Increasingly, news breaks on social media, where ordinary citizens post images and videos and their own commentary in the form of text. This user-generated content (UGC) is newsworthy information and invaluable for newsrooms. In order to incorporate this data into a news story, the journalist needs to process, compile and verify information on the social web within a very short timespan. This is done mostly manually and is a time-consuming and labour-intensive process for media organisations. This paper proposes Social Semantic Journalism framework as an assistant to journalists for breaking news production, and as solution to the above problem.
The consumers of news and information are no longer passive and isolated
consumers. Smart phones, digital cameras, mobile internet and social media platforms have
made us all broadcasters of information. We consume information from traditional
news sources, but also through social media platforms, with 1/3 of adults under 30
getting their news from social media [
Journalists are already monitoring social media for scoops, details, and images, but the process is laborious, and provides inconsistent results. In the deadline-driven world of journalism, the need to process huge volumes of community-sourced data in order to extract potential news stories is a universal problem. This data, known as user-generated content (UGC) is mostly unstructured, unfiltered and unverified, and often lacks contextual information. Traditional approaches to newsgathering are quickly overwhelmed by the volume and velocity of information being produced. Extracting stories from UGC goes beyond the simple transcoding of individual streams; it is also important for news organisations to have richly annotated, analysed and interconnected content.
Social Semantic Journalism addresses a universal problem experienced by media
organisations; the combination of vast amount of UGC across social media platforms
and the limited amount of time the journalist has to spare to extract potential news
stories from these mostly unstructured, unfiltered and unverified data. In this
situation, there is evidently a need for solutions that can help source, filter and verify
social media content for media organisations who are now competing with the
continuous flow of free content available 24/7 on the web, while budgets are tight and
deadlines are tighter. Social Semantic Journalism also aims to address the chief obstacle
facing news organisations, the vetting process, since the current manual process of
checking through user-generated content is considered to be overwhelming and
inadequate [
The remainder of this paper is as follows. Section 2 introduces Social Semantic Journalism. Section 3 briefly describes the technologies to best suited to constitute a framework for Social Semantic Journalism. Section 4 concludes the paper with summary remarks about the potential impact of Social Semantic Journalism. 2
The user-generated content shared on the social media now forms a significant source
of first hand information and content for breaking news coverage. Every minute 347
new blog posts are created, 74 hours of new video is uploaded to YouTube, 100,000
tweets are sent and Facebook users share 684,478 pieces of content [
Social Semantic Journalism proposes a Semantic-based solution that can formalise
and link unstructured UGC to other semantically-enriched data sets in what is termed
the “Linked Data Cloud” for integration, verification and fact-checking purposes, e.g.
government datasets or DBpedia/Wikipedia. By working with the media industry,
Semantic Web researchers can significantly add to the emerging field of
computational or data journalism by “developing techniques, methods, and user interfaces” that
can “help discover, verify, and even publish new public-interest stories at lower cost”
[
Semantic Web technologies are a means for providing a machine readable data
structure and also facilitate information integration from various sources which
are built using the same underlying technologies. The Semantic Web effort is
considered to be in an ideal position to make social web platforms interoperate by providing
standards to support data interchange and interoperation [
There are a number of technologies that will be required to produce a Social Semantic Journalism Framework. These technologies would inevitably work together, becoming the inputs and outputs for each other, creating a process up to meet the challenge that social media presents to journalists and editors as they try to what is news worthy in UGC. Figure 2 illustrates the technologies and process to realise Social Semantic Journalism. Content Discovery is the ingestion the raw content from social media and enriching it with semantic metadata, which can be made use of by the other phases. Data Ingestion is gathering a representative sample of data from microblog updates and the users posting them.
Entity Extraction and Semantic Annotation involves the identification of semantic entities such as ‘place’, ‘organisation’ and ‘person’ and linking them with relevant semantic metadata from Linked Open Data.
Noise Detection is the detecting and filtering non-relevant content from streams where a topic has already been identified.
Burst Detection is the discovery of bursts or sudden increases in frequency of topic and/or location specific microblog.
Filtering and Contextualisation uses the derived metadata from Content Discovery phase, and further refines the metadata, associating related content, putting news stories within a wider context of the news agenda and world events and starting to develop a provenance trace.
Contextualisation discovers background and contextual information for a specific news story, leveraging the metadata created during the content discovery stage. Provenance Construction is to produce a provenance trace and graph to be utilised for the trust verification stage.
User and Content Geolocation is to approximate the relevant location of an event/tweet by exploiting a combination of explicit GPS coordinates, disambiguation through semantic annotation and making use of social graph data.
Community Analysis relies on the event, burst and noise detection to isolate users generating timely and relevant UGC.
Trust Verification utilises the provenance data and the extracted concepts from Content Discovery and Filtering and Contextualisation.
Provenance Analysis provides the analysis, abstraction and summarisation of provenance information, which would help journalists in identifying eyewitnesses and assessing reputation of the source.
Point-of-View Analysis provides indicators for the perspective or point-of-view of a piece of content to inform the journalist as to the likely perspective the content takes.
4
This paper introduced Social Semantic Journalism framework, as a solution that can help journalists in the process of breaking news production, when the initial source is social media.
The potential impact of a framework for Social Semantic Journalism includes a dynamic and flexible alternative to newswire subscriptions, providing high-quality and timely news and open up the market to new media aggregators, curators and commentators, creating new business opportunities and opportunities for media exploitation and reuse. The novel ability to effectively exploit, large-scale social media streams by journalists will give a voice to citizens, enabling journalists to do richer and more relevant story development faster.
There is increasing evidence of a tipping point for technologies such as semantic web, linked data and natural language processing. Non-technology companies in the news industry sector such as BBC, New York Times, Novosti and the Press Association have begun to make considerable capital investments in the technologies employed with this project (e.g. linked data, language analytics, etc.). The benefits of using Linked Data for knowledge sharing, integration and reuse has been validated in a variety of application domains and contexts from the digital enterprise to healthcare and green IT. A social semantic journalism framework is an opportunity to demonstrate the viability of these approaches for integrating social media information from a community of users into the mainstream news media workflow.
An immediate consequence of the framework would be the development of a set of API-driven semantic services and tools to process social media content and data is the stimulation of demand for high-performance, bandwidth-hungry media applications and services. The torrent of disparate, contradictory and unstructured social media content is made accessible, relevant and useful.
The work presented in this paper has been partially funded by Science Foundation Ireland (SFI) under Grant Numbers 2/TIDA/I2389 and SFI/08/CE/I1380 (Lion-2).