This repo contains resources for students on the MA Data Journalism at Birmingham City University. The course module in Narrative covers a range of techniques for telling data stories across text, video and audio, visual and interactive forms, and web, chat and social platforms.
Most resources for the module can be found in the Narrative Moodle website. This repo contains extra resources and activities specific to data journalism.
Narrative: from media to interactive media aims to give you the skills to critically adapt to both new and existing storytelling formats and platforms.
We begin by introducing basic narrative concepts that can be used to get to grips with any format you might need, or want, to work in. Then we look at specific formats, from writing for the web, and online and social video, to visualisation and interactivity. By the end of the module you should be able to identify how to approach a specific story across different platforms - and you'll be telling one story in three different formats.
- Module Leader: Paul Bradshaw (paul.bradshaw@bcu.ac.uk) 0121 331 5367
- Twitter.com/paulbradshaw
- Room: MP364
- Initiate and develop innovative, conventional or emerging narrative techniques to produce a media artefact within an identified professional context
- Identify and critically evaluate narrative techniques used within relevant media
You will also be expected to feed your own experiences into each class - and your own problems and questions - rather than coming to the sessions with nothing to contribute or build on. As independent learners the emphasis is on you to drive your learning forward through conversation rather than accept it passively.
By the end of this week you should be able to identify the elements of a story, explain some of the problems with storytelling that need to be considered as a professional media worker, and use that to improve your own storytelling.
- Lecture: Narrative concepts
- Workshop & reading: see Moodle
- Task: Critical review of news structure in data stories
By the end of this week you should be able to use professional techniques to generate ideas and choose those that are professional and right for a target audience. You should also be able to write effective prompts to develop ideas with generative AI tools - and identify the challenges in using genAI models.
- Lecture: Developing great ideas for factual storytelling
- Workshop & reading: see Moodle
- Task: Brainstorm ideas for an interview-based story using the techniques you read about. Choose 3 of the best ideas and develop (improve) them. Pick one idea and identify 5 people you could interview
By the end of this week you should be able to outline a range of ways of identifying and finding potential interviewees and other sources for stories.
- Lecture: Story-led research and interviewees
- Workshop & reading: Research for the Newsroom playlist
- Task: Use a range of sources to identify potential interviewees for your story idea across all 4 categories. Identify any over-represented demographics and ways you might address that. Make a shortlist of your top 5 - why are they top?
By the end of this week you should be able to identify steps to take when planning an interview, identify and address cognitive biases, and deal with scenarios such as “copy approval” and anonymity.
- Lecture: Planning a great interview
- Workshop & reading: see Moodle
- Task: Make approaches to the five names on your interviewee list. Brainstorm the questions you will ask THEN draw up a shortlist of the ones best for your story
By the end of this week you should be able to explain different considerations when interviewing for online, video, or audio; how to conduct an interview using active listening; and how to identify and respond to interviewee strategies.
- Lecture: Doing the interview
- Workshop & reading: see Moodle
- Task: Conduct an interview — for story or for practice. Record it and review: good practice and improvements
During this week you should be consolidating your learning so far, developing your skills on one or more editorial project, reviewing examples of data-driven storytelling, and continuing to read widely around the subject.
By the end of this week you should be able to identify structure in storytelling and how different genres use forms such as the inverted pyramid and kabob. You could be able to use those structures to organise your own material into a first draft of your story.
- Lecture: Narrative structure and how it can help us organise information into stories
- Workshop & reading: see Moodle
- Task: Plan a story outline
- Additional reading:
- Lecture 1: Writing for the web - BASIC principles
- Workshop & reading: see Moodle
- Task: Assignment development
By the end of this week you should be able to identify more advanced narrative structures and outline how these are used in longer stories and feature formats to maintain audience interest
- Lecture 1: Genre and structure in factual storytelling (14')
- Lecture 2: Storytelling techniques: temporality, pacing, the narrator role and "show, don't tell" (12')
- Workshop: using an interview transcript to explore structure and genre
- Task: Edit your story for movement
- Reading: Data journalism on radio, audio and podcasts
By the end of this week you should be able to identify techniques used in social media storytelling, particularly the use of visual devices such as emojis, gifs and memes. You should be able to use some of these visual devices to tell stories across platforms.
- Video lecture 1: Visual storytelling (21')
- Video lecture 2: Writing for social and shortform (22')
- Reading: Writing for social media and chat apps
- Workshop: visual storytelling
- Task: Create some visual storytelling and social storytelling about your own work
Extra material on social video:
- Making video for online and social: the 5 types (24')
- Four roles online video plays (22')
- Read: Data journalism in broadcast news and video: 27+ examples to inspire and educate
By the end of this week you should be able to identify professional practices in your field and describe how you have applied those in your work, or why and how you might in future. You should be able to evaluate your work in relation to wider literature - both academic and practice-based.
- Workshop & reading: see Moodle
- Task: Review and revised production of multiplatform data story