Abstract
Discussions in the comment sections of news articles are often characterized by highly one-sided arguments. One reason for this is that users primarily consume information that fit into their worldview and consequently argue from their echo chamber when responding to a comment. In this paper we introduce a new approach to comment recommendation. To this end, we present a model that makes recommendations based on a specific comment the user is interested in. These recommendations provide the user with alternative related comments and thereby broaden the perspective of the user before reacting to the specific comment that was originally selected. This is in contrast to previous work that tries to recommend comments based on the interests and previous behavior of the user and therefore fueling the filter bubble by providing information that fit into the worldview of the user.
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Notes
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- 2.
- 3.
https://www.kaggle.com/aashita/nyt-comments accessed 09/21/2021.
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https://www.kaggle.com/aashita/nyt-comments accessed 09/21/2021
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See footnote 4
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Range: \(-1\) (perfect disagreement) to 1 (perfect agreement).
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Range: 0 (perfect agreement) to 10 (perfect disagreement).
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Acknowledgment
We would like to thank Markus Brenneis and Björn Ebbinghaus for their help with the annotations.
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Steimann, J., Feger, M., Mauve, M. (2022). Inspiring Heterogeneous Perspectives in News Media Comment Sections. In: Yamamoto, S., Mori, H. (eds) Human Interface and the Management of Information: Visual and Information Design. HCII 2022. Lecture Notes in Computer Science, vol 13305. Springer, Cham. https://doi.org/10.1007/978-3-031-06424-1_10
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