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Ostinato: The Exploration-Automation Cycle of User-Centric, Process-Automated Data-Driven Visual Network Analytics

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Transparency in Social Media

Part of the book series: Computational Social Sciences ((CSS))

Abstract

Network analysis is a valuable method for investigating and mapping the social structure driving phenomena and sharing the findings with others. The interactive visual analytics approach transforms data into views that allow the visual exploration of the structures and processes of networks represented by data, therefore increasing the transparency of editorial processes on social media as well as networked structures in innovation ecosystems and other phenomena. Although existing tools have opened many new exploratory opportunities, new tools in development promise investigators even greater freedom to interact with the data, refine and analyze the data, and explore alternative explanations for networked processes. This chapter presents the Ostinato Model—an iterative, user-centric, process-automated model for data-driven visual network analytics. The Ostinato Model simultaneously supports the automation of the process and enables interactive and transparent exploration. The model has two phases, Data Collection and Refinement and Network Creation and Analysis. The Data Collection and Refinement phase is further divided into Entity Index Creation, Web/API Crawling, Scraping, and Data Aggregation. The Network Construction and Analysis phase is composed of Filtering in Entities, Node and Edge Creation, Metrics Calculation, Node and Edge Filtering, Entity Index Refinement, Layout Processing and Visual Properties Configuration. A cycle of exploration and automation characterizes the model and is embedded in each phase.

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Notes

  1. 1.

    Ideally, a data scientist is a hacker, scientist, quantitative analyst, trusted adviser and business (domain) expert, all in one person (cf. Davenport, 2014).

  2. 2.

    Funding for young innovative companies, http://www.tekes.fi/en/funding/companies/funding-for-young-innovative-growth-companies/

  3. 3.

    Help:Infobox, http://en.wikipedia.org/wiki/Help:Infobox

  4. 4.

    The Terms of Service for a Web page must also be considered. When using Wikipedia as a data source, for example, one has to take into account the Terms of Service that specifically deny crawling Wikipedia for large amount of files. Instead of crawling the live website, users of the data are advised to download a copy of Wikipedia’s contents and set up a proxy for serving further processing.

  5. 5.

    When using names as identifiers, one can apply fuzzy string matching and semi-automated tools such as OpenRefine (http://openrefine.org/) or DataWrangler (http://vis.stanford.edu/wrangler/) to assist in the aggregation process.

  6. 6.

    Gephi Toolkit, http://gephi.github.io/toolkit/

  7. 7.

    Using a full stack programming language such as Python gives the developers more opportunities to turn the scripts developed for analysis into processes that run in the cloud, intermittently collecting and preprocessing the data and feeding results into dashboards implemented in Web technologies.

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Acknowledgement

The research reported in this chapter was funded through resources provided by Tekes—the Finnish Funding Agency for Innovation and mediaX at Stanford University.

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Authors and Affiliations

  1. Tampere University of Technology, Tampere, Finland

    Jukka Huhtamäki

  2. mediaX at Stanford University, Stanford, CA, USA

    Martha G. Russell

  3. University of Electro-Communications, Tokyo, Japan

    Neil Rubens

  4. VTT Technical Research Centre of Finland, Espoo, Finland

    Kaisa Still

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  1. Jukka Huhtamäki
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  2. Martha G. Russell
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  3. Neil Rubens
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  4. Kaisa Still
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Correspondence to Jukka Huhtamäki .

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Editors and Affiliations

  1. Purdue University, West Lafayette, Indiana, USA

    Sorin Adam Matei

  2. mediaX, Stanford University, Stanford, California, USA

    Martha G. Russell

  3. Computer Science, Purdue University, West Lafayette, Indiana, USA

    Elisa Bertino

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Huhtamäki, J., Russell, M.G., Rubens, N., Still, K. (2015). Ostinato: The Exploration-Automation Cycle of User-Centric, Process-Automated Data-Driven Visual Network Analytics. In: Matei, S., Russell, M., Bertino, E. (eds) Transparency in Social Media. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-18552-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-18552-1_11

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