Social Media Analytics

Objectivos

Social media platforms provide an innovative paradigm to address various cultural (e.g., freedom of speech), sociological (e.g., community opinions), and technological problems (e.g., media recommendation and popularity prediction) which were otherwise hard to address purely by traditional approaches. These platforms have not only changed the way we communicate but how we see the world and interact with contemporary society. As techno-social mediators, social media serve different purposes (e.g. personal communication, marketing strategies, social research, political or branding monitoring, etc.) for different stakeholders (ordinary users, small business account holders, artists, politicians, etc.). Rooted in the mechanisms of the Web and driven by software functioning, the changeable mode of social media has challenging traditional approaches of social data mining.


In this background, social media analytics have been carried out as the methodology of gathering data from vast amounts of (semi) structured and unstructured online data to extract insights that help to make better business decisions. In this course, this latter methodology is challenged by the preposition of analysing social media through following the logic of the medium (platforms) and by repurposing the platform native data (e.g. URLs, posts, hashtags). The course offers a theoretical-practical-technical introduction to social media analytics by introducing the concept and practice of doing digital methods. Students will be challenged to develop a group project based on one of the following approaches: i) hashtag engagement research; ii) visual network analysis; iii) identifying trends; iv) ¿time¿ perspective study; v) event-based analysis; and vi) textual &-or visual content analysis.

Caracterização geral

Código

400081

Créditos

7.5

Professor responsável

Vasco Manuel Monteiro

Horas

Semanais - A disponibilizar brevemente

Totais - A disponibilizar brevemente

Idioma de ensino

Português. No caso de existirem alunos de Erasmus, as aulas serão leccionadas em Inglês

Pré-requisitos

  

Bibliografia

Dragt, Els. (2017). How to research trends - move beyond trend watching to kickstart innovation. BIS Publishers.

Gerlitz, Carolin (2016). What Counts? Reflections on the Multivalence

of Social Media Data. Digital Culture & Society  2 (2). doi:10.14361/dcs-2016-0203.

Gillespie, Tarleton (2010). ¿The politics of ¿platforms¿,¿ New media & society, volume 12, number 3, pp. 347-364. doi:https://doi.org/10.1177%2F1461444809342738

Omena, J. J., & Granado, A. (2020). Call into the platform!. Revista ICONO14 Revista Científica De Comunicación Y Tecnologías Emergentes, 18(1), 89-122. https://doi.org/10.7195/ri14.v18i1.1436

Omena,  J.J.,  Rabello,  E.  &    Mintz,  A.  Digital  Methods  for  Hashtag  Engagement 

Research. Social Media and Society, special issue ¿Studying Instagram Beyond

Selfies¿, edited by Alessandro Caliandro and James Graham (forthcoming)

Omena, J.J. & Amaral, I. (2019). Sistema de leitura de redes digitais multiplataforma.

In: Métodos  Digitais:  Teoria-Prática-Crítica.,  edited  by  Janna  Joceli  Omena. 

Lisboa: ICNOVA.

Rieder, B., & Röhle, T. (2017). Digital Methods: From Challenges to Bildung. In M. T. Schafer & K. van Es (Eds.), The Datafied Society: Studying Culture Through Data (pp. 109¿124). Amsterdam: Amsterdam University Press.

Rogers, Richard. (2013). Digital Methods. Cambridge, MA: MIT Press.

Rogers, R. (2019). Doing Digital Methods. Sage Publication.Marres, N.

(2017). Digital Sociology: The Reinvention of Social Research. London:

Polity Press.

Schaefer, M.T. & Van Es, K. (Eds.). (2017). The datafied society: Studying culture through data. Amsterdam University Press. 

Further specific bibliography will be advised in each class according to the subject of the session.

Método de ensino

TEACHING AND LEARNING METHODS

 

The unit is based on a mix of theoretical-practical lectures and tutorials (practical labs). The theoretical sessions include the presentation of theoretical concepts and methodologies as well as application examples. The main objective of the practical classes is to familiarise students with a range of software and research techniques to perform data exploratory analysis and visualisation.

Método de avaliação

ASSESSMENT METHODS

 

1st call ¿ Problem-based test (solving problems) (20%), Project Phase 1 (20%), Project Phase 2 (20%), Project Phase 3 (20%), Oral Presentation (20%)

2nd call ¿ Exam (50%), Project (50%)


Minimum grade of 8 for the problem-based test (for the 1st call) and for the second call exam, in order to pass. The project (phases 1, 2 and 3) do not have a minimum grade but are mandatory. If the student does not deliver one of the phases of the project automatically fail the course. For the oral presentation, the presence of the student is also mandatory.

 

The phases 1 and 2 of the project will be delivered in the week 9 and week 12 classes, respectively. No exam will be held on the first call exam date on the exam calendar. The second call exam is July 16, 2019 at 20h30.

 

Students enrolled in previous years have to do all phases of the project.


 

PROJECT

 

For Project Phase 1, students, in group, will define a case study and platform (s) to be analysed. The requirement is to deliver a written document (maximum 2000 words) containing the following table content: introduction to the case study + research questions + query design + approach + methodology + preliminary findings for social media analysis. It is a mandatory requirement to indicate the group member (s) responsible for each table of content.

 

Project Phase 1 delivery due date: March 31, 2020, 23h59.


 

For Project Phase 2, students, in group, will expand the work of project phase 1 by analysing and exploring digital networks. The requirement is to deliver a written document (maximum 2000 words) containing the following table content: justify how/why the chosen type of digital network can help in the ongoing case study + research questions + query design + preliminary findings for social media analysis. It is a mandatory requirement to indicate the group member (s) responsible for each table of content.

 

Project Phase 2 delivery due date: May 17, 2020, 23h59.


 

For Project Phase 3 (or final project), there will be specific requirements:

 

  • The project must be based on, at least, two articles and one white paper related to the chosen approach;

  • Project table of content must include: 

(see examples of projects reports advanced by digital methods in https://smart.inovamedialab.org/

 

Title

Team (alphabetical order)

Project presentation URL

Abstract (200) ¿ a summary of your project

Key findings (500 words) ¿ the results of your project through the main findings

Introduction (600 words) ¿ contextualise & justify the case study

Research questions

Research design (600-700)

_Query design (which platform, why? which grammar(s), why? which tools, why?)

_Visual protocol 

_Methodology

Findings (1000-1200 words)

Discussion (400-600)

References  (200-400)

 

  • Projects must include links for the final dataset. 

  • Writing requirements: IGNORE 12; Arial or Times; space between lines 1,5; minimum of 3000 words and maximum of 4500 words (including title, abstract & references).

 

Project Phase 3 delivery due date: June 10, 2020, 23h59.


 

Important notes

 

  • Regarding the approaches to analyse social media (hashtag engagement research;  visual network analysis; identifying trends; ¿time¿ perspective study; event-based analysis; and textual &-or visual content analysis), the class as a whole must opt for three approaches.

  • The datasets for the projects are to be built and defined by each group.

 

Further rules and information related to the project are available in Moodle in a separated file.

 

 

Conteúdo

CUC1. Introduction to social media & research

CUC2. Digital methods for social media analysis 

CUC3. Visual Social Media laboratory I Introduction to digital visual methods

CUC4. Introduction to cross-platform digital networks

CUC5. Social media laboratory

Classes visual map: https://drive.google.com/file/d/1ujJDfkakaCsBGEbjw6Z9IlaoNWxhUH6m/view?usp=sharing