A Framework for Generating Annotated Social Media Corpora with Demographics, Stance, Civility, and Topicality
This framework provides a structured approach for researchers to create richly annotated social media datasets, enabling socio-technical analysis of online discussions, particularly for understanding public discourse on specific topics like student loans.
This paper introduces a framework for annotating social media text corpora with various categories, including demographic attributes, stance, topicality, and civility. They applied this framework to a Facebook comment corpus on student loan discussions, annotating for gender, military affiliation, age-group, political leaning, race, stance, topicality, neoliberalistic views, and civility.
In this paper we introduce a framework for annotating a social media text corpora for various categories. Since, social media data is generated via individuals, it is important to annotate the text for the individuals demographic attributes to enable a socio-technical analysis of the corpora. Furthermore, when analyzing a large data-set we can often annotate a small sample of data and then train a prediction model using this sample to annotate the full data for the relevant categories. We use a case study of a Facebook comment corpora on student loan discussion which was annotated for gender, military affiliation, age-group, political leaning, race, stance, topicalilty, neoliberlistic views and civility of the comment. We release three datasets of Facebook comments for further research at: https://github.com/socialmediaie/StudentDebtFbComments