The impact of science communication on Twitter: The case of Neil deGrasse Tyson

Denia, Elena The impact of science communication on Twitter: The case of Neil deGrasse Tyson. Comunicar, 2020, vol. 28, n. 65, pp. 21-30. [Journal article (Paginated)]

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English abstract

Public perceptions of science have been studied extensively since the mid-twentieth century. The aim of this project is to explore the interaction between science and the public in the digital world as a complement to traditional studies on the societal impact of science, particularly on the social network Twitter. It thus proposes a low-cost, easily reproducible methodology involving the design of an algorithm operating on representative sets of tweets to analyse their content by using computational techniques of data mining and natural language processing. To test this methodology, I analyse the communications of the popular science communicator Neil DeGrasse Tyson. The impact of the information is calculated in terms of 1) likes and retweets; 2) suggested formulas for measuring the popularity and controversial nature of the content; and 3) the semantic network. Relevant elements of the communications are then identified and classified according to the categories of “science”, “culture”, “political-social”, “beliefs”, “media” and “emotional”. The results reveal that content with an emotional charge in the communicator’s message triggers a substantially more profound response from the public, as do references to socio-political issues. Moreover, numerous concepts peripheral to the scientific discussion arouse more interest than the concepts central to the communication. Both these results suggest that science is more interesting when it is linked to other issues.

Spanish abstract

La percepción social de la ciencia se ha estudiado ampliamente desde mediados del siglo XX. El presente proyecto pretende abordar la interacción ciencia-público en el marco de la vida digital para complementar los estudios clásicos sobre impacto social de la ciencia, en particular en la red social Twitter. Se presenta así una propuesta metodológica con el diseño de un algoritmo que opera sobre conjuntos representativos de tweets para analizar su contenido utilizando técnicas computacionales de minería de datos y procesamiento del lenguaje natural, fácilmente reproducible por otros investigadores y de bajo coste. Para probar la herramienta, se analiza el discurso del popular divulgador Neil DeGrasse Tyson. El impacto de la información se calcula en términos de: 1) likes y retuit; 2) medidas sugeridas para la popularidad y el grado de contenido polémico; y 3) la red semántica. Tras identificar y clasificar los elementos relevantes del discurso por las categorías «ciencia», «cultura», «político-social», «creencias», «medios» y «emocional», los resultados revelan que una transmisión con carga emocional en el mensaje del divulgador despierta una respuesta sustancialmente más profunda en el público, así como la alusión a cuestiones socio-políticas. Además, numerosos conceptos periféricos a la discusión científica suscitan mayor interés que los propios centrales en el discurso. Ambos resultados sugieren que la ciencia interesa en mayor medida cuando va ligada a otros aspectos.

Item type: Journal article (Paginated)
Keywords: Twitter; communication; science; dissemination; impact; public; participation; computational analysis; Twitter; comunicación; ciencia; divulgación; impacto; público; participación; análisis computacional
Subjects: B. Information use and sociology of information > BJ. Communication
G. Industry, profession and education.
G. Industry, profession and education. > GH. Education.
Depositing user: Alex Ruiz
Date deposited: 09 Jan 2021 06:42
Last modified: 09 Jan 2021 06:42


Álvarez-Bornstein, B., & Montesi, M. (2019). Who is interacting with researchers on Twitter? A survey in the field of Information Science. JLIS, 10(2), 87-106.

Arrabal, G., & De-Aguilera, M. (2016). Comunicar en 140 caracteres. Cómo usan Twitter los comunicadores en España. [Communicating in 140 characters. How journalists in Spain use Twitter]. Comunicar, 46, 9-17.

Bauer, M.W., Allum, N., & Miller, S. (2007). What can we learn from 25 years of PUS survey research? Liberating and expanding the agenda. Public Understanding of Science, 16(1), 79-95.

Bauer, M.W., Shukla, R., & Allum, N. (2012). Towards cultural indicators of science with global validity. In M.W. Bauer, R. Shukla, & N. Allum (Eds.), The culture of science: How the public relates to science across the globe (pp. 1-17). Routledge.

Becker, B.F.H., Larson, H.J., Bonhoeffer, J., Van-Mulligen, E.M., Kors, J.A., & Sturkenboom, M. (2016). Evaluation of a multinational, multilingual vaccine debate on Twitter. Vaccine, 34(50), 6166-6171.

Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.

Brossard, D., & Scheufele, D.A. (2013). Science, new media, and the public. Science, 339(6115), 40-41.

Büchi, M. (2016). Microblogging as an extension of science reporting. Public Understanding of Science, 26(8), 953-968.

Dann, S. (2010). Twitter content classification. First Monday, 15(12).

Davis, R.C. (1958). The public impact of science in the mass media. Institute for Social Research, University of Michigan.

Dehkharghani, R., Mercan, H., Javeed, A., & Saygin, Y. (2014). Sentimental causal rule discovery from Twitter. Expert Systems with Applications, 41(10), 4950-4958.

European Commission (Ed.) (2008). Public engagement in science. Publications Office of the European Union.

Kahle, K., Sharon, A.J., & Baram-Tsabari, A. (2016). Footprints of fascination: Digital traces of public engagement with particle physics on CERN's social media platforms. PLoS One, 11(5).

Kaiser, D., Durant, J., Levenson, T., Wiehe, B., & Linett, P. (2014). The evolving culture of science engagement: an exploratory initiative. MIT & Culture Kettle.

Kapoor, K.K., Tamilmani, K., Rana, N.P., Patil, P., Dwivedi, Y.K., & Nerur, S. (2018). Advances in social media research: Past, present and future. Information Systems Frontiers, 20(3), 531-558.

Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? In M. Rappa, & P. Jones (Eds.), Proceedings of the 19th International Conference on World Wide Web (pp. 591-600). ACM.

Li, R., Crowe, J., Leifer, D., Zou, L., & Schoof, J. (2019). Beyond big data: Social media challenges and opportunities for understanding social perception of energy. Energy Research & Social Science, 56.

López-Pérez, L., & Olvera-Lobo, M.D. (2019). Participación digital del público en la ciencia de excelencia española: Análisis de los proyectos financiados por el European Research Council. El Profesional de la Información, 28(1), 1-10.

Matthes, J., & Kohring, M. (2008). The content analysis of media frames: Toward improving reliability and validity. Journal of Communication, 58(2), 258-279.

Mohammadi, E., Thelwall, M., Kwasny, M., & Holmes, K.L. (2018). Academic information on Twitter: A user survey. PLoS One, 13(5).

Moreno-Castro, C., Corell-Doménech, M., & Camano-Puig, R. (2019). Which has more influence on perception of pseudo-therapies: The media’s information, friends or acquaintances opinion, or educational background? Communication & Society, 32, 35-49.

Murphy, J., Hill, C., & Dean, E. (2013). Social media, sociality, and survey research. In C. Hill, J. Murphy and E. Dean (Eds.), Social media, sociality, and survey research (pp. 1-33). John Wiley & Sons.

Murphy, J., Link, M.W., Childs, J.H., Tesfaye, C.L., Dean, E., Stern, M., Pasek, J., Cohen, J., Callegaro, M., & Harwood, P. (2014). Social media in public opinion research: Executive summary of the AAPOR task force on emerging technologies in public opinion research. Public Opinion Quarterly, 78(4), 788-794.

Myers, S. A., Sharma, A., Gupta, P., & Lin, J. (2014). Information network or social network? the structure of the twitter follow graph. In Proceedings of the 23rd International Conference on World Wide Web (pp. 493-498). ACM.

Naaman, M., Boase, J., & Lai, C.H. (2010). Is it really about me? message content in social awareness streams. In Proceedings of the 2010 ACM conference on Computer supported cooperative work (pp. 189-192). ACM.

Narr, S., Luca, E.W.D., & Albayrak, S. (2011). Extracting semantic annotations from twitter. In Proceedings of the fourth workshop on Exploiting semantic annotations in information retrieval (pp. 15-16). ACM.

Nisbet, M.C., & Scheufele, D.A. (2009). What's next for science communication? Promising directions and lingering distractions. American Journal of Botany, 96(10), 1767-1778.

Pardo, R. (2001). La cultura científico-tecnológica de las sociedades de la modernidad tardía. Treballs de la Societat Catalana de Biologia, 51, 35-63.

Pearce, W., Holmberg, K., Hellsten, I., & Nerlich, B. (2014). Climate Change on Twitter: Topics, communities and conversations about the 2013 IPCC working group 1 report. PLoS One, 9(4).

Pérez-Rodríguez, A.V., González-Pedraz, C., & Alonso-Berrocal, J.L. (2018). Twitter como herramienta de comunicación científica en España. Principales agentes y redes de comunicación. Communication Papers, 7(13), 95-112.

Santoveña, S., & Bernal, C. (2019). Explorando la influencia del docente: Participación social en Twitter y percepción Académica. [Exploring the influence of the teacher: Social participation on Twitter and academic perception]. Comunicar, 58, 75-84.

ScienceFlows (Ed.) (2019). ScienceFlows.

Shan, L., Regan, Á., De-Brún, A., Barnett, J., Van-der-Sanden, M.C.A., Wall, P., & McConnon, Á. (2014). Food crisis coverage by social and traditional media: A case study of the 2008 Irish dioxin crisis. Public Understanding of Science, 23(8), 911-928.

Silge, J., & Robinson, D. (2016). Tidytext: Text mining and analysis using tidy data principles in R. Journal of Open Source Software, 1(3), 37.

Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media: Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29(4), 217-248.

Twitter (Ed.) (2019). Application programming interface.

Uren, V., & Dadzie, A.S. (2015). Public science communication on Twitter: A visual analytic approach. Aslib Journal of Information Management, 67(3), 337-355.

Veltri, G. (2013). Microblogging and nanotweets: Nanotechnology on Twitter. Public Understanding of Science, 22(7), 832-849.

Veltri, G., & Atanasova, D. (2015). Climate change on Twitter: Content, media ecology and information sharing behaviour. Public Understanding of Science, 26(6), 721-737.

Wilkinson, D., & Thelwall, M. (2012). Trending Twitter topics in English: An international comparison. Journal of the American Society for Information Science and Technology, 63(8), 1631-1646.

Zhao, W.X. Jiang, J., Weng, J., He, J., Lim E.P., Yan, H., & Li, X. (2011). Comparing Twitter and traditional media using topic models. In P. Clough et al. (Eds.), Lecture Notes in Computer Science: Vol 6611. Advances in Information Retrieval (pp. 338-349). Springer.


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