Publication
Data, technology and journalism
| dc.contributor.author | Silvestre, Cláudia | |
| dc.contributor.author | Frazão, Pedro | |
| dc.date.accessioned | 2022-01-04T15:20:48Z | |
| dc.date.available | 2022-01-04T15:20:48Z | |
| dc.date.issued | 2021-12-09 | |
| dc.description.abstract | Technological development has had a major impact on all levels of our societies [3], namely in the way of thinking and practicing journalism [5]. Consequently new job types like data journalist or infographic designers have been evolved in newsrooms. This reveals that jour nalism students should be trained to learn the contemporary technologies. “Interestingly, although transformation of journalism practices after the artificial intelligence and algo rithms is on the agenda of recent communication studies, the reflection of this technology in journalism education is still a neglected field of study.” ([4]:170). This technological change also include the way we teach data analysis, since data are really used as a primary source for telling a story [6]. Thinking about that, we carried out a small project using Python programming language. Communication students usually do not have any contact with a programming language, but it is a valued skill [1]. The goal of this project was to perform a statistical analysis of the Portuguese media coverage of the pandemic, focused on news headlines. We have chosen three Portuguese large-circulation newspapers, two daily newspapers: Di´ario de Not´ıcias and P´ublico, and a weekly, Expresso. The data set have 2025 headlines related to pandemic and were collected between between January and March, 2021. Data cleaning was the first step. Incomplete data and duplicate news were removed, after that we got 2020 headlines. Then we enriched the dataset by adding more variables, such as, news is on the first page (yes/no), references to numbers in the headline (yes/no) and the tone of the news (positive/neutral/negative). With this analysis we can conclude that most (80%) news had a neutral tone, and numerical information was rarely used (10%), but when it was used it often highlighted positive or negative information. The most common words used in headlines were pandemia (pandemic), vacinas (vaccines) and COVID. And headlines with numbers generally referred to the pandemic evolution, like number of infected, dead or recovered, and to COVID-19 crisis economic impact. Although this project is quite simple, we find it useful to introduce a programming lan guage, namely Python [1]. It also helps to increase technological skills knowledge which is ”one of the requirements of the modern era in additional of traditional journalism courses in journalism education” ([2]:7471). | en |
| dc.description.abstract | References [1] J. Brunner and J. Kim. Teaching data science. Procedia Computer Science, 80:1947–1956, 2016. [2] Ozen C. New Technologies Challenging the Practice of Journalism and The Impact of Educa- ¨ tion: Case of Northern Cyprus. EURASIA Journal of Mathematics, Science and Technology Education, 13:7463–7472, 2017. [3] J. Lasser, D. Manik, A. Silbersdorff, B. S¨afken, and T. Kneib. Introductory data science across disciplines, using Python, case studies, and industry consulting projects. Teaching Statistics, 43:190–200, 2021. [4] B. Narin. Teaching high tech storytelling: Reorganizing journalism education for programmer journalists and data journalists. Centro de Publicaciones PUCE, pages 169–201, 2018. [5] V. Somayyeh. The impact of technology on journalism. International Journal of Advance Engineering and Research Development, 5:550–555, 2018. [6] D. Spiegelhalter. The Art of Statistics : How to Learn from Data. Basic Books, United States, 2021. | pt_PT |
| dc.description.sponsorship | Investigação financiada pelo Politécnico de Lisboa como um Projeto de Investigação, Desenvolvimento, Inovação e Criação Artística (IDI&CA) IPL/2020/ETC_ESCS | pt_PT |
| dc.description.version | N/A | pt_PT |
| dc.identifier.citation | Silvestre, C. & Frazão, P. (2021, dez, 09-11). Data,technology and journalism. Paper presented at XVIII Meeting of the Portuguese Association for Classification and Data Analysis (JOCLAD 2021), Universidade da Beira Interior – Covilhã, Portugal | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.21/14136 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | CLAD - Associação Portuguesa de Classificação e Análise de Dados | pt_PT |
| dc.publisher | UBI - Universidade da Beira Interior | pt_PT |
| dc.relation.publisherversion | http://www.joclad.ipt.pt/joclad2021/ | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | pt_PT |
| dc.subject | Data analysis | en |
| dc.subject | Journalism students | en |
| dc.subject | Python | en |
| dc.subject | Teaching | en |
| dc.subject | IPL/2020/ETC_ESCS | pt_PT |
| dc.title | Data, technology and journalism | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Universidade da Beira Interior – Covilhã, Portugal | pt_PT |
| oaire.citation.title | XVIII Meeting of the Portuguese Association for Classification and Data Analysis (JOCLAD 2021) | pt_PT |
| person.familyName | Silvestre | |
| person.givenName | Cláudia | |
| person.identifier.ciencia-id | DA12-EF3F-C7CD | |
| person.identifier.orcid | 0000-0002-8850-4304 | |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
| relation.isAuthorOfPublication | 08fbc1bf-3387-4137-8c03-c4664dd43375 | |
| relation.isAuthorOfPublication.latestForDiscovery | 08fbc1bf-3387-4137-8c03-c4664dd43375 |
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