Mapping the communication of agricultural knowledge, an application of social network analysis

Main Article Content

Norman Aguilar-Gallegos
Leticia Elizabeth Romero-García

Scientific knowledge communication is a fundamental part of the scientific progress and vital for modern society. Thus, scientific conferences and congresses are crucial spaces for these processes of communication. This research aims to analyse the communication of scientific works at an agricultural congress, so as to add knowledge to the literature in three streams: data use, methodological application, and empirical evidence. To do this, we analysed 316 presentation titles done over five years. We used two methodological approaches: data mining and social network analysis (SNA). The results show that in the 316 titles, 1,093 different terms have been used, following a power-law distribution. Through a bigrams network, it was found that these terms are linked by 1,847 directed ties. Moreover, with SNA, the most important words were identified based on different indicators. It is concluded that the existing information in the files of scientific congresses is rich in content and could be useful for finding knowledge gaps. Furthermore, the used approaches complement each other. This paper provides technical and empirical evidence about the network mining field.

Keywords
Scientific communication, Agricultural knowledge, Text mining, Text analytics, Network mining, Bigrams network

Article Details

How to Cite
Aguilar-Gallegos, Norman; Romero-García, Leticia Elizabeth. “Mapping the communication of agricultural knowledge, an application of social network analysis”. Redes. Revista hispana para el análisis de redes sociales, 2023, vol.VOL 34, no. 2, doi:10.5565/rev/redes.995.
References

#science communication. (2009). Nature Chemical Biology, 5(9), 601–601. doi:10.1038/nchembio0909-601

Adhikari, A., Das, P., & Mukherjee, A. (2019). Generating a representative keyword subset pertaining to an academic conference series. Scientometrics, 119(2), 749–770. doi:10.1007/s11192-019-03068-1

Aguilar-Gallegos, N., Martínez-González, E. G., & Aguilar-Ávila, J. (2017). Análisis de redes sociales: Conceptos clave y cálculo de indicadores. Chapingo, México: Universidad Autónoma Chapingo (UACh), Centro de Investigaciones Económicas, Sociales y Tecnológicas de la Agroindustria y la Agricultura Mundial (CIESTAAM). Serie: Metodologías y herramientas para la investigación, Volumen 5.

Amato, F., Cozzolino, G., Moscato, F., & Xhafa, F. (2019). Semantic analysis of social data streams. In F. Xhafa, L. Barolli, & M. Greguš (Eds.), Advances in Intelligent Networking and Collaborative Systems (pp. 59–70). Cham: Springer International Publishing. doi:10.1007/978-3-319-98557-2_6

Arellano-Rojas, P., Calisto-Breiding, C., & Peña-Pallauta, P. (2022). Evaluación de la investigación científica: mejorando las políticas científicas en Latinoamérica. Revista Española de Documentación Científica, 45(3), e336. doi:10.3989/redc.2022.3.1879

Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and Its Applications, 311(3–4), 590–614. doi:10.1016/S0378-4371(02)00736-7

Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. Retrieved from http://www.jstor.org/stable/2780000

Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: software for social network analysis. Harvard, MA: Analytic Technologies.

Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. London: SAGE Publications Limited.

Burns, T. W., O’Connor, D. J., & Stocklmayer, S. M. (2003). Science communication: A contemporary definition. Public Understanding of Science, 12(2), 183–202. doi:10.1177/09636625030122004

Cruz-Ramírez, M., Díaz-Ferrer, Y., Rúa-Vásquez, J. A., & Rojas-Velázquez, O. J. (2020). Estudio cienciométrico de una red de coautoría en educación matemática. Un análisis de sus campos de investigación basado en el método Delphi. Revista Española de Documentación Científica, 43(4), e281. doi:10.3989/redc.2020.4.1727

Csárdi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.

Freeman, L. C. (1979). Centrality in social networks: conceptual clarification. Social Networks, 1(3), 215–239. doi:10.1016/0378-8733(78)90021-7

Freeman, L. C., Borgatti, S. P., & White, D. R. (1991). Centrality in valued graphs: A measure of betweenness based on network flow. Social Networks, 13(2), 141–154. doi:10.1016/0378-8733(91)90017-N

Hanneman, R. A., & Riddle, M. (2011). Concepts and measures for basic network analysis. In J. Scott & P. J. Carrington (Eds.), The SAGE Handbook of Social Network Analysis (pp. 340–369). London, UK: SAGE Publications Ltd.

Huang, Y., Liu, H., & Pan, J. (2021). Identification of data mining research frontier based on conference papers. International Journal of Crowd Science, 5(2), 143–153. doi:10.1108/IJCS-01-2021-0001

Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart farming and agriculture 4.0: New contributions and a future research agenda. NJAS - Wageningen Journal of Life Sciences, 90–91, 100315. doi:10.1016/j.njas.2019.100315

Klerkx, L., Landini, F., & Santoyo-Cortés, H. (2016). Agricultural extension in Latin America: current dynamics of pluralistic advisory systems in heterogeneous contexts. The Journal of Agricultural Education and Extension, 22(5), 389–397. doi:10.1080/1389224X.2016.1227044

Laborde, D., Martin, W., Swinnen, J., & Vos, R. (2020). COVID-19 risks to global food security. Science, 369(6503), 500–502. doi:10.1126/science.abc4765

Laender, A. H. F., de Lucena, C. J. P., Maldonado, J. C., de Souza e Silva, E., & Ziviani, N. (2008). Assessing the research and education quality of the top Brazilian Computer Science graduate programs. ACM SIGCSE Bulletin, 40(2), 135–145. doi:10.1145/1383602.1383654

Lang, D., & Chien, G.-T. (2018). wordcloud2: Create word cloud by htmlwidget. Retrieved from https://cran.r-project.org/package=wordcloud2

Lopez-Ridaura, S., Sanders, A., Barba-Escoto, L., Wiegel, J., Mayorga-Cortes, M., Gonzalez-Esquivel, C., … García-Barcena, T. S. (2021). Immediate impact of COVID-19 pandemic on farming systems in Central America and Mexico. Agricultural Systems, 192, 103178. doi:10.1016/j.agsy.2021.103178

Lortie, C. J. (2020). Online conferences for better learning. Ecology and Evolution, 10(22), 12442–12449. doi:10.1002/ece3.6923

Martins, W. S., Gonçalves, M. A., Laender, A. H. F., & Pappa, G. L. (2009). Learning to assess the quality of scientific conferences. Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL ’09), 193–202. doi:10.1145/1555400.1555431

Memon, N., Xu, J. J., Hicks, D. L., & Chen, H. (2010). Social network data mining: Research questions, techniques, and applications. In N. Memon, J. J. Xu, D. L. Hicks, & H. Chen (Eds.), Data Mining for Social Network Data. Annals of Information Systems, vol 12. (pp. 1–7). Boston, MA: Springer US. doi:10.1007/978-1-4419-6287-4_1

Nuñez Espinoza, J. F., Tisselli Vélez, E., Palma Tenango, M. de los Á., Ortega Ortega, T., Hernández, A. M., Salinas Martínez, J. A., … Cárdenas-Bejarano, E. (2017). Mapeo reticular del discurso de la sociología y el desarrollo rural en América Latina. Caso de estudio: Asociación Latinoamericana de Sociología Rural (ALASRU). REDES. Revista Hispana Para El Análisis de Redes Sociales, 28(2), 81–96. doi:10.5565/rev/redes.682

Núñez-Ríos, J. E., Aguilar-Gallegos, N., Sánchez-García, J. Y., & Cardoso-Castro, P. P. (2020). Systemic design for food self-sufficiency in urban areas. Sustainability, 12(18), 7558. doi:10.3390/su12187558

Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. doi:10.1016/j.socnet.2010.03.006

Pacheco-Almaraz, V., Palacios-Rangel, M. I., Martínez-González, E. G., Vargas-Canales, J. M., & Ocampo-Ledesma, J. G. (2021). La especialización productiva y agrícola desde su análisis bibliométrico (1915-2019). Revista Española de Documentación Científica, 44(3), e304. doi:10.3989/redc.2021.3.1764

Patterson, D. A. (2004). The health of research conferences and the dearth of big idea papers. Communications of the ACM, 47(12), 23–24. doi:10.1145/1035134.1035153

Pertuz, V., Pérez, A., Vega, A., & Aguilar-Ávila, J. (2020). Análisis de las redes de colaboración entre las Instituciones de Educación Superior en Colombia de acuerdo con ResearchGate. Revista Española de Documentación Científica, 43(2), e265. doi:10.3989/redc.2020.2.1686

Pretty, J., Sutherland, W. J., Ashby, J., Auburn, J., Baulcombe, D., Bell, M., … Pilgrim, S. (2010). The top 100 questions of importance to the future of global agriculture. International Journal of Agricultural Sustainability, 8(4), 219–236. doi:10.3763/ijas.2010.0534

Rodríguez, H., Ramírez-Gómez, C. J., Aguilar-Gallegos, N., & Aguilar-Ávila, J. (2016). Network analysis of knowledge building on rural extension in Colombia. Agronomía Colombiana, 34(3), 393–402. doi:10.15446/agron.colomb.v34n3.58500

Romero Goyeneche, O. Y., Velez Cuartas, G., Ramírez, M., Robledo Velásquez, J., & Balanzó, A. (2018). Colegios invisibles y patrones de colaboración en el Sistema de Investigación Agropecuaria en Colombia. REDES. Revista Hispana Para El Análisis de Redes Sociales, 30(1), 1–24. doi:10.5565/rev/redes.818

Ruiz León, A. A., & Russell Barnard, J. M. (2016). La estructura del sistema científico de México a finales del siglo XX: una visión a nivel de instituciones. REDES. Revista Hispana Para El Análisis de Redes Sociales, 27(2), 11–32. doi:10.5565/rev/redes.626

Russell, J. M., Madera Jaramillo, M. J., & Ainsworth, S. (2009). El análisis de redes en el estudio de la colaboración científica. REDES. Revista Hispana Para El Análisis de Redes Sociales, 17(2), 39–47. doi:10.5565/rev/redes.374

Silberberg, S. D., Crawford, D. C., Finkelstein, R., Koroshetz, W. J., Blank, R. D., Freeze, H. H., … Seger, Y. R. (2017). Shake up conferences. Nature, 548(7666), 153–154. doi:10.1038/548153a

Silge, J., & Robinson, D. (2017). Text mining with R. A tidy approach. O’Reilly Media, Inc.

Silge, J., & Robinson, D. (2016). tidytext: Text mining and analysis using tidy data principles in R. The Journal of Open Source Software, 1(3), 37. doi:10.21105/joss.00037

Sohn, E. (2018). The future of the scientific conference. Nature, 564(7736), S80–S82. doi:10.1038/d41586-018-07779-y

Stephens, E. C., Martin, G., van Wijk, M., Timsina, J., & Snow, V. (2020). Impacts of COVID-19 on agricultural and food systems worldwide and on progress to the sustainable development goals. Agricultural Systems, 183, 102873. doi:10.1016/j.agsy.2020.102873

Su, H. N., & Lee, P. C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in Technology Foresight. Scientometrics, 85(1), 65–79. doi:10.1007/s11192-010-0259-8

Vélez Cuartas, G., Suárez Tamayo, M., Jaramillo Guevara, L., & Gutiérrez, G. (2021). Nuevo modelo de métricas responsables para medir el desempeño de revistas científicas en la construcción de comunidad: el caso de Redes. REDES. Revista Hispana Para El Análisis de Redes Sociales, 32(2), 110–152. doi:10.5565/rev/redes.919

Viglione, G. (2020). A year without conferences? How the coronavirus pandemic could change research. Nature, 579(7799), 327–328. doi:10.1038/d41586-020-00786-y

Wasserman, S., & Faust, K. (1994). Social Network Analysis: methods and applications. Cambridge, UK: Cambridge University Press.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. On Use R! Springer International Publishing. doi:10.1007/978-3-319-24277-4

Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., … Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686

Zhuang, Z., Elmacioglu, E., Lee, D., & Giles, C. L. (2007). Measuring conference quality by mining program committee characteristics. Proceedings of the 2007 Conference on Digital Libraries - JCDL ’07, 225–234. doi:10.1145/1255175.1255220

Most read articles by the same author(s)