Stay on Topic, Please: Aligning User Comments to the Content of a News Article

Author:

Alshehri JumanahORCID,Stanojevic MarijaORCID,Dragut EduardORCID,Obradovic ZoranORCID

Publisher

Springer International Publishing

Reference38 articles.

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2. Bastos, M., Mercea, D.: Parametrizing Brexit: mapping Twitter political space to parliamentary constituencies. Inf. Commun. Soc. 21(7), 921–939 (2018). https://doi.org/10.1080/1369118X.2018.1433224

3. Celli, F., Stepanov, E.A., Poesio, M., Riccardi, G.: Predicting Brexit: classifying agreement is better than sentiment and pollsters. In: Nissim, M., Patti, V., Plank, B. (eds.) Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, PEOPLES@COLING 2016, Osaka, Japan, 12 December 2016, pp. 110–118. The COLING 2016 Organizing Committee (2016). https://www.aclweb.org/anthology/W16-4312/

4. Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440–8451. Association for Computational Linguistics (July 2020). https://doi.org/10.18653/v1/2020.acl-main.747. https://www.aclweb.org/anthology/2020.acl-main.747

5. Das, M.K., Bansal, T., Bhattacharyya, C.: Going beyond Corr-LDA for detecting specific comments on news & blogs. In: Carterette, B., Diaz, F., Castillo, C., Metzler, D. (eds.) 7th ACM International Conference on Web Search and Data Mining, WSDM 2014, New York, NY, USA, 24–28 February 2014, pp. 483–492. ACM (2014). https://doi.org/10.1145/2556195.2556231

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