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Public Sphere 2.0: Targeted Commenting in Online News Media

  • Ankan MullickEmail author
  • Sayan GhoshEmail author
  • Ritam DuttEmail author
  • Avijit GhoshEmail author
  • Abhijnan ChakrabortyEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)

Abstract

With the increase in online news consumption, to maximize advertisement revenue, news media websites try to attract and retain their readers on their sites. One of the most effective tools for reader engagement is commenting, where news readers post their views as comments against the news articles. Traditionally, it has been assumed that the comments are mostly made against the full article. In this work, we show that present commenting landscape is far from this assumption. Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article. In this paper, we build a system which can automatically classify comments against relevant sections of an article. To implement that, we develop a deep neural network based mechanism to find comments relevant to any section and a paragraph wise commenting interface to showcase them. We believe that such a data driven commenting system can help news websites to further increase reader engagement.

References

  1. 1.
    Park, D., Sachar, S., Diakopoulos, N., Elmqvist, N.: Supporting comment moderators in identifying high quality online news comments. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 1114–1125. ACM (2016)Google Scholar
  2. 2.
    Habermas, J.: Moral Consciousness and Communicative Action. MIT press, Cambridge (1990)Google Scholar
  3. 3.
    Ruiz, C., Domingo, D., Micó, J.L., Díaz-Noci, J., Meso, K., Masip, P.: Public sphere 2.0? The democratic qualities of citizen debates in online newspapers. Int. J. Press/Politics 16(4), 463–487 (2011)CrossRefGoogle Scholar
  4. 4.
    Nielsen, J.: Usability 101: Introduction to usability (2003)Google Scholar
  5. 5.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)CrossRefGoogle Scholar
  6. 6.
    Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The stanford CoreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60 (2014)Google Scholar
  7. 7.
    De Marneffe, M.C., Manning, C.D.: Stanford typed dependencies manual. Technical report, Technical report, Stanford University (2008)Google Scholar
  8. 8.
    Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)CrossRefGoogle Scholar
  9. 9.
    Hsu, C.F., Khabiri, E., Caverlee, J.: Ranking comments on the social web. In: International Conference on Computational Science and Engineering, CSE 2009, vol. 4, pp. 90–97. IEEE (2009)Google Scholar
  10. 10.
    Dalal, O., Sengemedu, S.H., Sanyal, S.: Multi-objective ranking of comments on web. In: Proceedings of the 21st International Conference on World Wide Web, pp. 419–428. ACM (2012)Google Scholar
  11. 11.
    Bansal, T., Das, M., Bhattacharyya, C.: Content driven user profiling for comment-worthy recommendations of news and blog articles. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 195–202. ACM (2015)Google Scholar
  12. 12.
    Shmueli, E., Kagian, A., Koren, Y., Lempel, R.: Care to comment?: recommendations for commenting on news stories. In: Proceedings of the 21st International Conference on World Wide Web, pp. 429–438. ACM (2012)Google Scholar
  13. 13.
    Agarwal, D., Chen, B.C., Pang, B.: Personalized recommendation of user comments via factor models. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 571–582. Association for Computational Linguistics (2011)Google Scholar
  14. 14.
    Liu, X.: Comment centric news analysis for ranking. Proc. Am. Soc. Inf. Sci. Technol. 46(1), 1–8 (2009)Google Scholar
  15. 15.
    Stroud, N.J., Van Duyn, E., Peacock, C.: News commenters and news comment readers. Engaging News Project (2016)Google Scholar
  16. 16.
    Chakraborty, A., Sarkar, R., Mrigen, A., Ganguly, N.: Tabloids in the era of social media? Understanding the production and consumption of clickbaits in Twitter. arXiv preprint arXiv:1709.02957 (2017)
  17. 17.
    Chakraborty, A., Messias, J., Benevenuto, F., Ghosh, S., Ganguly, N., Gummadi, K.P.: Who makes trends? Understanding demographic biases in crowdsourced recommendations. arXiv preprint arXiv:1704.00139 (2017)
  18. 18.
    Mullick, A., Maheshwari, S., Goyal, P., Ganguly, N., et al.: A generic opinion-fact classifier with application in understanding opinionatedness in various news section. In: Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, pp. 827–828 (2017)Google Scholar
  19. 19.
    Mullick, A., Ghosh D.S., Maheswari, S., Sahoo, S., Maity, S.K., Goyal, P., et al.: Identifying opinion and fact subcategories from the social web. In: Proceedings of the 2018 ACM Conference on Supporting Groupwork, pp. 145–149. ACM (2018)Google Scholar
  20. 20.
    Mullick, A., Goyal, P., Ganguly, N.: A graphical framework to detect and categorize diverse opinions from online news. In: Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pp. 40–49 (2016)Google Scholar
  21. 21.
    Almgren, S.M., Olsson, T.: Commenting, sharing and tweeting news. Nordicom Rev. 37(2), 67–81 (2016)CrossRefGoogle Scholar
  22. 22.
    Chakraborty, A., Ghosh, S., Ganguly, N., Gummadi, K.P.: Optimizing the recency-relevancy trade-off in online news recommendations. In: Proceedings of the 26th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 837–846 (2017)Google Scholar
  23. 23.
    Chakraborty, A., Patro, G.K., Ganguly, N., Gummadi, K.P., Loiseau, P.: Equality of voice: towards fair representation in crowdsourced top-k recommendations. In: ACM FAT* (2019)Google Scholar
  24. 24.
    Mullick, A., et al.: Drift in online social media. In: IEEE IEMCON, pp. 302–307, November 2018Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.MicrosoftHyderabadIndia
  2. 2.Indian Institute of Technology KharagpurKharagpurIndia
  3. 3.Max Planck Institute for Software SystemsSaarbrückenGermany

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