InstaCan: Examining Deleted Content on Instagram

As the speed, volume, and heterogeneity of data produced on the Web increases, we are faced with developing more intelligent and efficient strategies for storing and archiving data. The archiving of Web data involves many technical, governance, and policy related challenges, however one of the most prominent and timely challenges that archivists face involves the deletion of data which from existing data stores; popularised by the various policy-related movements, such as the 'right to be forgotten'. For social media researchers, organisations, and analysis companies, it is a requirement for them to comply to the removal requests of the streams they consume. However, due to the nature of archiving, this is often difficult to comply to, without becoming a resource intensive exercise. In this paper we investigate deleted content on Instagram, the structure of the Instagram platform, and develop and evaluate a method to identify content which will becomes deleted. Our work contributes to the archiving community, and the Web Science community, interested in understanding the social factors that contribute the use of Social Media.