JOURNAL ARTICLE

Networked Diasporic Memory Work of Assyrian Genocide Remembrance.

  • Published In: Diaspora: A Journal of Transnational Studies, 2025, v. 25, n. 1. P. 157 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Hughes, Erin; Karels, Martina 3 of 3

Abstract

This article focuses on how the Assyrian diaspora in the United States uses social media to commemorate and sustain collective memory of the Assyrian Genocide (also known as the Seyfo), a WWI-era atrocity perpetrated by the Ottoman Empire that resulted in the massacre of an estimated 250,000 Assyrians and the formation of a widespread diaspora. It examines the strategic role of diasporic organizations as memory agents who employ platforms like Twitter and Facebook around key remembrance days—Seyfo Remembrance Day (24 April) and Assyrian Martyrs Day (7 August)—to share historical narratives, foster intercommunal solidarity with Armenian and Greek genocide survivors, and advocate for formal recognition. The study introduces the concept of "networked memory work," describing how these actors use social media to transmit and institutionalize the memory of the genocide beyond the diaspora, aiding ongoing trauma processing and identity formation across generations. Calls to action, including education, cultural preservation, and political advocacy, are central to this digital memory work, which remains vital amid denialism and the challenges of sustaining diasporic cohesion.

Additional Information

  • Source:Diaspora: A Journal of Transnational Studies. 2025/04, Vol. 25, Issue 1, p157
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2025
  • ISSN:1044-2057
  • DOI:10.3138/diaspora.24.3.2024.00.14
  • Accession Number:187977667
  • Copyright Statement:Copyright of Diaspora: A Journal of Transnational Studies is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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