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BESOCIAL - a sustainable workflow for social media archiving

Social media is already a paramount part of our society and, thus, its content needs to be preserved. Because archiving is an expensive long-term commitment, deals with heterogeneous data and currently there is no complete workflow for social media archiving, we developed a Knowledge Graph-based workflow consisting of open source components.

This repository contains resources and software related to this workflow. First we present a comparison of social media harvesters where a summary is available in this GoogleSpreadsheet under a CC BY 4.0 license. Then we elaborate on the three major workflow components: (i) the selection and collection of social media content via the Social Feed Manager (SFM), (ii) the automatic and declaratively controlled Knowledege Graph generation, and (iii) the creation of domain-specific metadata records via declarative Knowledge Graph queries and templates.

The following workflow was developed for a social media archiving use case at KBR Royal Library of Belgium, funded by the Belgian Federal Science Policy Office (BELSPO) BRAIN 2.0. BESOCIAL.

Architecture for a social media archiving workflow Our architecture for a sustainable social media archiving workflow: heterogeneous data are collected (1, 2), declarative mapping rules are used to generate a Knowledge Graph (3), and declarative mappings and templates are used to create an API of domain-specific metadata records.

Comparison of social media harvesting tools

Several social media harvesting tools exist, we performed a comparison based on selected features to decide which tool we will reuse in our workflow.

We adapt a general web archiving framework of the Data Together Initiative and add social media archiving specific columns. A summary is available in this GoogleSpreadsheet under a CC BY 4.0 license.

After an initial selection we tested several of the surveyed tools. The folder harvesting-tool-comparison of this repository contains the documentation we created during this testing activity.

We selected the Social Feed Manager (SFM), based on its unique features combining machine-readable social media data with WARC-based preservation of technical metadata, and because it is an extensible framework reusing existing social media harvesting tools. Thus can also be used to harvest content from different social media providers, and new components like our Knowledge Graph generation can be integrated.

Social media harvesting

Compared to general websites, social media data comes from single providers and can be fetched via Application Programming Interfaces (APIs).

This step provides:

  • Content selection
  • Content collection

Based on the tool comparison above, we have chosen SFM to harvest social media. Users select content via the user interface of SFM and also configure how often the harvest process should be performed. Starts and stops of harvests are communicated on a RabbitMQ message queue, and thus further actions, such as a Knowledge Graph generation, can be triggered.

Knowledge Graph generation

Data related to social media archiving is heterogeneous and needs to be made interoperable for appropriate data stewardship: harvesters produce content in JSON format, technical metadata is enclosed in WARC files, and user-provided provenance information resides in databases.

This step provides:

  • Knowledge Graph requirements and a data model
  • Declarative Knowledge Graph generation rules
  • Software to automatically generate a Knowledge Graph

Knowledge Graph requirements and data model

We provide requirements for our Knowledge Graph in the form of user stories and competency questions, thus following common best practices. These requirements are available in this GoogleSpreadsheet. Given our use case we decided to use the Europeana Data Model (EDM), the de-facto standard for cultural heritage data. Additionally we use the PREMIS ontology in version 3 concerning preservation of digital content, which is able to describe collected and preserved WARC Files as well as the digital content they preserve. For each competency question we provide one SPARQL query to validate that our model can answer the question. Such a SPARQL query is a formal representation of the informal competency question, tied to a concrete model and therefore a concrete solution.

Knowledge Graph generation rules

The interoperable Knowledge Graph is generated using declarative reusable RML rules, thus no programming experience is needed. We express these RML rules using the more human-friendly YARRRML syntax, these rules are available in the mappings folder of this repository. During the Knowledge Graph generation process an RML processor generates a Knowledge Graph based on the RML rules.

Automatic Knowledge Graph generation

We provide adapter software which integrates an RML processor into the SFM framework, thus performing Knowledge Graph generation on the fly. The documentation and configuration of this adapter software resides in the message-queue-mapper folder of this repository. To this end, we use the RMLMapper which listens to the RabbitMQ message queue of the SFM framework.

Metadata records generation

Although a Knowledge Graph-based data model enables semantic interoperability of data, concrete preservation systems or stakeholders may demand data in a domain-specific syntax.

This step provides:

  • Software to build an API on top of a Knowledge Graph providing domain-specific metadata records
  • Queries to fetch data from the Knowledge Graph
  • Templates to create domain-specific metadata records

API to provide domain-specific metadata records

We use the tool Walder to set up an API on top of our Knowledge Graph. The configuration and related resources are available in the metadata-records-api folder of this repository. This tool can be configured to read RDF files in different serializations, from SPARQL endpoints or triple stores.

Knowledge Graph Queries

With the tool Walder GraphQL-LD can be specified. We provide queries as part of our configuration in the metadata-records-api directory.

Metadata records Templates

The tool Walder can use different HTML templating engines which we use to create XML-based metadata record templates. These templates reside in the metadata-records-api/views folder of this repository. We use the Handlebars engine and created three templates: one to view collections, one to export in EAD 2002 format and one in MARC XML format.


A sustainable workflow for Knowledge Graph-based social media archiving reusing open source components.






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