Comment-to-Article Linking in the Online News Domain

Online commenting to news articles pro- vides a communication channel between media professionals and readers offering a crucial tool for opinion exchange and free- dom of expression. Currently, comments are detached from the news article and thus removed from the context that they were written for. In this work, we propose a method to connect readers’ comments to the news article segments they refer to. We use similarity features to link comments to relevant article segments and evaluate both word-based and term-based vector spaces. Our results are comparable to state-of-the- art topic modeling techniques when used for linking tasks. We demonstrate that arti- cle segments and comments representation are relevant to linking accuracy since we achieve better performances when similar- ity features are computed using similarity between terms rather than words.