Performing Social Media: Artistic Approaches to Analyzing Big Data

Osborne, Tess; Warner, Emily; Jones, Phil; Resch, Bernd

doi:10.1080/2373566x.2018.1543552

Cited by 6 publications

(2 citation statements)

References 29 publications

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“…Osborne et al 44 proposed a strategy to extract diverse storylines from multiple data sets through performance art. The authors contend that merging spatialized big data and art goes beyond the conventional data models explored and conveyed through GIScience.…”

Section: Organization Of Big Data In Social Mediamentioning

confidence: 99%

A taxonomy and survey of big data in social media

Hemmati,

Arzanagh,

Rahmani

2023

Concurrency and Computation

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SummaryExamining the particular value of each platform for big data would be difficult because of the variety of social media forms and sizes. Using social media to objectively and subjectively analyze large groups of individuals makes it the most effective tool for this task. There are numerous sources of big data within the organization. Social media can be identified by the interaction and communication it facilitates. Utilizing social media has become a daily occurrence in modern society. In addition, this frequent use generates data demonstrating the importance of researching the relationship between big data and social media. It is because so many internet users are also active on social media. We conducted a systematic literature review (SLR) to identify 42 articles published between 2018 and 2022 that examined the significance of big data in social media and upcoming issues in this field. We also discuss the potential benefits of utilizing big data in social media. Our analysis discovered open problems and future challenges, such as high‐quality data, information accessibility, speed, natural language processing (NLP), and enhancing prediction approaches. As proven by our investigations of evaluation metrics for big data in social media, the distribution reveals that 24% is related to data‐trace, 12% is related to execution time, 21% to accuracy, 6% to cost, 10% to recall, 11% to precision, 11% to F1‐score, and 5% run time complexity.

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Section: Organization Of Big Data In Social Mediamentioning

confidence: 99%

A taxonomy and survey of big data in social media

Hemmati,

Arzanagh,

Rahmani

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…While interested in big data and network analyses, including concerted efforts to situate these (eg Crampton et al, 2013 ; Mah, 2017 ) we are also inviting researchers who are doing digital research that involves granular, qualitative approaches, including narrative-driven, creative ( Zebracki & Luger, 2019 ) and event focused work. For example, Bork-Hüffer, Mahlknecht, and Kaufmann's (2020) examination of cyberbullying and offline bullying brings together children's narratives of these experiences in relation to geographic processes, while creative digital geographic scholarship such as that by Osborne, Warner, Jones, and Resch (2019) and Thornton (2020) demonstrates the sorts of fine-grained, imaginative refiguring of big data that are possible when formulating artistic engagements with ‘the digital’.…”

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confidence: 99%