Volume index - Journal index - Article index - Map ---- Back

Comunicar Journal 65: Emerging mixed methods in social research: The digital challenge (Vol. 28 - 2020)

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

https://doi.org/10.3916/C65-2020-02

Elena Denia

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.

Keywords

Twitter, communication, science, dissemination, impact, public, participation, computational analysis

PDF file in Spanish

PDF file in English

References

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. https://doi.org/10.4403/jlis.it-12530

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. https://doi.org/10.3916/C46-2016-01

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. https://doi.org/10.1177/0963662506071287

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. https://doi.org/10.4324/9780203813621

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. https://doi.org/10.1016/j.vaccine.2016.11.007

Blei, D.M., Ng, A.Y., & Jordan, M.I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022. https://bit.ly/2wQLaGj

Brossard, D., & Scheufele, D.A. (2013). Science, new media, and the public. Science, 339(6115), 40-41. https://doi.org/10.1126/science.1232329

Büchi, M. (2016). Microblogging as an extension of science reporting. Public Understanding of Science, 26(8), 953-968. https://doi.org/10.1177/0963662516657794

Dann, S. (2010). Twitter content classification. First Monday, 15(12). https://doi.org/10.5210/fm.v15i12.2745

Davis, R.C. (1958). The public impact of science in the mass media. Institute for Social Research, University of Michigan. https://stanford.io/2w9teGk

Dehkharghani, R., Mercan, H., Javeed, A., & Saygin, Y. (2014). Sentimental causal rule discovery from Twitter. Expert Systems with Applications, 41(10), 4950-4958. https://doi.org/10.1016/j.eswa.2014.02.024

European Commission (Ed.) (2008). Public engagement in science. Publications Office of the European Union. https://bit.ly/2uB98Vg

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). https://doi.org/10.1371/journal.pone.0156409

Kaiser, D., Durant, J., Levenson, T., Wiehe, B., & Linett, P. (2014). The evolving culture of science engagement: an exploratory initiative. MIT & Culture Kettle. https://bit.ly/2Wjy5hG

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. https://doi.org/10.1007/s10796-017-9810-y

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. https://doi.org/10.1145/1772690.1772751

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. https://doi.org/10.1016/j.erss.2019.101217

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. https://doi.org/10.3145/epi.2019.ene.06

Matthes, J., & Kohring, M. (2008). The content analysis of media frames: Toward improving reliability and validity. Journal of Communication, 58(2), 258-279. https://doi.org/10.1111/j.1460-2466.2008.00384.x

Mohammadi, E., Thelwall, M., Kwasny, M., & Holmes, K.L. (2018). Academic information on Twitter: A user survey. PLoS One, 13(5). https://doi.org/10.1371/journal.pone.0197265

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. https://doi.org/10.15581/003.32.3.35-48

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. https://doi.org/10.1002/9781118751534.ch1

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. https://doi.org/doi:10.1093/poq/nfu053

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. https://doi.org/10.1145/2567948.2576939

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. https://doi.org/10.1145/1718918.1718953

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. https://doi.org/10.1145/2064713.2064723

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. https://doi.org/10.3732/ajb.0900041

Pardo, R. (2001). La cultura científico-tecnológica de las sociedades de la modernidad tardía. Treballs de la Societat Catalana de Biologia, 51, 35-63. https://bit.ly/2T0n8B5

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). https://doi.org/10.1371/journal.pone.0094785

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. https://doi.org/10.33115/udg_bib/cp.v7i13.21986

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. https://doi.org/10.3916/C58-2019-07

ScienceFlows (Ed.) (2019). ScienceFlows. https://bit.ly/2wGZ8dB

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. https://doi.org/10.1177/0963662512472315

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

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. https://doi.org/10.2753/MIS0742-1222290408

Twitter (Ed.) (2019). Application programming interface. https://developer.twitter.com

Uren, V., & Dadzie, A.S. (2015). Public science communication on Twitter: A visual analytic approach. Aslib Journal of Information Management, 67(3), 337-355. https://doi.org/10.1108/AJIM-10-2014-0137

Veltri, G. (2013). Microblogging and nanotweets: Nanotechnology on Twitter. Public Understanding of Science, 22(7), 832-849. https://doi.org/10.1177/0963662512463510

Veltri, G., & Atanasova, D. (2015). Climate change on Twitter: Content, media ecology and information sharing behaviour. Public Understanding of Science, 26(6), 721-737. https://doi.org/10.1177/0963662515613702

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. https://doi.org/10.1002/asi.22713

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. https://doi.org/10.1007/978-3-642-20161-5_34