An Ontological Approach to Misinformation: Quickly Finding Relevant Information

Hicks, Chelsea

doi:10.24251/hicss.2017.111

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Another emerging, serious issue is the identification and detection of the widespread misinformation on social media and websites. In fact, the use of mega-platforms, such as Facebook and Twitter, as vectors for widespread misinformation spreading, e.g., during tragedies, national crises, or political campaigns, has been the subject of collective anxiety and a growing field of research [247][248][249][250][251]. Moreover, while one of the most beneficial values of text mining in big data analytics for businesses and governments is derived from the monitoring of human behavior and its predictive potential, the massive collection, instantaneous transmission, and combination and reuse of personal information for unforeseen purposes have placed new strains on strictly following the principles of data protection, which calls for a thorough consideration of their applications [252].…”

Section: Discussionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Discussionmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

203

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“…The requirement that business data must be accurate remains a critical issue, with fitness for use being a key criterion (Baesens, Bapna, Marsden, & Vanthienen, 2016). Hicks (2017) notes that ontologies are an understudied area in information systems. Hence, this research makes a significant contribution, extending existing research through illustrating and validating the potential of ontologies as a plausible mechanism by which to integrate both structured and unstructured data from multiple diverse sources, including legacy information systems.…”

Section: Discussionmentioning

confidence: 99%

Distributed data and ontologies: An integrated semantic web architecture enabling more efficient data management

Browne

O’Reilly

Hutchinson

et al. 2019

Asso for Info Science & Tech

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Regulatory reporting across multiple jurisdictions is a significant cost for financial services organizations, due to a lack of systems integration (often with legacy systems) and no agreed industry data standards. This article describes the design and development of a novel ontology‐based framework to illustrate how ontologies can interface with distributed data sources. The framework is then tested using a survey instrument and an integrated research model of user satisfaction and technology acceptance. A description is provided of extensions to an industry standard ontology, specifically the Financial Industry Business Ontology (FIBO), towards enabling greater data interchange. Our results reveal a significant reduction in manual processes, increase in data quality, and improved data aggregation by employing the framework. The research model reveals the range of factors that drive acceptance of the framework. Additional interview evidence reveals that the ontological framework also allows organizations to react to regulatory changes with much‐improved timeframes and provides opportunities to test for data quality.

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