SMART Data Sprint: Interpreters of Platform Data
29 January – 2 February, 2018
9:30 – 17:30 I #SMARTdatasprint I Facebook Group: SMART Data Sprint
Universidade Nova de Lisboa I NOVA FCSH I iNOVA Media Lab
SMART Data Sprint is an intensive hands-on work, driven by social media data. We adopt inventive and experimental ways of reading, seeing and analysing platform data, with the aim of responding a set of research questions. For one week participants will have the chance to attend to keynote lectures, short talks, and parallel sessions of practical labs. After that, experts and scholars will invite participants to join projects and work in a collective problem.
SMART Data Sprint is open to doctoral students and scholars, as well as master’s students. Non-academics, research professionals, data journalists, designers, and passionates about data and platform-led studies are also welcome. Our goal is to collectively achieve concrete outcomes, creating the opportunity for knowledge production and providing an environment in which participants can equally contribute and benefit from each other’s expertise.
We are pleased to announce that Bernhard Rieder (associate professor in New Media and Digital Culture at the University of Amsterdam and a researcher with the Digital Methods Initiative) and Dhiraj Murthy (associate Professor of Journalism and Sociology at the University of Texas at Austin) are joining SMART Data Sprint 2018 with keynote talks and practical labs.
˚ ˚ Interpreters of Platform Data ˚ ˚
The definition of data is broadly accepted as “facts and statistics collected together for reference or analysis” or “a set of values of qualitative or quantitative variables”. Data in philosophy are “things known or assumed as facts, making the basis of reasoning or calculation”. A range of methods and methodologies have been developed overtime to question, measure, and critique society into the rationale of ‘data’.
On the basis of web platforms, likes, reactions, comments, shares and others are becoming the fine grain of social research, valued by quantification or what can be calculated. However, there is still so much to be explored in this field. There is a call for grasping platform data and learning how social research can benefit from that. Additionally, there is also interest in understanding how such data is constituted, shaped and made available. In this frame, how to interpret platform data? What counts?
Platform data is defined as every form of traceable human and non-human activity rendered in software, “produced to be standardised in form and flexible in meaning” and valuation . Scholars have been approaching different ways of reading, seeing and analysing platform data. For example, using a set of images on Instagram to approach climate change studies; or selecting a collection of Facebook Pages to explore the raise of alt-right/left movements; or utilizing a list of link domain to detect fake news. These collectable forms of activity (e.g. likes, posts, URLs) are made available for researchers through calling APIs, for instance relying on open source data extraction software. Before exploring such data outcomes (e.g. in CSV. files format), it is vital to understand the platform’s culture of use and technical infrastructure.
The SMART Data Sprint 2018 invites participants not only to be “interpreters of data” , but to consider the technology and processes behind data. That is a challenging scenario for social media driven research and its methodological process. Participants will have the opportunity to join projects that contemplate public health issues (Zika Virus) and Gender Studies (femicide).
 See https://en.oxforddictionaries.com/definition/data and https://en.wikipedia.org/wiki/Data
 Gerlitz, Carolin. 2016. “What Counts? Reflections on the Multivalence of Social Media Data.” Digital Culture & Society 2 (2). doi:10.14361/dcs-2016-0203.
 boyd, danah and Crawford, Kate, Six Provocations for Big Data (September 21, 2011). A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, September 2011. Available at SSRN: https://ssrn.com/abstract=1926431 or http://dx.doi.org/10.2139/ssrn.1926431
Gitelman, Lisa (editor). 2013. “Raw Data” Is an Oxymoron. MIT Press. doi:10.1080/1369118X.2014.920042.
˚ ˚ Keynote Talks and Practical Labs ˚ ˚
Senior researchers, and doctoral and masters students will also be leading Practical Labs* that contemplate the following themes:
» Data Extraction Tools
» Working with tabular data
» Raw Graphs
» Visual content analysis with Image Plot
» Network Analysis
» Image Networks
 Associate professor in New Media and Digital Culture at the University of Amsterdam and a researcher with the Digital Methods Initiative.
 Associate Professor of Journalism and Sociology at the University of Texas at Austin
* Detailed info on Practical Labs will be available for all SMART Data Sprint participants (after the acceptance notification).
˚ ˚ Projects ˚ ˚
The projects for SMART Data Sprint 2018 will cover Public Health and Gender Studies.
Project leaders: Elaine Rabello – Associate Professor at Social Medicine Institute I Rio de Janeiro State University , and Gustavo Correa Matta – Researcher on National School of Public Health / Oswaldo Cruz Foundation.
*This work and the research leaders are partially supported by the European Union’s Horizon 2020 Research and Innovation Programme under ZIKAlliance Grant Agreement no. 734548. They are also partially supported by The Newton Fund under the Grant Agreement Brazil-UK.
Project 1: Inês Amaral – Centro de Estudos de Comunicação e Sociedade – CECS, Universidade do Minho and Instituto Superior Miguel Torga.
Project 2 (focus on Italy): Alessandra Cicali – Italian Journalist at EURETE – EUropean REporting TEam.
Participants are also welcome to pitch a project by previously requesting the project description proposal form and then attaching it to their applications. Project descriptions will be available for all SMART Data Sprint participants (after the acceptance notification).
˚ ˚ Preparation ˚ ˚
We suggest all participants to watch web tutorials about data extraction tools and visualization software. We also recommend visiting the following links: Netlytic, médiala tools, DMI tools, Raw, list of research software developed by Bernhard Rieder. Tools such as Gephi for visualization and exploration of graphs and networks, and the Firefox extensions, e.g. Save Images, DownThemAll, and GrabThemAll, may also be helpful.
Please bring your computer and everything you need to support your work.
˚ ˚ Applications, Tuition Fee and Logistics ˚ ˚
Please send an email to smart.inovamedialab[at]fcsh.unl.pt with your CV (with photo) and a brief statement introducing your research interests and explaining how SMART Data Sprint may benefit your current work.
The deadline for applications is8 January 2018. New deadline is 21 January 2018.
The cost of SMART Data Sprint is 320 euros for the general public. There is a different fee for NOVA students (270 euros).
There is a special fee for those who are interested in Image Networks:
|Courses||Deadline for applications||Tuition Fee|
|SMART Data Sprint: Interpreters of Platform Data||
New date 21 January 2018
EUR 320 – [all participants]
EUR 270 – [NOVA students]
SMART Data Sprint: Interpreters of Platform Data
Image Networks: Automated Analysis of Visual Content
New date 21 January 2018
EUR 385 – [all participants]
EUR 325 – [NOVA students]
SMART Data Sprint is a full-time (from 9:30 to 17:30) and self-catered course. Thus, participants are responsible for their own meals. There are affordable restaurants close to NOVA FCSH and options inside the Faculty. We have no agreement with specific hotels, so it is up to participants to choose their accommodations. However, Lisbon has a wide offer in hotels. We have selected some that are near the venue, please check the list here.You should visit the Turismo de Lisboa website for touristic information.
˚ ˚Social Media˚ ˚
SMART is a research group of iNOVA Media Lab specialised in Social Media Research Techniques. The central idea of SMART lies in social media methods with the intention to build new data-driven research techniques to social science and humanities, and, in parallel, to engage with (and learn from) device culture.
About INOVA Media Lab
iNOVA Media Lab is an applied research laboratory at NOVA FCSH devoted to an interdisciplinary convergence of digital media and emerging technologies. The lab is organized around four research lines: (a) immersive and interactive storytelling, (b) visualization and data analysis, (c) digital journalism and (d) science communication.
Agre, Philip E. 1994. “Surveillance and Capture: Two Models of Privacy.” The Information Society 10: 110–27. doi:10.1080/01972243.1994.9960162.
boyd, danah and Crawford, Kate, Six Provocations for Big Data (September 21, 2011). A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, September 2011. Available at SSRN: https://ssrn.com/abstract=1926431 or http://dx.doi.org/10.2139/ssrn.1926431
Gillespie, Tarleton. 2015. “Platforms Intervene.” Social Media + Society 1 (1): 1–2. doi:10.1177/2056305115580479.Gitelman, Lisa (editor). 2013. “Raw Data” Is an Oxymoron. MIT Press. doi:10.1080/1369118X.2014.920042.
Gerlitz, Carolin. 2016. “What Counts? Reflections on the Multivalence of Social Media Data.” Digital Culture & Society 2 (2). doi:10.14361/dcs-2016-0203.Murthy, Dhiraj (2013). Twitter: Social Communication in the Twitter Age. Polity Press.
Omena et al. (forthcoming). Visualising Hashtag Engagement (draft available at https://www.academia.edu/34546911/Visualising_Hashtag_Engagement._Imagery_of_Political_Engagement_on_Instagram)
Rieder, Bernhard. 2014. “Engines of Order. Social Media and the Rise of Algorithmic Knowing.” SlideShare.https://www.slideshare.net/bernhardrieder/engines-of-order-social-media-and-the-rise-of-algorithmic-knowing.
Rieder et al. 2015. “Data Critique and Analytical Opportunities for Very Large Facebook Pages: Lessons Learned from Exploring ‘We Are All Khaled Said.’” Big Data & Society 2 (2): 2053951715614980. doi:10.1177/2053951715614980.