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Appears in Collections:History and Politics Journal Articles
Peer Review Status: Refereed
Title: Heritage-based tribalism in Big Data ecologies: Deploying origin myths for antagonistic othering
Author(s): Bonacchi, Chiara
Krzyzanska, Marta
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Keywords: heritage
origin myths
Big Data
Issue Date: 2021
Date Deposited: 25-Mar-2021
Citation: Bonacchi C & Krzyzanska M (2021) Heritage-based tribalism in Big Data ecologies: Deploying origin myths for antagonistic othering. Big Data and Society, 8 (1).
Abstract: This article presents a conceptual and methodological framework to study heritage-based tribalism in Big Data ecologies by combining approaches from the humanities, social and computing sciences. We use such a framework to examine how ideas of human origin and ancestry are deployed on Twitter for purposes of antagonistic ‘othering’. Our goal is to equip researchers with theory and analytical tools for investigating divisive online uses of the past in today’s networked societies. In particular, we apply notions of heritage, othering and neo-tribalism, and both data-intensive and qualitative methods to the case of people’s engagements with the news of Cheddar Man’s DNA on Twitter. We show that heritage-based tribalism in Big Data ecologies is uniquely shaped as an assemblage by the coalescing of different forms of antagonistic othering. Those that co-occur most frequently are the ones that draw on ‘Views on Race’, ‘Trust in Experts’ and ‘Political Leaning’. The framings of the news that were most influential in triggering heritage-based tribalism were introduced by both right- and left-leaning newspaper outlets and by activist websites. We conclude that heritage-themed communications that rely on provocative narratives on social media tend to be labelled as political and not to be conducive to positive change in people’s attitudes towards issues such as racism.
DOI Link: 10.1177/20539517211003310
Rights: This article is distributed under the terms of the Creative Commons Attribution 4.0 License ( which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (
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