Online Social Data School: December 2023

Module overview

Our modules cover the research data pipeline of digital investigations, from the conception of a project involving social data to data collection, analysis, and visualisation. Teaching includes lectures, participatory sessions, workshops, and hands-on activities with leading researchers and practitioners in the field.

You will be able to extract data from social networks, analyse multiple video and photographic sources, and learn how computer vision and automation can help in the investigation of public interest stories and projects. We will also explore how to use AI, both as a tool for investigations and as an object of inquiry.

Methodology for Digital Investigations
This module addresses fundamental aspects of investigative practice in digital environments and dwells on the importance of using methodology(ies) for data inquiry. This module will benefit researchers doing investigations using Open Source Intelligence (OSINT) tools, data collection and analysis, and developing automated investigation tools. It critically reflects on the essential phases of digital investigations: Identification of a Problem (formulation of hypotheses), Information Gathering, Preservation, Verification, Analysis, and Dissemination.

By the end of the module, participants will have the principles to conduct investigations that effectively identify, prove, and strategically disseminate issues in the public interest with fairness and rigour. Its scope is meant to be applied along with the rest of the tools and methods from SDS 2023 modules.

Basic Automations for Investigations
In this module, we will look into basic software and online tools to carry out a series of automations which can help investigate the social. The tools and methods we will touch upon include automated social media content data collection, scraping, image/video analysis, and generative AI. This practical module will use case studies to demonstrate tools and exercises, including geolocation of occurrences of human rights abuses to bulk image analysis from popular trends on social media.

Social Network Analysis with Digital Data
“Social network” has become a catch-all term for the online spaces where we connect with other people and trade information in exchange for our personal data and attention. Considering the societal impacts of data-driven economics and politics, knowing how to reclaim and reappropriate these data to trace the form and content of online social networks is a vital skill for journalists, civil society and academics alike. 

This module will provide a gentle introduction to social network analysis (SNA) with digital data. Social Data School participants will be given the opportunity to “learn by doing” the process of digital data collection as well as the basics of social network visualisation and analysis. After being introduced to the fundamental concepts of SNA, the participants will explore all stages of a social network analysis project, including research design, data collection, data wrangling, graph visualisation, and analysis with essential network measures. The focus will be on retrieving electronic archival data (e.g., social media platforms) for non-programmers and practical examples of network analysis with specialised software (e.g., Gephi). At the end of the two sessions, participants will be equipped with the basic tools to perform meaningful visualisations and analyses of network data. Typical use cases of SNA range from investigative journalism to NGO monitoring and academic research.

Data protection and surveillance in a networked world
This module will explore legal and critical perspectives on data collection and use. From a legal point of view, this module will look at privacy considerations and general principles of data protection law, setting out some of the constraints and general principles applying to personal data, in particular. More critically, participants will explore questions of power arising from data collection and use, including surveillance business models, the data-driven economy, issues of facial recognition and other forms of surveillance, and state surveillance programs. The module will include a lecture and group work involving relevant case studies and a presentation.

Critical Approaches to Visualisation
It is often said we live in a society saturated with data. Visualisation methods can play a crucial role in helping to cut through the information overload. Badly designed charts, graphs and diagrams, on the other hand, can confuse or deceive. This session will introduce and contextualise established general principles of graphical communication and good practice in data visualisation methods, helping you to think more critically about your own work and that of others. We will focus on graphical display as an interpretative and persuasive practice which requires as much attention to detail as writing. A hands-on collaborative exercise with an investigative scenario will give you the chance to put your visualisation skills to work.

Principles of Machine Learning

In a world where Artificial Intelligence and Machine Learning are not only buzzwords in tech but permeate every aspect of our lives, this module cuts through the buzz to introduce and contextualise both the theory and practical aspects of machine learning. We will focus on the theoretical fundamental aspects of machine learning and the practical application of Teachable Machines, a no-code web tool.  Participants will get experience in understanding how machine learning models function, how to train them, and crucially, what implications demand close attention. This session is a direct dive into the essentials. By the end of the module, participants will be able to comprehend the theoretical fundamentals of machine learning and have hands-on experience using teachable machines, helping us demystify the complexities of  this technology.

Digital Cartography

Keynote speech

Jacopo Ottaviani will be talking about MapMakoko, a drone mapping and data journalism project implemented in Lagos, Nigeria.  Code for Africa, a non-profit organisation working on civic technology and digital transformation in African countries, worked with the local community to create a bottom-up, open source map of the area using drones, smartphones, and crowdsourcing. Putting themselves on the map was a way for the community to state their right to exist. Jacopo will talk about the project, explain its methodology and share behind the scenes challenges and achievements.

Workshop: Digital cartography

During the workshop, the participants will be learning how to make interactive maps. The trainer will use Datawrapper and go through the entire map lifecycle, ranging from data research, data cleaning, geolocation and visualisation. The trainees will have the chance to build an interactive map during the workshop under the supervision of the trainer, using sample geographic datasets, and get to know more about the use of drones and satellite imagery for mapping purposes.

*Please note this programme is subject to change

Teaching team

Dr Anne Alexander (Director of Learning, CDH)
Jonathan Blaney (CDH Research Software Engineer)
Dr Jennifer Cobbe (Postdoctoral Research Associate, Cambridge University)
Dr Irving Huerta (Data School Convenor, CDH)
Dr Hugo Leal (Research Associate, Minderoo Centre for Technology and Democracy)
Members of Amnesty International’s Digital Verification Corps (Centre of Governance and Human Rights at Cambridge University)
Ana María Zapata, Editor-in-Chief of the International Journal for Digital Art History

*Teaching team may be subject to change

How to apply & fees

Who can apply?

The school welcomes applications from all backgrounds.

You might be in journalism, marketing, academia, an NGO, activism, a trade union, a civil society organisation, civil service. Anyone who works with social data is welcome to apply.

No previous experience of coding is required and there are no specific academic requirements, however the course content is broadly suitable for those with an undergraduate degree or equivalent professional experience. The School is taught in English.

We are committed to facilitating participation by women, black and minority ethnic applicants as they have historically been under-represented in the technology and data science sector. We also welcome applications from outside the UK, assuming they can attend the live workshop slots during 1pm-4pm GMT. Sessions will not be recorded and therefore live attendance is required.

---

When and where

This school will be held online: 11–19 December 2023. Data School live sessions are timetabled daily from 1pm–4pm (GMT). To convert this to your timezone you can use this Time Zone Converter.

Sessions will include live-taught instruction on Zoom, demonstrations and discussions online, with access to self-paced study materials and support via email-based discussion groups between sessions. Participants will need a laptop or desktop computer and internet access to participate in the sessions. Some sessions will require software installation — full instructions will be provided but please ensure you have access rights to install software on the device you will be using.

The school is highly interactive and participants need to be able to join the discussions in real time, so please ensure you have internet access which will enable viewing of online videos and live participation through a video call.

An in-person Social Data School is also scheduled to take place in Cambridge 9-13 September 2024. Applications will open in early 2024 – please check our website or sign up to our mailing list for more details. Please note that the fees for in-person Data Schools are higher than for the online versions.

---

Fees 

Standard: £245 per person
Early Bird (until 3 September 2023): £195 per person
Concession (limited places): £75 per person

There is a limited number of concessionary places for the unemployed, unfunded projects, and Global South residents that can demonstrate financial need. In addition, a small number of bursaries (waived fee) are available to those who are not able to afford this training and can demonstrate how attending the school will be beneficial for them. You can apply for this on the application form.

The deadline for payment is four weeks before the start of the School.

---

How to apply

Complete the application form by 29 October 2023. You will hear whether your application was successful or not by 6 November 2023.

The Social Data School is application-only with limited places. During your application you should make best use of the free text sections to explain your current experience, and what you would get out of attending the School.

*Applications are now closed.