Julian Risch, Ralf Krestel
We are happy to announce that our resource paper "A Dataset of Journalists’ Interactions With Their Readership: When Should Article Authors Reply to Reader Comments?" by Julian Risch and Ralf Krestel was accepted at the 29th ACM International Conference on Information and Knowledge Management (CIKM2020). The paper is based on our long-term collaboration with journalists and data scientists in the online news domain. A preprint can be found here and the code and data are on GitHub. There is also an interactive visualization of the learned word embeddings.
Abstract:
The comment sections of online news platforms are an important space to indulge in political conversations andto discuss opinions. Although primarily meant as forums where readers discuss amongst each other, they can also spark a dialog with the journalists who authored the article. A small but important fraction of comments address the journalists directly, e.g., with questions, recommendations for future topics, thanks and appreciation, or article corrections. However, the sheer number of comments makes it infeasible for journalists to follow discussions around their articles in extenso. A better understanding of this data could support journalists in gaining insights into their audience and fostering engaging and respectful discussions. To this end, we present a dataset of dialogs in which journalists of The Guardian replied to reader comments and identify the reasons why. Based on this data, we formulate the novel task of recommending reader comments to journalists that are worth reading or replying to, i.e., ranking comments in such a way that the top comments are most likely to require the journalists' reaction. As a baseline, we trained a neural network model with the help of a pair-wise comment ranking task. Our experiment reveals the challenges of this task and we outline promising paths for future work. The data and our code are available for research purposes from: hpi.de/naumann/projects/repeatability/text-mining.html.
