This week we are presenting our research on using topic models in search and content analysis at the Young Statistician Meeting 2016. You can find our abstract and the accompanying slides below.


Kitti Balogh: Unveiling latent topic structure in anti-Roma discourse using Latent Dirichlet Allocation 

From the mid 2000’s the number of anti-Roma and racist utterances have been increasing in Hungary and this manner of speech has also become accepted in common discourse. The research focused on extracting anti-Roma topics over this period using a hierarchical Bayesian model called Latent Dirichlet Allocation (LDA). The source of the analysis was collected from online newsportal which is the flagship of the far-right media in Hungary. The corpus consists of more than 10.000 anti-Roma news from 2006 until 2015. 27 anti-Roma topics were extracted by using LDA which opens the possibility to analyze the distribution of various topics over time and see how they are connected to the most influential events during the period of investigation. The identified topics correspond to categories identified by qualitative studies on Roma media representation in Hungary. Our research suggests that topic modeling could be a useful supplementary tool to the toolbox of traditional qualitative discourse analysis researchers. Our research project culminated into an interactive data visualization and a data visualization dashboard which can be accessed on following links:



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