Abstract
This chapter takes a closer look at global knowledge dynamics, that is, how the distributed entities that are embodied in humans and networks of humans around the world may become related, or linked. In doing so, it focuses on key dynamics within digital culture that help or hinder people reproduce, reconstruct, or change knowledge across existing boundaries.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Cited in I. ibn Musa Abu Ishaq al-Shatibi (1884), Al -Muwafaqat fi Usul Al-Sharai’a, 1/75, Tunis (716–778).
- 2.
The entrepreneurial significance of such an understanding was once described by Steve Jobs as follows: ‘You know we’re constantly taking. We don’t make most of the food we eat, we don’t grow it, anyway. We wear clothes other people make, we speak a language other people developed, we use a mathematics other people evolved and spent their lives building. I mean we’re constantly taking things. It’s a wonderful ecstatic feeling to create something and put it into the pool of human experience and knowledge.’—quote from an interview with Steve Jobs in 1983. Available in excerpts from http://bits.blogs.nytimes.com/2014/01/24/the-30-year-old-macintosh-and-a-lost-conversation-with-steve-jobs/.
- 3.
For a valuable overview on the distinction between explicit and tacit knowledge, see Harry Collins (2010), Tacit and explicit knowledge. Chicago: Chicago University Press.
- 4.
Empirical approaches like Paolillo’s are rarely employed in debates on linguistic diversity dynamics. This is further complicated by inconsistent and outdated statistics during the process of information gathering (cf. Gerrand, 2007).
- 5.
In addition, each language version of Wikipedia is governed and run by its respective editors. The respective language policy debate thus can be distinct from others, which makes it an interesting case also for language planning.
- 6.
One useful tool for analysing Wikipedia’s statistics is the Wikimedia Tool-labs. Wikimedia Tool-labs is hosted by Wikimedia Labs and provides sophisticated access to the Wikipedia database for researchers. For more information, see https://wikitech.wikimedia.org/wiki/Main_Page (retrieved 20 February 2016).
- 7.
First, it is straightforward to assign the region to the official language that no other nation state uses. Second, for those states that do have more than one official language, and if some of the official languages are already assigned (e.g., Russian for Belarus and English for Ireland), they are deliberately removed from that region. The results are that all European official language versions of Wikipedia are assigned, with Belgium, Switzerland, and Cyprus left out.
- 8.
For visual examples of choropleth maps and cartograms for geographic and linguistic characteristics, see Graham, 2009, Petzold & Liao, 2011, and http://www.worldmapper.org/.
- 9.
Apart from that, it also links to previous media research on geolinguistic regions, which has long been concerned with international trade of media formats (Albizu, 2007; Sinclair, 1996). With these techniques we can argue that by using geolinguistic analysis we are able to gain more insights into the diffusion dynamics of knowledge entities in general.
- 10.
Definition of inter-language link by Wikimedia: ‘The inter-language link is intended to link articles in the different languages together.’ Cf. http://meta.wikimedia.org/wiki/A_newer_look_at_the_interlanguage_link (retrieved 12 April 2010).
- 11.
A few others have also visualised these linkages of knowledge entities across languages. One of the projects that stands out is Omnipedia by researchers at Northwestern University. Retrieved from http://omnipedia.northwestern.edu (retrieved 2 May 2015).
- 12.
To be comprehensive: it also has been found that nonmatching topics were sometimes interlinked (Hecht & Gergle, 2010).
- 13.
In fact, we have observed some large-scale (yet often failed) attempts of knowledge shifts from knowledge-rich source languages to target languages where less knowledge existed on Wikipedia. For instance, Google and Wikipedia formed a joint venture in 2010 for which Google provided its translation software to help a team of volunteers, translators, and Wikipedia contributors across India, the Middle East, and Africa to translate several million words for Wikipedia into languages such as Arabic, Gujarati, Hindi, Kannada, Swahili, Tamil, and Telugu.
- 14.
As a brief methodological note, the selected raw data was generated with the Wikimedia Toolserver, which was then processed with the programming scripts written by Han-Teng Liao to produce a network graph file. In a next step, the network graph file was fed into social network analysis and exploring tools such as NodeXL and UCINET. The tentative graph shown in this chapter is produced by UCINET, with the spring embedding layout. The settings for the layout are as follows: the criteria are based on ‘Distances + Node Repulsion’; the starting positions are based on ‘Gower scaling’; the number of iterations is 30; the distance between components is 30; the proximities are based on ‘geodesic distances’. The original dataset used for Fig. 4.3 covers all inter-language links among all language versions of Wikipedia. In other words, all the links that appear in the figure suggest that there exists a substantial number of links between the various nodes of language versions, while if no link appears between the nodes in Fig. 4.3, it means that there exists no or some unsubstantial number of links between them. However, we have limited the selected data to those entries with less than three inter-language links. Such a choice, it could be argued, may have a bias toward arbitrary entries. While this may be interpreted as a limitation of the study, the limitation was chosen deliberately to show that, even by choosing only the most arbitrary Wikipedia entries it is possible to gain valuable insights about the relationship among languages/entities of knowledge. Further research will be necessary to confirm the conjectures and clustering effects.
- 15.
Disproportionality in receiving and giving links is emphasised by unidirectional linkages in Fig. 4.3. Since the focus in the figure is on top-incoming and top-outgoing links, no proportional distribution (bidirectional linking) is visualised.
- 16.
As an additional methodological note: for each language node, the shown outgoing links visualise the major linking targets—that is, the links that have more than 7.5 per cent of total outgoing external links. Similar to the discussion on the use of cartograms, several network diagrams could be created at different times so that a process of diffusion may be observed. The use of network graphs for diffusion research has several extra benefits that may not be offered using area cartograms. First, because the network graph may show a core-peripheral structure, suggesting some hierarchical relationships, researchers can observe how the spread of inter-language links reinforces, reconstitutes, or shifts the existing hierarchical relationship. For example, as languages (e.g., Japanese, Chinese, Russian, German) grow in terms of number of entries, will these languages reinforce the current central position of English, or will the central node shift from English to another language, or will these languages reconstitute in order to become central nodes of similar significance? Second, unlike area cartograms, where geographic affinity is already assumed and presented on the map, the diffusion patterns observed from network graphs reflect actual linking affinity, which in the case of inter-language links of the Wikipedia is likely to include geolinguistic kinds of affinity. Third, the use of network graphs can be regarded as an independent cross-check for the cartogram results because the geographic affinity is not assumed in the former while it is in the latter. For example, if some relationship that appears in the network graph cannot be explained by cartogram results, researchers will have to come up with explanations as to why such relationships may exist without clear geolinguistic affinity.
References
Abrams, D. M. and S. H. Strogatz. (2003). Modelling the dynamics of language death. Nature, 424 (21 August), 900.
Albizu, J.A. (2007). Geolinguistic Regions and Diasporas in the Age of Satellite Television. International Communication Gazette, 69, 239–261.
Ambrose, J.E., and Williams, C.H. (1981). On the spatial definition of minority: Scale as an influence on the geolinguistic analysis of Welsh. In: Minority languages today, edited by E. Haugen, J.D. McClure and D.S. Thompson, 53–71. Edinburgh: Edinburgh University Press.
Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51), 21544–21549.
Bao, P., Hecht, B., Carton, S., Quaderi, M., Horn, M., & Gergle, D. (2012). Omnipedia: Bridging the Wikipedia Language Gap. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2012), pp. 1075–1084. New York: ACM Press.
Barabasi, L. (2003). Linked. New York: Penguin.
Biersteker, A. (2014). Links that speak only some languages. Proc Natl Acad Sci USA, 112(15), E1814.
Brandes, U., Kenis, P., Lerner, J., & Raaij, D. V. (2009). Network analysis of collaboration structure in Wikipedia. Proceedings of the 18th International Conference on World Wide Web, ACM, 731–740.
Breton, R.-L. (1991). Geolinguistics: Language dynamics and ethnolinguistic geography. Ottawa: University of Ottawa Press.
Cartwright, D. (2006). Geolinguistic analysis in language policy. In An introduction to language policy, edited by T. Ricento, 194–209. Malden: Wiley-Blackwell.
Choudhury, M.D., Sundaram, H., John, A., Seligmann, D.D., Kelliher, A. (2010). Birds of a feather: Does User Homophily Impact Information Diffusion in Social Media? In: Proceedings of the Computing Research Repository. Available at https://arxiv.org/abs/1006.1702 (retrieved 15 June 2010).
Cohen, L., Frazzini, A., & Malloy, C. (2008). The small world of investing: Board connections and mutual fund returns. Journal of Political Economy, 116(5), 951–979.
Coleman, J., Katz, E., & Menzel, H. (1957). The diffusion of an innovation among physicians. Sociometry, 20(4), 253–270
Collins, R. (1994). “Trading in culture: the role of language.” Canadian Journal of Communication, 19: 377–99.
Collins, R. (2002). Media and identity in contemporary Europe: consequences of global convergence. Portland: Intellect.
Collins, H. (2010). Tacit and explicit knowledge. Chicago: Chicago University Press.
Donovan, P. (2007). How idle is idle talk? One hundred years of rumor research. Diogenes, 54(1), 59–83.
Erickson, B. H., Nosanchuk, T. A., Mostacci, L., & Dalrymple, C. F. (1978). The flow of crisis information as a probe of work relations. Canadian Journal of Sociology, 3, 71–87;
Flache, A. & Macy, M. W. (2011). Small worlds and cultural polarization. The Journal of Mathematical Sociology, 35, 146–176.
Gerrand, P. (2007). Estimating linguistic diversity on the Internet: A taxonomy to avoid pitfalls and paradoxes. Journal of Computer-Mediated Communication, 12 (4), 1298–1320.
Graham, M. (2009). Neogeography and the Palimpsests of Place. Tijdschrift voor Economische en Sociale Geografie, 101 (4), 422–436.
Hartley, J., & Potts, J. (2014). Cultural Science: A Natural History of Stories, Demes, Knowledge and Innovation. London & New York: Bloomsbury Academic.
Hecht, B., and Gergle, D. (2010). The Tower of Babel Meets Web 2.0. Paper presented at CHI2010. Atlanta: April 10–15.
Herring, S.C., Paolillo, J. C., Ramos-Vielba, I., Kouper, I., Wright, E., Stoerger, S., Scheidt, L.A. and B. Clark. (2007). Language networks on LiveJournal. In: Proceedings of the 40th Hawai‘i International Conference on Systems Sciences.
Hidlago, C. (2015). Why Information Grows: The Evolution of Order, from Atoms to Economies. New York: Basic Books.
Holloway, T., Bozicevic, M., & Börner, K. (2007). Analyzing and visualizing the semantic coverage of Wikipedia and its authors: Research Articles. Complex, 12(3), 30–40.
Huvila, I. (2010). Where does the information come from? Information source use patterns in Wikipedia. Information Research, 15 (3). Available at http://www.informationr.net/ir/15-3/paper433.html (retrieved 19 October 2010).
Lazer D., Pentland A, Adamic, L. A., et al. (2009). Computational social science. Science 323(5915), 721–723.
Liao, H. A. T. & Petzold, T. (2010). Analysing geo-linguistic dynamics of the World Wide Web: The use of cartograms and network analysis to understand linguistic development in Wikipedia, Cultural Science Journal, 3(2), 1–18.
Liben-Nowell, D. & Kleinberg J. M. (2008). Tracing information flow on a global scale using Internet chain-letter data. Proceedings of the National Academy of Sciences, 105(12), 4633–4638.
Lee, K., Kim, H., Shin, H., & Kim, H. (2009). FolksoViz: A semantic relation-based folksonomy visualization using the Wikipedia Corpus. Proceedings of the 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, IEEE Computer Society, 24–29.
Jesus, R., Schwartz, M., & Lehmann, S. (2009). Bipartite networks of Wikipedia’s articles and authors: A meso-level approach. Proceedings of the 5th International Symposium on Wikis and Open Collaboration, ACM, 1–10.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.
Mira, J., and Paredes, A. (2005). Interlinguistic similarity and language death dynamics. Europhysics Letters, 69(6), 1031–1034. Available at http://arxiv.org/PS_cache/physics/pdf/0501/0501097v1.pdf (retrieved 24 January 2011).
Yavas, M., & Yücel, G. (2014). Impact of homophily on diffusion dynamics over social networks. Social Science Computer Review, 32(3): 354–372.
Paolillo, J.C. (2007). How much multilingualism on the Internet? Language diversity on the Internet. In: The Multilingual Internet, edited by Danet, B. and S. C. Herring, 408–430. Oxford: Oxford University Press.
Petzold, T., and Liao, H. T. (2011). Geo–linguistic analysis of the World Wide Web: The use of cartograms and network analysis to understand linguistic development in Wikipedia. In: Nexus: New Intersections in Internet Research, edited by Araya, D., T. Houghton and Y. Breindl, 55–75. New York: Peter Lang.
Phelps, J. E., Lewis, R., Mobilio, L., Perry, D., & Raman, N. (2004). Viral marketing or electronic word-of-mouth advertising: examining consumer responses and motivations to pass along email. Journal of Marketing Research, 44(4), 333–348.
Polanyi, M. (1967). The tacit dimension. New York: Anchor Books.
Pool, I. de S. & Kochen, M. (1978). Contacts and influence. Social Networks, 1(1), 5–51.
Raisz, E. (1938). General cartography. New York: McGraw-Hill.
Richardson, R. J., Erickson, B. H., & Nosanchuk, T. A. (1979). Community size, network structure, and the flow of information. Canadian Journal of Sociology, 4, 379–392.
Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press
Rogers, E. M. & Agarwala-Rogers, R. (1976). Communication in organizations. New York: The Free Press.
Rogers, E. M., Medina, U. E., Rivera, M. A., & Wiley, C. J. (2005). Complex adaptive systems and the diffusion of innovations. The Innovation Journal, 10(3), 2–26.
Ronen S., et al. (2014). Links that speak: The global language network and its association with global fame. Proc Natl Acad Sci USA, 111(52), E5616–E5622.
Ryle, G. (1946). Knowing how and knowing that. Proceedings of the Aristotelian Society 46.
Shannon, Claude E. & Warren Weaver. (1949). A Mathematical Model of Communication. Urbana: University of Illinois Press.
Schnettler, S. (2009). A structured overview of 50 years of small-world research. Social Networks, 31, 165–178.
Schnettler, S. (2013). A structured overview of 50 years of small-world research. In: Schnettler, S. (Ed.), Small world research, volume 1, 11–48 (2013). Los Angeles: SAGE.
Sinclair, J., Jacka, E,, and Cunningham, S. (1996). New Patterns in Global Television: Peripheral Vision. Oxford: Oxford University Press.
Watts, D. J., Dodds, P. S., & Newman, M. E. J. (2002). Identity and search in social networks. Science, 296(5571), 1302–1305.
Wulczyn, E., West, R., Leskovec, J., & Zia, L. (2016). Growing Wikipedia across languages via recommendation, ACM International Conference on World Wide Web (WWW).http://www2.compute.dtu.dk/pubdb/views/edoc_download.php/6012/pdf/imm6012.pdf (retrieved 2 July 2015).
Zuckerman, E. (2009). Mapping globalization. Talk at the Berkman Center for Internet and Society, Harvard University. January 27th. Available at http://cyber.law.harvard.edu/events/luncheon/2009/01/zuckerman (retrieved 12 February 2009).
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Petzold, T. (2017). What Knowledge Grows?. In: Global Knowledge Dynamics and Social Technology. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-41234-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-41234-4_4
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-319-41233-7
Online ISBN: 978-3-319-41234-4
eBook Packages: Literature, Cultural and Media StudiesLiterature, Cultural and Media Studies (R0)