Selfie stories in time: connecting qualitative and quantitative analysis of image-based personal narratives.

Words-from-2014-11-02-to-2014-12-02En noviembre estaremos en el congreso de ECREA en Praga presentando un paper titulado  ‘Selfiestories in time’ donde comenzaremos a abordar las narrativas personales que encontramos en los selfies objeto de nuestro estudio. Las diferentes temporalidades están presentes en los distintos casos de estudio, Entre ellos, la producción de imágenes en los contextos de festivales de música, muy ancladas en la experiencia del evento en si mismo. En los próximos meses iremos detallando en los posts los resultados de testar nuestra propuesta metodológica mixta en diferentes contextos. 

Abstract 

Selfie stories in time: connecting qualitative and quantitative analysis of image-based personal narratives

This presentation is a part of an ongoing research project entitled ‘Selfiestories and personal data” funded by the BBVA (Spain). This two-year research project approaches the phenomenon of the selfie as performed and part of a personal or collective narrative (Vivien and Burgess, 2013), present in social media platforms like Instagram or Twitter. Methodologically, we do so through a double perspective: on the one side, qualitative fieldwork oriented to specific forms of personal narratives and, on the other side, through the analysis of vast datasets coming from personal narratives related to collective time-set experiences, using Big Data techniques. For this purpose, we are developing a data extractor based on instagram to test how these narratives can be analyzed from this complementary perspective. Considering selfies beyond image and representation (Gómez and Thornham, 2015) and highlighting here the social and conversational aspects of the phenomenon, in our empirical work we will focus on the contextual information. In this regard, location, description (through plain text or hashtags, but also mentions, timeframe, its connection to a user stream or feed (Fallon, 2014), and the relation to other narratives become essential.This perspective is essentially different to other attempts to approach the notion of the selfie through Big Data image analysis (Manovich’s Selfie City being the prime example). In order to conduct a parallel research, both from qualitative and quantitative approaches, timeframes constitute a common element which can allow an interrelated and enriched set of data when applied to specific case studies. In our presentation we will draw an initial distinction of different forms of personal narratives and its relation to specific timeframes and experiences. Based on our preliminary work, we will suggest a consideration of different kinds of image-based shared personal narratives: some more tied to the time of the everyday – like those connected to health, self-improvement or holidays.- and others pointing to a delimited period of time -like those connected to public events or unexpected incidents-. The first category, more oriented to the individual, extends across a more or less continuous timeframe, but at the same time it’s comparable to parallel actions carried out by other users in multiple locations (for example, narratives about fitness or healthy food). The second category, more oriented to the collective, is time-constrained and it’s easily identifiable with similar actions by other users in identical or close locations (for example, selfies taken during a General Election’s day or during a music festival). A third category, midway between the everyday and the eventful, might be identified through purposeful actions along time, for example social, commercial or political campaigns, as well as artist appropriations of the language of the personal narratives. We will present some preliminary empirical results of our research through a set of four case studies that illustrate these different categories in a sense that image and contextual data can help us to shape a complex methodology where qualitative fieldwork enters into a dialogue with Big Data analysis.

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