Element 68Element 45Element 44Element 63Element 64Element 43Element 41Element 46Element 47Element 69Element 76Element 62Element 61Element 81Element 82Element 50Element 52Element 79Element 79Element 7Element 8Element 73Element 74Element 17Element 16Element 75Element 13Element 12Element 14Element 15Element 31Element 32Element 59Element 58Element 71Element 70Element 88Element 88Element 56Element 57Element 54Element 55Element 18Element 20Element 23Element 65Element 21Element 22iconsiconsElement 83iconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsiconsElement 84iconsiconsElement 36Element 35Element 1Element 27Element 28Element 30Element 29Element 24Element 25Element 2Element 1Element 66

Making Sense of User Comments

Making Sense of User Comments

Newsrooms are still searching for ways to manage user comments because of both a desire for professional distance from their audiences and a lack of analytical tools. This paper presents findings from our exploratory, interdisciplinary study in journalism research and computer science that focuses on the algorithmic classification and clustering of user comments. In contrast to endeavours that aim at filtering out hate speech or spam, we take a more constructive approach and focus on detecting particularly useful or high-quality user contributions that can be leveraged for journalistic purposes. On the basis of a literature review and our own preliminary research on audience participation and user review analytics, we developed a mock-up of a software framework to help journalists systematically analyze user comments to this end. We then surveyed its effectiveness through two group discussions – one with comment moderators and another with editors from different editorial departments of a large German online newsroom. Features that journalists and comment moderators considered useful include the categorization of user comments in pro- and contra-arguments towards a certain topic, the automated assessment of comments' quality as well as the identification of surprising or exceptional comments and those that present new questions, arguments or viewpoints.

Keywords: User comments, journalism, automated content analysis, software requirements
 

Loosen, W.; Häring, M.; Kurtanović, Z.; Merten, L.; Reimer, J.; van Roessel, L.; & Maalej, W. (2017): Making Sense of User Comments. Identifying Journalists’ Requirements for a Comment Analysis Framework. SCM Studies in Media and Communication, 7(4), http://www.scm.nomos.de/en/archive/2017/issue-4/beitrag-loosen/.

 

Making Sense of User Comments

Newsrooms are still searching for ways to manage user comments because of both a desire for professional distance from their audiences and a lack of analytical tools. This paper presents findings from our exploratory, interdisciplinary study in journalism research and computer science that focuses on the algorithmic classification and clustering of user comments. In contrast to endeavours that aim at filtering out hate speech or spam, we take a more constructive approach and focus on detecting particularly useful or high-quality user contributions that can be leveraged for journalistic purposes. On the basis of a literature review and our own preliminary research on audience participation and user review analytics, we developed a mock-up of a software framework to help journalists systematically analyze user comments to this end. We then surveyed its effectiveness through two group discussions – one with comment moderators and another with editors from different editorial departments of a large German online newsroom. Features that journalists and comment moderators considered useful include the categorization of user comments in pro- and contra-arguments towards a certain topic, the automated assessment of comments' quality as well as the identification of surprising or exceptional comments and those that present new questions, arguments or viewpoints.

Keywords: User comments, journalism, automated content analysis, software requirements
 

Loosen, W.; Häring, M.; Kurtanović, Z.; Merten, L.; Reimer, J.; van Roessel, L.; & Maalej, W. (2017): Making Sense of User Comments. Identifying Journalists’ Requirements for a Comment Analysis Framework. SCM Studies in Media and Communication, 7(4), http://www.scm.nomos.de/en/archive/2017/issue-4/beitrag-loosen/.

 

Infos zur Publikation

ÄHNLICHE PUBLIKATIONEN UND VERWANDTE PROJEKTE

Newsletter

Infos über aktuelle Projekte, Veranstaltungen und Publikationen des Instituts.

NEWSLETTER ABONNIEREN!