A Big Linked Data Toolkit for Social Media Analysis and Visualization Based on W3C Web Components

Sánchez-Rada, J. Fernando; Pascual, Alberto González; Conde, Enrique Álvarez; Iglesias, Carlos Á.

doi:10.1007/978-3-030-02671-4_30

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…Compared to the cases described before, all of the remaining, including 10 [37], 11 [65], and 26 [73], follow a different aim. In here, the linkage of data from different sources is of major interest.…”

Section: ) Description Of Clustermentioning

confidence: 99%

“…The sixth cluster includes the seven case no. 7 [64], 10 [37], 11 [65], 26 [73], 27 [45], 42 [40] and 43 [33]. When inspecting the cases, two different groups were found out.…”

Section: ) Description Of Clustermentioning

confidence: 99%

“…11 [65] presents an architecture for the processing of data coming from different social media channeld and in [73] a data management system environment that is supposed to deal with unstructured data from various sources is introduced. All of those case studies require the processing of unstructured data [37,65,73]. Most of the considered data such as insurance contracts and claims are in an unstructured textual form [37].…”

Section: ) Description Of Clustermentioning

confidence: 99%

“…For the analysis, data mining techniques can be used and also efficient querying and searching over the data in real-time should be enabled. Further information are provided in [37,65,73].…”

Section: ) Standard Use Case 6 -Data Connectionmentioning

confidence: 99%

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Identifying Similarities of Big Data Projects–A Use Case Driven Approach

et al. 2020

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Big data is considered as one of the most promising technological advancements in the last decades. Today it is used for a multitude of data intensive projects in various domains and also serves as the technical foundation for other recent trends in the computer science domain. However, the complexity of its implementation and utilization renders its adoption a sophisticated endeavor. For this reason, it is not surprising that potential users are often overwhelmed and tend to rely on existing guidelines and best practices to successfully realize and monitor their projects. A valuable source of knowledge are use case descriptions, of which a multitude exists, each of them with a varying information density. In this design science research endeavor, 43 use cases are identified by conducting a thorough literature review in combination with the application and adaption of a corresponding template for big data projects. By a subsequent categorization, which is performed by identifying and employing a hierarchical clustering algorithm, nine different standard use cases emerge, as the contribution's artifact. This provides decision-makers with an initial entry point, which can be utilized to shape their project ideas, not only by identifying the general meaningfulness of their potential big data project but also in terms of concrete implementation details.

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Section: ) Description Of Clustermentioning

confidence: 99%

“…The sixth cluster includes the seven case no. 7 [64], 10 [37], 11 [65], 26 [73], 27 [45], 42 [40] and 43 [33]. When inspecting the cases, two different groups were found out.…”

Section: ) Description Of Clustermentioning

confidence: 99%

Section: ) Description Of Clustermentioning

confidence: 99%

Section: ) Standard Use Case 6 -Data Connectionmentioning

confidence: 99%

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Identifying Similarities of Big Data Projects–A Use Case Driven Approach

et al. 2020

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“…The use of linked data enables reusability of ingestion modules and interoperability and provides a uniform schema for processing data. The ingestion is implemented using GSICrawler [69], the module responsible for retrieving data. GSICrawler contains scraping modules based on Scrapy [70] and other modules that connect to external APIs.…”

Section: Software Architecture and Componentsmentioning

confidence: 99%

Making Sense of Language Signals for Monitoring Radicalization

et al. 2022

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Understanding radicalization pathways, drivers, and factors is essential for the effective design of prevention and counter-radicalization programs. Traditionally, the primary methods used by social scientists to detect these drivers and factors include literature reviews, qualitative interviews, focus groups, and quantitative methods based on surveys. This article proposes to complement social science approaches with computational methods to detect these factors automatically by analyzing the language signals expressed in social networks. To this end, the article categorizes radicalization drivers and factors following the micro, meso, and macro levels used in the social sciences. It identifies the corresponding language signals and available language resources. Then, a computational system is developed to monitor these language signals. In addition, this article proposes semantic technologies since they offer unique exploration, query, and discovery capabilities. The system was evaluated based on a set of competency questions that show the benefits of this approach.

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