Fesabid’2000: las jornadas del milenio y la reflexión

Comments on the VII Jornadas Españolas de Documentación, that were celebrated in Bilbao in October of 2000 under the theme: "Knowledge management: challenges and solutions of the information professionals

Fesabid’2000: las jornadas del milenio y la reflexión

Comments on the VII Jornadas Españolas de Documentación, that were celebrated in Bilbao in October of 2000 under the theme: "Knowledge management: challenges and solutions of the information professionals

Content analysis of the IweTel e-mailing list (2001-2007)

The evolution of the mailing list IweTel is presented, including a description of its evolution and how it has been used by its subscribers, by analysing the content of the messages sent from 2001 to 2007. The study shows that the list is used mainly for professional communication and information exchange and, overall, by the academic community. However, a lack of active participation by its members is also revealed. On the other hand, list management has improved in recent years, and so has the quality of the debates

Selective linking from social platforms to university websites: a case study of the Spanish academic system

Mention indicators have frequently been used in Webometric studies because they provide a powerful tool for determining the degree of visibility and impact of web resources. Among mention indicators, hypertextual links were a central part of many studies until Yahoo! discontinued the ¿linkdomain¿ command in 2011. Selective links constitute a variant of external links where both the source and target of the link can be selected. This paper intends to study the influence of social platforms (measured through the number of selective external links) on academic environments, in order to ascertain both the percentage that they constitute and whether some of them can be used as substitutes of total external links. For this purpose, 141 URLs belonging to 76 Spanish universities were compiled in 2010 (before Yahoo! stopped their link services), and the number of links from 13 selected social platforms to these universities were calculated. Results confirm a good correlation between total external links and links that come from social platforms, with the exception of some applications (such as Digg and Technorati). For those universities with a higher number of total external links, the high correlation is only maintained on Delicious and Wikipedia, which can be utilized as substitutes of total external links in the context analyzed. Notwithstanding, the global percentage of links from social platforms constitute only a small fraction of total links, although a positive trend is detected, especially in services such as Twitter, Youtube, and Facebook.Orduña Malea, E.; Ontalba Ruipérez, JA. (2013). Selective linking from social platforms to university websites: a case study of the Spanish academic system. Scientometrics. 95(2):593-614. doi:10.1007/s11192-012-0851-1S593614952Aguillo, I. F. (2009a). Measuring the institutions’ footprint in the web. Library Hi Tech, 27(4), 540–556.Aguillo, IF. (2009b). Cibermetría: introducción teórico-práctica. Version 1.56 (unpublished teaching material).Alonso Berrocal, J. L., Figuerola, C. G., & Zazo, A. F. (2004). Cibermetria: nuevas tecnicas de estudio aplicables al Web. Gijon: Trea.Bar-Ilan, J. (2003). A microscopic link analysis of academic institutions within a country—the case of Israel. Scientometrics, 59(3), 391–403.Bar-Ilan, J. (2005). What do we know about links and linking? A framework for studying links in academic environments. Information Processing & Management, 41(4), 973–986.Baron, L., Tague-Sutcliffe, J., & Kinnucan, M. T. (1996). Labeled, typed links as cues when reading hypertext documents. Journal of the American Society for Information Science, 47(12), 896–908.Bjorneborn, L., & Ingwersen, P. (2004). Toward a basic framework for webometrics. Journal of the American Society for Information Science and Technology, 55(14), 1216–1227.Cabezas-Clavijo, A., & Torres-Salinas, D. (2012). Google scholar citations y la emergencia de nuevos actores en la evaluación de la investigación. Anuario Thinkepi, 6, 147–153.Cronin, B., Snyder, H., Rosenbaum, H., Martinson, A., & Callahan, E. (1998). Invoked on the Web. Journal of the American Society for Information Science, 49(14), 1319–1328.Dhyani, D., Keong Ng, W., &, SS Bhowmick (2002) A survey of Web Metrics. ACM Computing Surveys, 34(4), 469–503.Eysenbach, G. (2012). Can Tweets predict citations? Metrics of social impact based on Twitter and correlation with traditional metrics of scientific impact. Journal of Medical Internet Research, 13(4).Faba-Perez, C., Bote, Guerrero, Vicente, P., & Moya-Anegon, F. (2004). Fundamentos y técnicas cibermétricas. Mérida: Junta de Extremadura.Garcia-Santiago, M. D. (2001). Topología de la información en la World Wide Web: modelo experimental y bibliométrico en una red hipertextual nacional. Granada: Universidad, Departamento de Biblioteconomía y Documentación.Gibson, D., Kleinberg, J. & Raghavan, P. (1998a). Inferring web communities from link topology. In: Proceedings of the 9th ACM conference on Hypertext and hypermedia (pp. 225–234). http://www.cs.cornell.edu/home/kleinber/ht98.pdf . Accessed 4 June 2012.Gibson, D., Kleinberg, J. & Raghavan, P. (1998b). Structural analysis of the World Wide Web. In: World Wide Web Consortium Workshop on Web Characterization. Position paper. http://www.w3.org/1998/11/05/wc-workshop/Papers/kleinber1.html . Accessed 4 June 2012.Haas, S. W., & Grams, E. S. (1998). A link taxonomy of Web pages. Proceedings of the 61st ASIS annual meeting (pp. 485–495).Heylighen, F. (2000). Web connectivity analysis. http://pespmc1.vub.ac.be/WEBCONAN.html . Accessed 4 June 2012.Katz, J.S. (2004). Co-link web indicators of the European Research Area: web indicators for scientific, technological and innovation research (Technical report).Kim, H. J. (2000). Motivations for hyperlinking in scholarly electronic articles: a qualitative study. Journal of the American Society for Information Science, 51(10), 887–899.Larson, R. (1996). Bibliometrics of the world wide web: An exploratory analysis of the intellectual structure of cyberspace. Proceedings of AISS 59th annual meeting (pp. 71–78). http://sherlock.berkeley.edu/asis96/asis96.html . Accessed 4 June 2012.Li, X., Thelwall, M., & Giustini, D. (2011). Validating online reference managers for scholarly impact measurement. Scientometrics, 91(2), 461–471.Nielsen, F. Å. (2007). Scientific citations in Wikipedia. First Monday, 12(8). http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1997/1872 . Accessed 4 June 2012.Orduña-Malea, E., Serrano-Cobos, J., Ontalba-Ruipérez, J.-A., & Lloret-Romero, N. (2010). Presencia y visibilidad web de las universidades públicas españolas. Revista española de documentación científica, 33(2), 246–278.Priem, J., & Costello, K. L. (2010). How and why scholars cite on Twitter. Proceedings of the American Society for Information Science and Technology, 47, 1–4.Priem, J. & Hemminger, B. M. (2010). Scientometrics 2.0: Toward new metrics of scholarly impact on the social Web. First Monday, 15(7). http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2874/2570 . Accessed 4 June 2012.Rocki, M. (2005). Statistical and mathematical aspects of rankings: lessons from Poland. Higher education in Europe, 30(2), 173–181.Seeber, M., Lepori, B., Lomi, A., Aguillo, I., & Barberio, V. (2012). Factors affecting web links between European higher education institutions. Journal of informetrics, 6, 435–447.Smith, A. G. (1999). A tale of two web spaces: comparing sites using web impact factors. Journal of documentation, 55(5), 577–592.Thelwall, M. (2001). Extracting macroscopic information from web links. Journal of the American Society for Information Science and Technology, 52(13), 1157–1168.Thelwall, M. (2009). Introduction to Webometrics: quantitative web research for the social sciences. San Rafael, CA: Morgan & Claypool (Synthesis Lectures on Information Concepts, Retrieval, and Services, v. 1, n. 1).Thelwall, M. (2010). Webometrics: emergent or doomed? Information research, 15(4). http://InformationR.net/ir/15-4/colis713.html . Accessed 4 June 2012.Wilkinson, D., Harries, G., Thelwall, M., & Price, E. (2003). Motivations for academic Web site interlinking: evidence for the Web as a novel source of information on informal scholarly communication. Journal of information science, 29(1), 59–66

Las revistas digitales académicas españolas de Documentación: análisis de las existentes y propuesta de modelo

In this text, Spanish academic e-journals are analyzed, and proposal of definition and a model of e-journal are presented

Fesabid'2003: las jornadas de referencia

Commentaries about the 8th Jornadas Españolas de Documentación that took place in Barcelona on February 2003, under the theme of "Organisational information systems: effectiveness and transparency"

Identifying institutional relationships in a geographically distributed public health system using interlinking and co-authorship methods

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s11192-016-1839-zLink analysis is highly effective in detecting relationships between different institutions, relationships that are stronger the greater their geographical proximity. We therefore decided to apply an interlinking analysis to a set of geographically dispersed research entities and to compare the results with the co-authorship patterns between these institutions in order to determine how, and if, these two techniques might reveal complementary insights. We set out to study the specific sector of public health in Spain, a country with a high degree of regional autonomy. We recorded all Spanish health entities (and their corresponding URLs) that belong to, and were hyperlinked from, the national government or any of the regional governments, gathering a total of 263 URLs. After considering their suitability for web metric analysis, interlinking scores between all valid URLs were obtained. In addition, the number of co-authored articles by each pair of institutions and the total scientific output per institution were retrieved from Scopus. Both interlinking and co-authorship methods detect the existence of strength subnets of geographically distributed nodes (especially the Catalan entities) as well as their high connectivity with the main national network nodes (subnet of nodes distributed according to dependence on national government, in this case Spain). However, the resulting interlinking pattern shows a low but significant correlation (r = 0.5) with scientific co-authorship patterns. The existence of institutions that are strongly interlinked but with limited scientific collaboration (and vice versa) reveals that links within this network are not accurately reflecting existing scientific collaborations, due to inconsistent web content development.Ontalba Ruipérez, JA.; Orduña Malea, E.; Alonso-Arroyo, A. (2016). Identifying institutional relationships in a geographically distributed public health system using interlinking and co-authorship methods. Scientometrics. 106(3):1167-1191. doi:10.1007/s11192-016-1839-zS116711911063Aguillo, I. F., Granadino, B., Ortega, J. L., & Prieto, J. A. (2006). Scientific research activity and communication measured with cybermetrics indicators. Journal of the American Society for Information Science and Technology, 57(10), 1296–1302.Almind, T. C., & Ingwersen, P. (1998). Informetric analyses on the world wide web: methodological approaches to ‘webometrics’. Journal of Documentation, 53(4), 404–426.Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.Bar-Ilan, J. (2005). What do we know about links and linking? A framework for studying links in academic environments. Information Processing and Management, 41(4), 973–986.Barnett, George A., & Park, Han W. (2014). Examining the international internet using multiple measures: New methods for measuring the communication base of globalized cyberspace. Quality and Quantity, 48(1), 563–575.Eurostat. (2011). Regions in the European Union. Nomenclature of territorial units for statistics. NUTS 2010/EU-27. http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-RA-11-011/EN/KS-RA-11-011-EN.PDF Accessed 16 August 2015.García-Lacalle, J., Pina, V., & Royo, S. (2011). The unpromising quality and evolution of Spanish public hospital web sites. Online Information Review, 35(1), 86–112.García-Santiago, L., & Moya-Anegón, F. (2009). Using co-outlinks to mine heterogeneous networks. Scientometrics, 79(3), 681–702.González-Bailón, S. (2009). Opening the black box of link formation: Social factors underlying the structure of the web. Social Networks, 31(2009), 271–280.Heimeriks, G., Hörlesberger, M., & Van den Besselaar, P. (2003). Mapping communication and collaboration in heterogeneous research networks. Scientometrics, 58(2), 391–413.Heimeriks, G., & Van den Besselaar, P. (2006). Analyzing hyperlinks networks: The meaning of hyperlink based indicators of knowledge production. Cybermetrics, 10(1), http://cybermetrics.cindoc.csic.es/articles/v10i1p1.pdf . Accessed 16 August 2015.Holmberg, K. (2010). Co-inlinking to a municipal Web space: A webometric and content analysis. Scientometrics, 83(3), 851–862.Holmberg, K., & Thelwall, M. (2009). Local government web sites in Finland: A geographic and webometric analysis. Scientometrics, 79(1), 157–169.Khan, G. F., & Park, H. W. (2011). Measuring the triple helix on the web: Longitudinal trends in the university-industry-government relationship in Korea. Journal of the American Society for Information Science and Technology, 62(12), 2443–2455.Lang, P. B., Gouveia, F. C., & Leta, J. (2014). Health research networks on the web: An analysis of the Brazilian presence. Cadernos de Saúde Pública, 30(2), 369–378.Leydesdorff, L., & Curran, M. (2000). Mapping university-industry-government relations on the Internet: The construction of indicators for a knowledge-based economy. Cybermetrics, 4(1). http://www.cybermetrics.info/articles/v4i1p2.pdf . Accessed 16 August 2015.Méndez-Vásquez, R. I., Suñen-Pinyol, E., Cervelló, R., & Camí, J. (2008). Mapa bibliométrico de España 1996–2004: Biomedicina y ciencias de la salud. Medicina clínica, 130(7), 246–253.Méndez-Vásquez, R. I., Suñén-Pinyol, E., & Rovira, L. (2012). Caracterización bibliométrica de la investigación biomédica española, WOS 1997–2011. http://bac.fundaciorecerca.cat/mb11 . Accessed 16 August 2015.Ministerio de Sanidad, Servicios Sociales e Igualdad. (2012). Sistema Nacional de Salud. España 2012. http://www.msssi.gob.es/organizacion/sns/docs/sns2012/SNS012__Espanol.pdf . Accessed 16 August 2015.Orduna-Malea, E., Ortega, J. L., & Aguillo, I. F. (2014). Influence of language and file type on the web visibility of top European universities. Aslib Proceedings, 66(1), 96–116.Orduna-Malea, E., & Aguillo, I. F. (2014). Cibermetría. Midiendo el espacio red. Barcelona: UOC Publishing.Orduna-Malea, E., & Aytac, S. (2015). Revealing the online network between university and industry: The case of Turkey. Scientometrics, 105(3), 1849–1866.Orduna-Malea, E., Delgado López-Cózar, E., Serrano-Cobos, J., & Romero, N. L. (2015a). Disclosing the network structure of private companies on the web: The case of Spanish IBEX 35 share index. Online Information Review, 39(3), 360–382.Orduna-Malea, E., & Ontalba-Ruipérez, J. A. (2013). Proposal for a multilevel university cybermetric analysis model. Scientometrics, 95(3), 863–884.Orduna-Malea, E., Torres-Salinas, D., & Delgado López-Cózar, E. (2015b). Hyperlinks embedded in twitter as a proxy for total external in-links to international university websites. Journal of the Association for Information Science and Technology, 66(7), 1447–1462.Ortega, J. L. (2007). Visualización de la Web universitaria Europea: análisis cuantitativo de enlaces a través de técnicas cibermétricas. Madrid: Universidad Carlos III de Madrid.Ortega, J. L., & Aguillo, I. F. (2009). Mapping world-class universities on the web. Information Processing and Management, 45(2), 272–279.Ortega, J. L., Orduna-Malea, E., & Aguillo, I. F. (2014). Are web mentions accurate substitutes for inlinks for Spanish universities? Online Information Review, 38(1), 59–77.Park, H. W. (2011). How do social scientists use link data from search engines to understand Internet-based political and electoral communication? Quality and Quantity, 46(2), 679–693.Park, H. W., & Thelwall, M. (2003). Hyperlink analyses of the World Wide Web: A review. Journal of Computer-Mediated Communication. doi: 10.1111/j.1083-6101.2003.tb00223.x .Romero-Frías, E., & Vaughan, L. (2010a). Patterns of web linking to heterogeneous groups of companies: The case of stock exchange indexes. Aslib Proceedings, 62(2), 144–164.Romero-Frías, E., & Vaughan, L. (2010b). European political trends viewed through patterns of Web linking. Journal of the American Society for Information Science and Technology, 61(10), 2109–2121.Seeber, M., Lepori, B., Lomi, A., Aguillo, I. F., & Barberio, V. (2012). Factors affecting web links between European higher education institutions. Journal of Informetrics, 6(3), 435–447.Stuart, D., & Thelwall, M. (2006). Investigating triple helix relationships using URL citations: A case study of the UK West Midlands automobile industry. Research Evaluation, 15(2), 97–106.Sud, P., & Thelwall, M. (2014). Linked title mentions: A new automated link search candidate. Scientometrics, 101(3), 1831–1849.Thelwall, M. (2001). Extracting macroscopic information from web links. Journal of the American Society for Information Science and Technology, 52(13), 1157–1168.Thelwall, M. (2002). Evidence for the existence of geographic trends in university web site interlinking. Journal of Documentation, 58(5), 563–574.Thelwall, M. (2004). Link analysis: An information science approach. San Diego: Elsevier.Thelwall, M. (2006). Interpreting social science link analysis research: A theoretical framework. Journal of the American Society for Information Science and Technology, 57(1), 60–68.Thelwall, M. (2009). Introduction to webometrics: Quantitative web research for the social sciences. San Rafael, CA: Morgan & Claypool Publishers.Thelwall, M., & Sud, P. (2011). A comparison of methods for collecting web citation data for academic organisations. Journal of the American Society for Information Science and Technology, 62(8), 1488–1497.Thelwall, M., & Tang, R. (2003). Disciplinary and linguistic considerations for academic web linking: An exploratory hyperlink mediated study with Mainland China and Taiwan. Scientometrics, 58(1), 155–181.Thelwall, M., Tang, R., & Price, L. (2003). Linguistic patterns of Academic web use in Western Europe. Scientometrics, 56(3), 417–432.Vaughan, L. (2006). Visualizing linguistic and cultural differences using web co-link data. Journal of the American Society for Information Science and Technology, 57(9), 1178–1193.Vaughan, L., & Thelwall, M. (2003). Scholarly use of the web: What are the key inducers of links to journal web sites? Journal of the American Society for Information Science and Technology, 54(1), 29–38.Vaughan, L., & Thelwall, M. (2004). Search engine coverage bias: Evidence and possible causes. Information Processing and Management, 40(4), 693–707.Vaughan, L., & Wu, G. (2004). Links to commercial websites as a source of business information. Scientometrics, 60(3), 487–496.Vaughan, L., & You, J. (2006). Comparing business competition positions based on Web co-link data: The global market vs. the Chinese market. Scientometrics, 68(3), 611–628.Weber, M. S., & Monge, P. (2011). The flow of digital news in a network of sources, authorities, and hubs. Journal of Communication, 61(6), 1062–1081.Wilkinson, D., Harries, G., Thelwall, M., & Price, L. (2003). Motivations for academic Web site interlinking: Evidence for the Web as a novel source of information on informal scholarly communication. Journal of information science, 29(1), 49–56.Wilkinson, D., & Thelwall, M. (2013). Search markets and search results: The case of Bing. Library and Information Science Research, 35(4), 318–325

Presence of the Spanish digital press on the social Web: analysis of Menéame

The objective of our study is to measure the visibility of the Spanish press in Menéame, the most important social content manager in Spanish, during 2007 and the first quarter of 2008. To this end, the number of news items published in Menéame is counted and averaged with votes and comments received, focusing on the evolution of the five media with a greater presence; finally, the obtained ranking is contrasted with that of OJD Interactiva. The most represented newspaper is El País, followed at a certain distance by El Mundo and 20minutos and, farther behind, La Vanguardia and El Periódico de Catalunya

Aproximación a la moral en la Tarraconense (S. XIII-XV)

In this paper are analyzed the behaviors and the moral of the society (clergymen and secular) Catalan Late Medieval, through the Tarraconense council's canons, the pastoral visits and the penitentials.El objetivo principal del presente artículo es analizar los comportamientos y la moral de la sociedad (cleros y seglares) catalana bajomedieval; lo cual se ejemplifica en las fuentes de época, esto es, los cánones conciliares de la Tarraconense, las visitas pastorales o los penitenciales

Hit count estimate variability for website-specific queries in search engines: The case for rare disease association websites

"This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here https://doi.org/10.1108/AJIM-10-2017-0226. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited"[EN] Purpose - The purpose of this paper is to determine the effect of the chosen search engine results page (SERP) on the website-specific hit count estimation indicator. Design/methodology/approach - A sample of 100 Spanish rare disease association websites is analysed, obtaining the website-specific hit count estimation for the first and last SERPs in two search engines (Google and Bing) at two different periods in time (2016 and 2017). Findings - It has been empirically demonstrated that there are differences between the number of hits returned on the first and last SERP in both Google and Bing. These differences are significant when they exceed a threshold value on the first SERP. Research limitations/implications - Future studies considering other samples, more SERPs and generating different queries other than website page count (ositeW) would be desirable to draw more general conclusions on the nature of quantitative data provided by general search engines. Practical implications - Selecting a wrong SERP to calculate some metrics (in this case, website-specific hit count estimation) might provide misleading results, comparisons and performance rankings. The empirical data suggest that the first SERP captures the differences between websites better because it has a greater discriminating power and is more appropriate for webometric longitudinal studies. Social implications - The findings allow improving future quantitative webometric analyses based on website-specific hit count estimation metrics in general search engines. Originality/value - The website-specific hit count estimation variability between SERPs has been empirically analysed, considering two different search engines (Google and Bing), a set of 100 websites focussed on a similar market (Spanish rare diseases associations), and two annual samples, making this study the most exhaustive on this issue to date.Font-Julian, CI.; Ontalba Ruipérez, JA.; Orduña Malea, E. (2018). Hit count estimate variability for website-specific queries in search engines: The case for rare disease association websites. Aslib Journal of Information Management. 70(2):192-213. https://doi.org/10.1108/AJIM-10-2017-0226S192213702Bar-Ilan, J. (2001). Scientometrics, 50(1), 7-32. doi:10.1023/a:1005682102768Bowler, L., Hong, W., & He, D. (2011). The visibility of health web portals for teens: a hyperlink analysis. Online Information Review, 35(3), 443-470. doi:10.1108/14684521111151469European Organization for Rare Diseases (2012), “What is a rare disease?”, available at: www.eurordis.org/content/what-rare-disease (accessed 10 January 2018).Forman, J., Taruscio, D., Llera, V. A., Barrera, L. A., Coté, T. R., … Edfjäll, C. (2012). The need for worldwide policy and action plans for rare diseases. Acta Paediatrica, 101(8), 805-807. doi:10.1111/j.1651-2227.2012.02705.xGao, Y., & Vaughan, L. (2005). Web hyperlink profiles of news sites. Aslib Proceedings, 57(5), 398-411. doi:10.1108/00012530510621851Gouveia, F. C., & Kurtenbach, E. (2009). Mapping the web relations of science centres and museums from Latin America. Scientometrics, 79(3), 491-505. doi:10.1007/s11192-007-1949-8Groselj, D. (2014). A webometric analysis of online health information: sponsorship, platform type and link structures. Online Information Review, 38(2), 209-231. doi:10.1108/oir-01-2013-0011Lewandowski, D. (2008). A three-year study on the freshness of web search engine databases. Journal of Information Science, 34(6), 817-831. doi:10.1177/0165551508089396Li, X. (2003). A review of the development and application of the Web impact factor. Online Information Review, 27(6), 407-417. doi:10.1108/14684520310510046Noruzi, A. (2006). The web impact factor: a critical review. The Electronic Library, 24(4), 490-500. doi:10.1108/02640470610689188Orduna-Malea, E. (2014), “Caracterización y rendimiento del sistema museístico de la comunidad valenciana a través de un análisis cibermétrico”, in Gimenez-Chornet, V. (Ed.), Gestión Cultural: Innovación y Tendencias, Tirant Lo Blanch, Valencia, pp. 13-43.Orduña-Malea, E., Delgado López-Cózar, E., Serrano-Cobos, J., & Romero, N. L. (2015). Disclosing the network structure of private companies on the web. Online Information Review, 39(3), 360-382. doi:10.1108/oir-11-2014-0282Park, H. W., Kim, C.-S., & Barnett, G. A. (2004). Socio-Communicational Structure among Political Actors on the Web in South Korea. New Media & Society, 6(3), 403-423. doi:10.1177/1461444804042522Rodríguez i Gairín, J. M. (1997). Valoración del impacto de la información en Internet: Altavista, el «Citation Index» de la red. Revista española de Documentación Científica, 20(2), 175-181. doi:10.3989/redc.1997.v20.i2.591Romero-Frías, E., & Vaughan, L. (2010). European political trends viewed through patterns of Web linking. Journal of the American Society for Information Science and Technology, 61(10), 2109-2121. doi:10.1002/asi.21375Satoh, K. and Yamana, H. (2012), “Hit count reliability: how much can we trust hit counts?”, in Sheng, Q.Z., Wang, G., Jensen, C.S. and Xu, G. (Eds), Asia-Pacific Web Conference, Springer, Berlin Heidelberg, April, pp. 751-758.Snyder, H., & Rosenbaum, H. (1999). Can search engines be used as tools for web‐link analysis? A critical view. Journal of Documentation, 55(4), 375-384. doi:10.1108/eum0000000007151Uyar, A. (2009). Investigation of the accuracy of search engine hit counts. Journal of Information Science, 35(4), 469-480. doi:10.1177/0165551509103598Vaughan, L., & Thelwall, M. (2004). Search engine coverage bias: evidence and possible causes. Information Processing & Management, 40(4), 693-707. doi:10.1016/s0306-4573(03)00063-3Vaughan, L., & Wu, G. (2004). Links to commercial websites as a source of business information. Scientometrics, 60(3), 487-496. doi:10.1023/b:scie.0000034389.14825.bcWilkinson, D., & Thelwall, M. (2013). Search markets and search results: The case of Bing. Library & Information Science Research, 35(4), 318-325. doi:10.1016/j.lisr.2013.04.00