Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics

Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019), pages 1004–1006 Minneapolis, Minnesota, USA, June 6–7, 2019. ©2019 Association for Computational Linguistics 1004 Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics Nazanin Afsarmanesh, Jussi Karlgren, Peter Sumbler, Nina Viereckel Gavagai Stockholm , Sweden {nazanin, jussi, peter, nina}@gavagai.io Abstract This report describes the starting point for a simple rule based hypothesis testing excercise on identifying hyperpartisan news items car- ried out by the Harry Friberg team from Gav- agai. We used manually crafted metatopics, topics which often appear in hyperpartisan texts as rant conduits, together with tonality analysis to identify general characteristics of hyperpartisan news items. While the precision of the resulting effort is less than stellar— our contribution ranked 37th of the 42 successfully submitted experiments with overly high recall (95%) and low precision (54%)—we believe we have a model which allows us to continue exploring the underlying features of what the subgenre of hyperpartisan news items is char- acterised by. 1 Hyperpartisanism Hyperpartisan news are news items that are strongly argumentative and one-sided. However, being biased is not enough to be characterised as being hyperpartisan, neither is it enough for a news item to use strong language. Confounders for this task includes items that use strong lan- guage without being partisan, items that are sub- jective but not ”hyper”, and items that report dis- passionately on typically hyperpartisan topics. We hypothesise that authors of hyperpartisan texts are in the process of performing a sub-genre of their own, intended not as much to convey the reader information about some state of the world but to mobilise sentiment and affect in the read- ership, and establishing a shared attitudinal space. Taking this point of departure, we assume that the linguistic items employed by the authors of hy- perpartisan text are not only related to the topics under discussions, nor to argumentation, but also include some genre-specific features to explicitly signal hyperpartisanness. This report describes an experiment based on these starting points, per- formed on data from the 2019 SemEval task on Hyperpartisan News Detection. (Kiesel et al., 2019) 2 Gavagai Explorer The Gavagai Explorer is a commercially available tool which provides an end-to-end solution for the analysis of unstructured text data (Espinoza et al., 2018). We have in these experiments made use of its components for topic clustering, sentiment analysis, and concept modelling. 2.1 Trigger Topics The topic clustering is based on lexical cues, and can be used to detect what themes and topics are prevalent in some set of e.g. customer feedback messages. Here, we used the topic clustering to establish what sort of themes were frequent in the hyperpartisan training set. We postulate that many metatopics turn out to become lightning rods for hyperpartisan argumen- tation, somewhat (but not entirely) unpredictably. A characteristic of some of the more extreme sam- ple items was that a hyperpartisan rant will bring in additional only marginally related topics into an argumentation. We identified a small …

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