Data Visualisation in Cyberspace: Some characteristics and sociological concerns

Overview

The following report is an analysis of the cyber culture of data visualisation (DV or dataviz). This topic will be explored from four major premises of a subject undertaken at the University of Wollongong in 2017. The first of these arguments will maintain that there is significant authority of governments, bureaucracies and big corporations between the user and the computer, questioning the legitimacy of related structures of command. In support of this first argument, a case study will be introduced to exemplify hegemonic discourse and critique an associated DV. Secondly, it will be argued that contrary views and polarities are a distinctive trait of cyber culture, reflecting speculations and viewpoints, from dystopian and utopian to quotidian, in popular cultural and scholarly works. A third argument will maintain that the cyber culture of data visualisation is associated with exponential trends, proliferating issues of concern outside the scope of this report. In contrast, the limits of cyber culture will be identified along with affordances of visualising data and information online.

Key terms and definitions

Cyber culture will be identified as the sets of ideas associated with digitally enabled communication, networked computation and media technologies.  Ahead, ‘data visualisation’ will refer to ‘the visual representation of statistical and other types of numeric and non-numeric data through the use of static or interactive pictures and graphics’ (Gatto 2015: p.5).  This definition will explore the processes of making data and information visible as distinct from a mental representation of the same.

‘Data’ may be qualitative or quantitative.  According to McLeod, qualitative data gathers non-numerical information: ethnographic, open-ended questionnaires, unstructured interviews and observations, whereas quantitative data is numerical, can be placed in categories, rank order or units of measurement and is presented in graphs and tables of raw data (2008). Data will be distinguished from information ahead (Diffen 2017) in laying groundwork for a later consideration of emergent critical perspectives (Department of Sociological Studies 2016), given the online popularity of the info-graphic.  According to Zins, data, information and knowledge are interrelated key concepts but the nature of their relations and meanings is debatable (2007: p.479).  Ahead, information will be understood in its popular usage to mean facts, figures and representations of things.

Significant background

In a recent publication by the Reuters Institute for the Study of Journalism, Gatto reported an exponential increase in Internet data between 2003- 2014, referring to this phenomenon as a ‘data explosion’ (2015: p.4).  The report’s author noted related academic struggles to keep abreast of change (Gatto 2015: p.4). ‘Data explosion’ in popular culture refers to exponential trends in the amount of available information or data online and is highly relevant to the topic of DV as an answer to ‘information overload’. This latter phrase refers to the impact of too much information on understanding and decision-making,  popularised by Alvin Tofler in his best-selling book Future Shock (1970) and mentioned by American social scientist Bertram Gross in The Managing of Nations (1964).  ‘Infobesity’ is an alternative term for the phrase, identified by Brief as a word people are using to describe an epidemic that is plaguing the world due to overindulgence in information (2013).  The author explains, information, like food, can be harmful when left unprocessed:

An uncontrollable flood of it overwhelms us, and we feel stressed. Our systems shut down, and our capacity to absorb additional information actually decreases (Brief 2013).

A solution to the problem of information overload, DVs or infographics are currently experiencing widespread popularity, as they are able to uncover patterns in large data, make people get a point faster and be easily shared (Gatto 2015: p.15).  Benefits to academics include the improvement of research, theory and analysis, as well as the dissemination, impact or promotion of these academic undertakings (Gatto 2015: p.15). On the other hand, visualisation may aid in the understanding of a story but always from a particular angle (Gatto 2015: p.19).  Similarly, the Australian Bureau of Statistics webpage cautions against ‘spin’ in this ‘information-rich age,’ underscoring the importance of independent critical thinking to accurate understanding, interpretation and evaluation of data to inform research, planning and decision-making (ABS 2017).

De Nardis’ wrote in The Global War for Internet Governance that transformation of information and business by the Internet had been the greatest communication development of the 20th century (Yale Law School 2014).  Significantly, this author also defined conflicts associated with Internet governance as the ‘new spaces where political and economic power is unfolding in the 21st century’ (Yale Law School 2014). In the following section, the first feature of cyberculture will be considered in relation to the topic.

Polarities, futuristic thinking and cultural lag

Fisher and Wright offer an explanation for why polarity and contradiction characterize cyber culture, applying Ogburn’s theory of a cultural lag (2001).  Accordingly, this phenomenon is associated with ideologically charged periods during which heightened hopes and fears reflect the unknown effects of newly introduced technologies (Fisher and Wright 2001).

At a recent sociological conference, this point was exemplified in recognition of a need to reconcile DV as a process that makes data transparent with DV as a form of social control (Department of Sociological Studies 2016). In his bestselling book How to Lie with Statistics, the cultural lag of historical statistical illiteracy was implied by Huff in citing GH Wells’ famous prediction, ‘Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write’ (1954).  The forward thinking nature of Well’s prediction is underscored by Smith’s observation that graphs did not appear in newspapers until 1952 (2017). On the other hand, as Benjamin Disraeli famously stated, ‘There are three kinds of lies: lies, damned lies and statistics.’ Further examples will be offered ahead, underscoring a polarity of views as a prominent feature of cyberculture. In the following section the author will exemplify how significant authority exists in relationship between the user and the computer, concerning DV.

Authority between the user and the computer

Gatekeeping

The relationship between the user and the computer may be understood as a metaphorical ‘gate’ (Shoemaker, Riccio & Johnson 2013). In 1947, Lewin formally identified and described several facets of the gatekeeping process and how it can change the channels of communication, credited with having introduced Lewin’s ideas to the modern age of the Internet (Shoemaker, Riccio & Johnson 2013).  These authors document how the ‘top-down path’ of 20th-century journalism now resides in the annals of history, as a small number of officials and journalists no longer exercise gatekeeping authority (Shoemaker, Riccio & Johnson 2013).

Similarly, in 2013 an Internet Governance Forum attended by senior journalists noted that the reach of public broadcasting is shrinking due to gatekeepers and internet users’ increasing reliance upon them (2013). Today, the public more or less mediate their own news but represent millions of voices with few being heard (Internet Governance Forum 2013).  Since headlines appeared alleging the meddling of Russia in the US election (Kaplan 2017), a charge of ‘fake news’ against mainstream media has witnessed a spate of articles advising readers how to spot the gatekeeper (Kiely and Robertson 2016).

Barzilai-Nahon defined the ‘gate’ as the ‘entrance to or exit from a network or its sections’ (2008: p. 1501).  Significantly, this author described four attributes that determine how individuals can interact with the gate, including: ‘Political power in relation to the gatekeeper, information production ability, relationship with the gatekeeper, and alternatives in the context of gatekeeping’ (Barzilai-Nahon 2008: p. 1501).  In the following section, the author will consider conventions, values and conflicts of interest as inherently of concern to the cyber culture of DV.

Conventions

In looking at the ideological work that data visualisations do, conventions function to produce a sense of ‘objectivity, transparency and facticity’ (DSS 2012) whereas data visualisation may be value-laden, ambiguous or fictitious (Hill 2016).   According to Kennedy et al. four conventions imbue data visualisations with a sense of objectivity, transparency and factuality, including: (a) two-dimensional viewpoints; (b) clean layouts; (c) geometric shapes and lines; (d) the inclusion of data sources (Kennedy et al 2016).  These authors argue, visualisation designers’ stated intentions about the processes they undergo in making information visual helps researchers to understand the qualities of power that operate in and through the production of their works (Kennedy et al 2016).  In the ‘Politics of data visualisation’, Boehnert also argues that ideological assumptions, inherent values and unstated political agendas underpin DV and are implicit in data selection, methods of statistical analysis and styles used to communicate information (2016).  Boehert contends that big data driven visualisations flatten phenomenon and fail to capture the power relations and ideologies associated with highly politicised issues, also raising fears (2016).

Hegemonic discourse

Atherton describes hegemonic discourse as assumptions that are so embedded in a culture that it appears nonsensical to question these ‘norms’ (2013). Moreover, hegemonic discourse determines not only answers but questions that may be asked about a culture (Atherton 2013).  Hegemony is also able to explain why web-based marginalised groups continue to be misrepresented in society despite the voice they have found in the Age of the Internet.  Hegemonic discourse may pervade data visualisation, impeding the relationship between the user and the computer. In Australia, for example, the Commonwealth Government has exercised control over the direction of social policy research since the founding of the Commonwealth Research Bureau in 1944 (Morning Bulletin 1947). For example, boards of research institutes such as the Australian Institute of the Family Studies (AIFS) are accountable to the Parliament of Australia (AIFS 2016). Funds are selectively allocated and validate the political policy agenda of the day.

Statistics Misuse

Of further relevance to data visualisation is the misuse of statistics. According to a glossary of statistical terms by the Organisation for Economic Co-operation and Development (OECD), a statistical rate is the measurement of a phenomenon over time; however, this term may be falsely employed in failing to represent a given population.  Consequently, an unrepresentative sample will draw a conclusion about a population based on a measure that is biased or prejudiced (Nicholas 2013).  According to ‘Aldrich’s study of spurious and genuine correlations’, Francis Galton invented correlation in the 19th century – a concept that is recognised as one of the main milestones of statistics (Aldrich 1995). Info-graphics by the author detail findings of a case study, attached as an online appendix.  This case study features examples of data visualisations that contradict nationally funded Australian research findings and critique a related hegemonic discourse (Appendix). This study critically examines a DV attached to an Australian Government commissioned report in which a ‘statistical rate’ is associated with an ‘unrepresentative sample,’ leading to spurious correlations and the misrepresentation of an historically marginalised group.

Exponential concerns and issues

Exponential trends in the growth of data and technology are prominent in the Age of the Internet, proliferating issues and concerns. For example, according to Strickland, the author of the Singularity proposed that ‘mankind is heading toward an irrevocable destiny in which we will evolve beyond our understanding through the use of technology (2017), predicting that this event will occur in 2045 on the assumption of Moore’s Law (Think Big 2017). Conversely, Gartner’s Hype Cycle for Emerging Technologies is an info-graphic that purports to predict when emerging technologies will ‘plateau’ out as S curves (Gartner 2017).

Given the exponential rate of technological change associated with DV, a lag in learning may present a danger of information overload due to entropy. Moore explains entropy as the decay that occurs ‘(o)nce a system has to expend more energy (funds, labor etc) to ensure its expansion than it has available’ (Slack 2017).

Entropy

Norwegian sociologist Johan Galtung put forward a theory of social entropy in 1967 (Hmolpedia online Encyclopaedia of Human Thermodynamics, Human Chemistry, and Human Physics,  2017), describing personal and social forces of both high and low variety.  For example, in systems of high entropy, push factors are ‘too much dissonance, information overload and ambivalence’ at the individual level (2017).

Williams and Srnicek oppose ‘hegemonic powers of the right’ in Accelerate Manifesto for an Accelerationist Politics (2013).  These proponents push ‘towards a future that is (the) altern­at­ive mod­ern­ity neo­lib­er­al­ism is inher­ently unable to gen­er­ate’ (2013).  The authors offer a ‘radically new social, political, organisational, and economic vision’ in the face of ever-accelerating catastrophes of ‘a secular crisis in capitalism’ (Williams and Srnicek 2013).  The authors see these dangers apparent in the generation of new ideas and modes of organisation, and in increasing automation in production processes, including intellectual (Williams and Srnicek 2013). The following section concludes this report, noting limitations and affordances of our technology infused realities.

 Limits and Affordances

A significant lag in learning is associated with exponential trends in cyber technology.  A recent report by Reuters Institute for the study of Journalism (Gatto 2015) noted for example, challenges social scientists face in adopting DV as a research and dissemination tool, including a lack of knowledge about software and platforms that produce DVs (Gatto 2015). Healy and Moody describe sociology as ‘lagging in the use of visual tools’ despite the discipline’s promising beginnings (Healy and Moody 2014 p. 105).  Sociology and social science developed in the first half of the 20th century – the age of formal methods in statistics and social science, when there was ‘little innovation in graphical methods’ (Heer 2011: p.5).  Highlighting the difference between worlds, authors note that during this period data was presented in tables (Healy and Moody 2014: p.108).

According to Provost, Rosling once lamented the limitations of quantitative analysis for data visualisation, stating: My interest is not data, it’s the world. And part of world development you can see in numbers. Others, like human rights, empowerment of women, it’s very difficult to measure in numbers…’ (Provost 2013).

Data visualization expert at the University of Washington, Professor Jeffrey Heer aims ‘to enhance people’s ability to understand and communicate data through the design of new interactive systems for data visualization and analysis’ and by the study of perceptual, cognitive, and social factors affecting the process (2017). Importantly, Heer identifies a need to increase scepticism of data and consider new hypotheses, inquiring, ‘Are we asking too much of technology and not enough of ourselves?’ (2015).  Exemplifying unwanted trends in reference to the work of his own students, Heer emphasises the importance of decision making over design variation, exploration over specification, and data variation over design variation (2015).

Contradictorily, Associate Professor at the University of Michigan, Eytan Adar has banned the word ‘”explore” from all project proposals in (his) infovis class,’ calling for a more bounded approach to evaluating ‘a design decision in context of a specific set of tasks’ (n.d.).  David McCandless emphasises the need to unpack the richer meanings in data by employing statistical models that visualise relativity of phenomena, regarding DV as a solution to the ‘data glut’ (2010).

Rosling noted the limitations of data, arguing that publicly funded and searchable data is necessary for further development on online interactive graphics software (2013). McLelland notes that big data is less actionable than some might think, considering much of it is derived from the number of connected devices in the Internet Universe (2015). However, McLelland cites significant findings of the latest Digital Universe report, persuading online audiences to ‘hone in on high-value, “target-rich data” that is easy to access…available in real-time, has a large footprint (and) can effect meaningful change’ (McLelland 2015).  In an online publication for the World Economic Forum, Patil wrote about key ways in which data science is changing the world, citing a post-graduate employment rate of 100% in the discipline (2014). Concluding this section on a note of global significance, a recent study by the McKinsey Global Institute found that in order to benefit from Big Data, significant training will be necessary to equip the four to five million jobs required in the U.S.alone for data analysis skills by 2018 (cited by the University of California 2017).

Conclusion

This report has underscored the impact of cyberculture on DV. Background information has identified information overload as of particular concern due to an exponential increase of data. The significant authority of governments, bureaucracies and big corporations between the user and the computer, contrary views and polarities, and a proliferation of issues and concerns have raised traits of cyberculture that have shaped the development of DV. These have suggested why humanity is struggling to adjust to technological advancement. Importantly, limits and affordances have acknowledged constructive positive notes on which to conclude this report.

Words 2750

 

References

Aldrich, J 1995, ‘’Correlations genuine and spurious in Pearson and Yule’, Statistical Science, vol 10, no 4, pp. 364-376.

Australian Institute of Family Studies (AIFS) 2017, About AIFS, Australian Government, viewed 21 May 2017, <https://aifs.gov.au/about-us&gt;.

Atherton, JS 2013, The theory of theory, Doceo, viewed 21 May 2017, <http://www.doceo.co.uk/about.htm&gt;.

Australian Bureau of Statistics (ABS) 2017, What is statistical literacy and why is it important to be statistically literate, viewed 6 May 2017, <http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/1307.6Feature+Article1Mar+2009&gt;.

Barzilai-Nahon, K 2008, ‘Toward a Theory of Network Gatekeeping: A Framework for Exploring Information Control’, Journal of the American Information Science and Technology, vol 59, no 9.

Boehnert, J 2015, ‘Viewpoint: The politics of data visualisation’, Discover Society,  3 August, viewed 21 May 2017, <http://discoversociety.org/2015/08/03/viewpoint-the-politics-of-data-visualisation/&gt;.

Boyle, K 2000, ‘An Introduction to Gatekeeper: The Government’s Public Key Infrastructure’, Journal of Law, Information and Science, vol 38.

Brief, B 2013, Infobesity: The enemy of good decisions, 11 June, viewed 6 June 2017, <http://www.bain.com/publications/articles/infobesity-the-enemy-of-good-decisions.aspx&gt;.

Cavaness, C 2014, Historically marginalized groups in psychological science, University of Rhode Island, viewed 25 May 2017, <http://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1253&context=oa_diss&gt;.

Chowdery, N 2015, Infobesity – The Science of Too Much Information, 16 September, viewed 25th May 2017, <http://www.icytales.com/infobesity-the-science-of-too-much-information/&gt;.

Critical Thought TV 2012, Jason Silva | The Emerging Singularity, video, YouTube, 1 April, viewed 25th May 2017, <http://youtu.be/YkM-w-bxa8Y?t=4m40s&gt;.

Cruz , X  2013, Participatory Action Research (PAR) for Sustainable Community Development,  Postgrowth, viewed 25 May 2017, <http://postgrowth.org/participatory-action-research-par-for-sustainable-community-development/&gt;.

Diffen 2017, Data vs. Information, viewed 11 May 2017, <http://www.diffen.com/difference/Data_vs_Information#.22Misleading.22_Data&gt;.

Department of Sociological Studies (DSS) 2012, Culture and politics of data visualisation: A one-day conference, University of Sheffield, viewed 11 May 2017,<https://www.sheffield.ac.uk/socstudies/events/culturedatavis&gt;.

Fisher, DR & Wright, LH 2001, ‘On Utopias and Dystopias: Toward an Understanding of the Discourse Surrounding the Internet’, Computer Mediated Communication, vol 6, no 2.

Higgins, D 2012, Past and present adoptions in Australia, Australian Institute of Family Studies, Australian Government, viewed 25 May 2017, <https://aifs.gov.au/publications/past-and-present-adoptions-australia&gt;

Nicholas, L 2013, Biased/Unrepresentative sample, presentation, Prezi, viewed 22 May 2017, < https://prezi.com/vq1xhmtd9s7t/biasedunrepresentative-sample/&gt;.

Gartner, JR 2016, ‘Gartner’s 2016 hype cycle for emerging technologies’, Gartner, 11 August, viewed 22 May 2017, <http://www.gartner.com/newsroom/id/3412017&gt;.

Gatto, MAC 2015, Making Research Useful: Current Challenges and Good Practices in Data Visualisation, Reuters Institute for the Study of Journalism, University of Oxford, viewed 11 May 2017, <https://reutersinstitute.politics.ox.ac.uk/sites/default/files/Making%20Research%20Useful%20-%20Current%20Challenges%20and%20Good%20Practices%20in%20Data%20Visualisation.pdf&gt;.

Gross, BM 1964, The managing of organisations: The administrative struggle, Volume 2, MacMillan, London.

Healy, K & Moody, M 2014, ‘Data visualization in Sociology’, Annual Review of Sociology, vol 40, pp. 105-128.

Heer, JM 2011, What was the first data visualization?, Stanford University, viewed 22 May 2017, <http://hci.stanford.edu/courses/cs448b/f11/lectures/CS448B-20111006-EDA.pdf&gt;.

2015, The future of data visualization, video, YouTube, 20 February, viewed 22 May 2017, <https://www.youtube.com/watch?v=vc1bq0qIKoA&gt;.

2017, About, University of Washington, viewed 22 May 2017, <https://homes.cs.washington.edu/~jheer/&gt;.

Hegemony – Ten minute philosophy – Terms 2015, video, Youtube, 27 December, viewed 21 May 2017, <https://youtu.be/js8E6C3ZnJ0?t=3m59s&gt;.

Hill, RL 2016, Making sense of data visualisations, Seeing Data, viewed 11 May 2017, <http://seeingdata.org/seeing-data-blog/big-data-visualisation-ideological-work-data-visualisation-conventions/&gt;.

Huffpost UK nd, Brave new world drug, Pinterest,  viewed 25 May 2017, <https://au.pinterest.com/explore/brave-new-world-drug/&gt;.

Internet Governance Forum 2013, IGF2008-WS20-Internet Governance Forum, video, Youtube, 19 April, viewed 21 May 2017, <https://www.youtube.com/watch?v=u2L7tpDhXs8&gt;.

Kaplan, A 2017, ‘As Senate Investigates Russia’s Role In Proliferation Of Fake News, Fake News Purveyors Deny Any Trump-Russia Connection’, Media Matters, viewed 15 May 2017, <https://www.mediamatters.org/research/2017/03/30/senate-investigates-russias-role-proliferation-fake-news-fake-news-purveyors-deny-any-trump-russia/215873.

Kennedy, H, Hill, RC, Aiello, G & Allen, W 2016, ‘The work that data visualisations do’, Information, Communications and Society, vol 19, no 6, pp. 715-735.

Kiely, E & Robertson, L 2016, ‘How to spot fake news’, FactCheck.org, viewed 15 May 2017, http://www.factcheck.org/2016/11/how-to-spot-fake-news/&gt;.

Labs, SS 2016, ‘The real prerequisite for machine learning isn’t math, it’s data analysis’, blog post, R-Bloggers, 16 May, viewed 25 May 2017, <https://www.r-bloggers.com/the-real-prerequisite-for-machine-learning-isnt-math-its-data-analysis/&gt;.

Lasquite, M 2015, ‘Solutions to the Biggest Challenges in Creating Infographics’, blog post,  VisMe,1 July, viewed 25 May 2017, <http://blog.visme.co/solutions-to-the-biggest-challenges-in-creating-infographics-part-1/&gt;.

Marrero, D 2015, ‘Chinese Internet restrictions affect social media, search engines’, Suffolk Journal, viewed 15 May 2017.

McCandless 2010, The beauty of data visualization, video, Ted, July, viewed 22 May 2017, <https://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization&gt;.

McLeod, S 2008, Qualitative vs quantative, Simply Psychology, viewed 25th May 2017, <http://www.simplypsychology.org/qualitative-quantitative.html&gt;.

McLelland, C 2015, The internet of things and big data: Unlocking the power, ZDNET, 2 March, viewed 22 May 2017, <http://www.zdnet.com/article/the-internet-of-things-and-big-data-unlocking-the-power/&gt;.

Morning Bulletin 1947, ‘Growth of Social Services’, 22 May, p. 7, viewed 21 September 2011, <http://nla.gov.au/nla.news-article56782635&gt;.

Palmer, N 2012, ‘Hegemony: The haves and “soon-to-haves”’, Sociology in Focus, viewed 19 May 2017, <http://sociologyinfocus.com/tag/hegemony/&gt;.

Patil, DJ 2014, ‘Three ways data science is changing the world’, World Economic Forum, viewed 11 May 2017, <http://amp.weforum.org/agenda/2014/03/three-ways-data-science-changing-world&gt;.

Provost, C 2013, 17 May, viewed 22 May 2017, <https://www.theguardian.com/global-development/2013/may/17/hans-rosling-data-population-fertility&gt;.

Rosling, H 2013, The best stats you’ve ever seen, video, Ted, 13 July, viewed 25 May 2017, <https://www.ted.com/playlists/474/the_best_hans_rosling_talks_yo&gt;.

2014, How not to be ignorant about the world, Ted, 11 September, viewed 25 May 2017, <https://www.ted.com/talks/hans_and_ola_rosling_how_not_to_be_ignorant_about_the_world&gt;.

Shoemaker, P, Riccio, J & Johnson, P 2013, Gatekeeping, Oxford Bibliographies, viewed 14 May 2017, <http://www.oxfordbibliographies.com/view/document/obo-9780199756841/obo-9780199756841-0011.xml&gt;.

Shoemaker, PJ & Reese, SD 2013. Mediating the message: A media sociology perspective. 3d ed. New York: Routledge.

Smith, A 2016, Unpacking data visualisation, video, Youtube, 8 September, viewed 21 May 2017, <https://www.youtube.com/watch?v=AU1CouHqQQY&feature=youtu.be&gt;.

Strickland, J 2017, ‘What’s the technological singularity?’, nd, viewed 25th May 2017, <http://electronics.howstuffworks.com/gadgets/high-tech-gadgets/technological-singularity.htm&gt;.

Think Big Editors 2017, ‘Big idea: Technology grows exponentially’, viewed 19 May 2017, <http://bigthink.com/think-tank/big-idea-technology-grows-exponentially&gt;.

Turney, J 2015, ‘Modelling liveable futures’, blog post, Future Earth, 26 February, viewed 25 May 2017, <http://www.futureearth.org/blog/2015-feb-26/modelling-liveable-futures&gt;.

University of California 2017, ‘What is data science: A new field emerges’, viewed 5 Jun 2017, <https://datascience.berkeley.edu/about/what-is-data-science/&gt;.

Weber, B 2013, 5 June, Something Google, Yahoo, Facebook, And Instagram Are Doing Better At Than The Entire United States, Upworth, viewed 25 May 2017, <http://www.upworthy.com/congratulations-youre-having-a-baby-now-about-that-time-off-thing?c=bl3&gt;.

Zins, C 2007, ‘Conceptual approaches for defining data, information, and knowledge’, Journal of the American Society for Information Science and Technology, vol 58, no 4.

 

 

 

 

 

 

 

 

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