[Posting the following on behalf of Ruth Page, one of our symposium speakers.]
In my paper at the Moving Lives symposium I will talk about a model I am developing for analysing shared stories. Shared stories are a narrative genre that is increasingly important in the contemporary communicative landscape. Shared stories have antecedents in retellings that are found in literary contexts (adaptations), in co-constructed conversational stories and in the multiple versions of stories that circulate in the news media. But above all, shared stories are prevalent in social media contexts where the verb ‘share’ has taken on media-specific meanings. As John (2013) points out, ‘sharing’ has become a potent keyword in social media, where its communicative meaning (to tell) and its distributive meaning (to give away) has a particular resonance in the ability to publish and repost stories which are told and retold by many people and across many contexts.
In the biographical narratives about public figures that are told in social media contexts, the analysis of shared stories needs to take into account of the contexts in which the stories are produced and reproduced. This includes analysis of:
- The generic context (the site and its affordances)
- The interactional context (the ways stories are embedded in relation to other kinds of talk)
- The multimodal context (the audio-visual content that might accompany any written text)
- The meta-data available
The analysis of how material is shared (in terms of distribution) can benefit from ‘big data’ approaches that are able to trace patterns through the meta-data across many interactions, but often this can miss the multi-modal complexity of what is being told in the story itself. As a case study, I will explore data taken from a public Facebook community page that marked the death of former Prime Minister, Margaret Thatcher.
A note on tools:
Netvizz is a great, free tool developed by Rieder (2013) and his colleagues, which allows you to export publically available data from Facebook and then use tools like Gephi to explore network analysis, or transfer content into other software for further analysis.
And a note on the wider project:
I have also been applying this model of shared stories to data taken from Wikipedia and Twitter, though there will not be time to discuss this in my presentation! I’m very happy to chat in person about the project, or to share draft work in progress.