International Journal on Digital Libraries

, Volume 17, Issue 3, pp 239–256 | Cite as

Characteristics of social media stories

What makes a good story?
  • Yasmin AlNoamany
  • Michele C. Weigle
  • Michael L. Nelson


An emerging trend in social media is for users to create and publish “stories”, or curated lists of  Web resources, with the purpose of creating a particular narrative of interest to the user. While some stories on the Web are automatically generated, such as Facebook’s “Year in Review”, one of the most popular storytelling services is “Storify”, which provides users with curation tools to select, arrange, and annotate stories with content from social media and the Web at large. We would like to use tools, such as Storify, to present (semi-)automatically created summaries of archival collections. To support automatic story creation, we need to better understand as a baseline the structural characteristics of popular (i.e., receiving the most views) human-generated stories. We investigated 14,568 stories from Storify, comprising 1,251,160 individual resources, and found that popular stories (i.e., top 25 % of views normalized by time available on the Web) have the following characteristics: 2/28/1950 elements (min/median/max), a median of 12 multimedia resources (e.g., images, video), 38 % receive continuing edits, and 11 % of their elements are missing from the live Web. We also checked the population of Archive-It collections (3109 collections comprising 305,522 seed URIs) for better understanding the characteristics of the collections that we intend to summarize. We found that the resources in human-generated stories are different from the resources in Archive-It collections. In summarizing a collection, we can only choose from what is archived (e.g., is popular in Storify, but rare in Archive-It). However, some other characteristics of human-generated stories will be applicable, such as the number of resources.


Stories Storify Storytelling Social media Curation Collections Archive Social networks Archive-It 



This work was supported in part by IMLS LG-71-15-0077-15. We thank Kristine Hanna and Jefferson Bailey of the Internet Archive for the Archive-It data and baseline story summaries.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Yasmin AlNoamany
    • 1
  • Michele C. Weigle
    • 1
  • Michael L. Nelson
    • 1
  1. 1.Department of Computer ScienceOld Dominion UniversityNorfolkUSA

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