big Data: Quasi-public Goods Correlating with National Security and Social Stability

Zhang¹,

Wang²,

Liu³

et al. 2022

Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022)

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“…the "personal big data", is enabling a datafication of the human life and facilitating the creation of rich composite personas of different users [1]. With 1.4 billion smartphones and millions of quantified-self device users, more and more users keep track of their behavior, which has already shown value in the fields of healthcare, well-being, and urban planning [2], [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Altrumetrics: Inferring Altruism Propensity Based on Mobile Phone Use Patterns

Bati

Singh

2021

IEEE Trans. Big Data

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Altruism, i.e. an act that one does at their own expense that tends to enhance others well-being, is a fundamental human behavior with implications for personal and societal welfare. Hence, modeling altruism is an important building block in designing humancentered computing systems. Traditional methods for understanding an individual's altruistic propensities have been surveys and lab experiments. However, the emerging "personal big data" coming from mobile and ubiquitous devices allows for creation of lower-cost, quicker, automated methods for modeling human behaviors and propensities. We propose a new methodology to model altruism using phone data. Based on analysis of data from a 10-week field study (N=55 participants), we report that: (1) multiple phone-based features are associated with users' altruistic propensities; (2) phone features-based altruism prediction model yielded significantly better performance than a demography-based model. The results pave way for utilizing "personal big data" to model altruism in multiple commercial and social applications.

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“…Given the evergrowing popularity of the Internet, smartphones, and social media, these individuals generate data that are able to be acquired and analyzed. As such, various groups including academics, clinicians, nongovernmental organizations, and governmental bodies have seized the opportunity to make use of various forms of dataincluding data related to natural disaster relief and disease epidemiology, among others-in the service of positive social impact (Decuyper, 2016). In this article, we describe our current research involving unstructured social media data generated by a marginalized, often misunderstood societal group: gang-involved and affiliated young people.…”

mentioning

confidence: 99%

Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data

Frey

Patton

Gaskell

et al. 2018

Social Science Computer Review

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Mining social media data for studying the human condition has created new and unique challenges. When analyzing social media data from marginalized communities, algorithms lack the ability to accurately interpret off-line context, which may lead to dangerous assumptions about and implications for marginalized communities. To combat this challenge, we hired formerly gang-involved young people as domain experts for contextualizing social media data in order to create inclusive, community-informed algorithms. Utilizing data from the Gang Intervention and Computer Science Project—a comprehensive analysis of Twitter data from gang-involved youth in Chicago—we describe the process of involving formerly gang-involved young people in developing a new part-of-speech tagger and content classifier for a prototype natural language processing system that detects aggression and loss in Twitter data. We argue that involving young people as domain experts leads to more robust understandings of context, including localized language, culture, and events. These insights could change how data scientists approach the development of corpora and algorithms that affect people in marginalized communities and who to involve in that process. We offer a contextually driven interdisciplinary approach between social work and data science that integrates domain insights into the training of qualitative annotators and the production of algorithms for positive social impact.

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On the research for big data uses for public good purposes

Cited by 5 publications

References 15 publications

Big Data: Quasi-public Goods Correlating with National Security and Social Stability

Big Data: Quasi-public Goods Correlating with National Security and Social Stability

Altrumetrics: Inferring Altruism Propensity Based on Mobile Phone Use Patterns

Artificial Intelligence and Inclusion: Formerly Gang-Involved Youth as Domain Experts for Analyzing Unstructured Twitter Data

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