Abstract
Identifying controversial topics is not only interesting from a social point of view, it also enables the application of methods to avoid the information segregation, creating better discussion contexts and reaching agreements in the best cases. In this paper we develop a systematic method for controversy detection based primarily on the jargon used by the communities in social media. Our method dispenses with the use of domain-specific knowledge, is language-agnostic, efficient and easy to apply. We perform an extensive set of experiments across many languages, regions and contexts, taking controversial and non-controversial topics. We find that our vocabulary-based measure performs better than state of the art measures that are based only on the community graph structure. Moreover, we shows that it is possible to detect polarization through text analysis.
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Notes
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In physics, the electric dipole moment is a measure of the separation of positive and negative electrical charges within a system, that is, a measure of the system’s overall polarity.
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Q(G)=\(\sum _{C \in G}(e_{c}-a_{c})\), where G is the graph, C each of its communities, \(e_{c}\) the fraction of internal edges and \(a_{c}\) the fraction of edges in the border.
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Code and networks used in this work are available here: http://github.com/jmanuoz/Vocabulary-based-Method-for-Quantify-Controversy.
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This is a measure based on random walks over the graph structure.
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Where k is the number of classes and h the dimension of the text representation.
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We compare polynomial models of degree 1 to 5 and logmodel, linear model has the lowest RMSE error training with 10-fold cross-validation.
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Ortiz de Zarate, J.M., Feuerstein, E. (2020). Vocabulary-Based Method for Quantifying Controversy in Social Media. In: Alam, M., Braun, T., Yun, B. (eds) Ontologies and Concepts in Mind and Machine. ICCS 2020. Lecture Notes in Computer Science(), vol 12277. Springer, Cham. https://doi.org/10.1007/978-3-030-57855-8_12
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