SCAN – Systematic Content Analysis of User Comments for Journalists
How can reader comments in news articles be semi-automatically analysed? The SCAN Project will develop a framework that supports journalist to identify and use opinions, topic suggestions or further information in user comments for journalistic purposes.
Journalistic editorial departments face an increasing amount of feedback from the audience, e.g., in forums, comment sections and social media. The amount of comments and other feedback from the audience poses an enormous challenge for editorial departments. A large part of this effort concentrates on the downside of this development: filtering spam, hate speech or content that could be propaganda. The SCAN Project, in collaboration with Prof. Dr. Walid Maalej and his team of the Department of Informatics of Universität Hamburg, focuses on a constructive approach and wants to support journalists to extract the "journalistic sense" out of user comments for their own work but also for the audience itself. Thus, they should be able to find helpful comments or identify different opinions on a topic much faster. Dr. Wiebke Loosen and Lisa Merten present the interdisciplinary cooperation in the 15th episode of the BredowCast.
As part of the larger transformation of public communication in the digital age, professional journalists are facing an increasing amount of audience feedback, e.g. in forums, comments sections, and social media. In pre-digital times, conversations among audience members about mass media content remained largely invisible to journalists, with the exception of letters or calls to the editor. Today, the conversations of "the people formerly known as the audience" (Jay Rosen) are becoming visible to journalists, but also to other users, fundamentally changing how today's journalists and their audiences perceive, use, and manage this kind of feedback.
Most (online) newsrooms will consider comment sections and other features for audience feedback mandatory. However, newsrooms differ regarding how they manage these spaces, how they engage their users, and how they make use of the feedback for their own journalistic reporting – not the least because the manual handling and summarising of comments by journalists or dedicated social media editors is time consuming, while a fully automated analysis is expensive and error-prone. Accordingly, the development of tools to assist journalists in analysing, filtering, and summarising user-generated content has been identified as a main challenge for news organisations.
The Hans-Bredow-Institut works together with the Department of Informatics of Universität Hamburg in order to develop a framework that supports journalist to analyse, filter, and summarise user-generated content. This framework enables them to carry out a systematic, semi-automated analysis of audience feedback to better reflect the voice of users, mitigate the analysis efforts, and help journalist in generating new content from the user comments. With the framework journalists can create different samples of user comments, configure the questions they want to answer from the comments, and assign the question-answering task to "human coders" from the crowd.
The framework uses machine learning and natural language processing techniques in combination with manual content analysis (peer coding) and crowdsourcing to automatically filter spams, distinguish between praise and criticism, and cluster the comments into customisable categories. Moreover, journalists can create basic summaries about the comments such as how many users were for or against a particular position. As part of the project, we will (a) discuss and develop the framework requirements with journalists and (b) evaluate the framework in a concrete use case with a large German online news site.
The requirements for such a system will be specified together with journalists in the course of the project. Furthermore, it will be tested within the scope of a certain case on a big German news website.
Research programme:
RP1 - Transformation of Public Communication
Involved persons
Prof. Dr. Wiebke Loosen • Julius Reimer, M. A. • Dr. Lisa Merten •
Google Computational Journalism Research Programme
Prof. Dr. Walid Maalej, Universität Hamburg
Senior Researcher Journalism Research
Tel. +49 (0)40 45 02 17 - 91
Fax +49 (0)40 45 02 17 - 77
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media economy and media organisation • framework for media production • journalism • online journalism • media organisations • media economy
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