skip to main content
10.1145/3411763.3451815acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
poster
Public Access

Making Sense of Online Discussions: Can Automated Reports help?

Published:08 May 2021Publication History

ABSTRACT

Enabling healthier online deliberation around issues of public concerns is an increasingly vital challenge in nowadays society. Two fundamental components of a healthier deliberation are: i. the capability of people to make sense of what they read, so that their contribution can be relevant; and ii. the improvement of the overall quality of the debate, so that noise can be reduced and useful signals can inform collective decision making. Platform designers often resort to computational aids to improve these two processes. In this paper, we examine automated reporting as promising mean of improving sensemaking in discussion platforms. We compared three approaches to automated reporting: an abstractive summariser, a template report and an argumentation highlighting system. We then evaluated improvements in sensemaking of participants and the perception on overall quality of the debate. The study suggests that argument mining technologies are particularly promising computational aids to improve sense making and perceived quality of online discussion, thanks to their capability to combine computational models for automated reasoning with users’ cognitive needs and expectation of automated reporting.

References

  1. Tanja Aitamurto, Mike Ananny, Chris W Anderson, Larry Birnbaum, Nicholas Diakopoulos, Matilda Hanson, Jessica Hullman, and Nick Ritchie. 2019. HCI for accurate, impartial and transparent journalism: Challenges and solutions. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems. 1–8.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Kholod Alsufiani, Simon Attfield, and Leishi Zhang. 2017. Towards an instrument for measuring sensemaking and an assessment of its theoretical features. In Proceedings of the 31st British Computer Society Human Computer Interaction Conference. BCS Learning and Development Ltd., 86.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Jordan Beck, Bikalpa Neupane, and John M Carroll. 2018. Managing Conflict in Online Debate Communities: Foregrounding Moderators’ Beliefs and Values on Kialo. (2018).Google ScholarGoogle Scholar
  4. Elena Cabrio and Serena Villata. 2018. Five Years of Argument Mining: a Data-driven Analysis.. In IJCAI, Vol. 18. 5427–5433.Google ScholarGoogle Scholar
  5. Eshwar Chandrasekharan, Chaitrali Gandhi, Matthew Wortley Mustelier, and Eric Gilbert. 2019. Crossmod: A cross-community learning-based system to assist reddit moderators. Proceedings of the ACM on human-computer interaction 3, CSCW(2019), 1–30.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Quanze Chen, Jonathan Bragg, Lydia B Chilton, and Dan S Weld. 2019. Cicero: Multi-turn, contextual argumentation for accurate crowdsourcing. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. John W Creswell and J David Creswell. 2017. Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.Google ScholarGoogle Scholar
  8. Anna De Liddo and Simon Buckingham Shum. 2013. Improving online deliberation with argument network visualization. (2013).Google ScholarGoogle Scholar
  9. Anna De Liddo, Nieves Pedreira Souto, and Brian Plüss. 2020. Let’s replay the political debate: Hypervideo technology for visual sensemaking of televised election debates. International Journal of Human-Computer Studies 145 (2020), 102537.Google ScholarGoogle ScholarCross RefCross Ref
  10. Leon Derczynski and Kalina Bontcheva. 2014. Pheme: Veracity in Digital Social Networks.. In UMAP workshops.Google ScholarGoogle Scholar
  11. Alexis Dinno. 2015. Nonparametric pairwise multiple comparisons in independent groups using Dunn’s test. The Stata Journal 15, 1 (2015), 292–300.Google ScholarGoogle ScholarCross RefCross Ref
  12. Pinar Dönmez, Carolyn Rosé, Karsten Stegmann, Armin Weinberger, and Frank Fischer. 2005. Supporting CSCL with automatic corpus analysis technology. In International Conference on Computer Supported Collaborative. 125–134.Google ScholarGoogle ScholarCross RefCross Ref
  13. Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2020. GSum: A General Framework for Guided Neural Abstractive Summarization. arXiv preprint arXiv:2010.08014(2020).Google ScholarGoogle Scholar
  14. John Dowell, Michael Tscholl, Thomas Gladisch, and Marzieh Asgari-Targhi. 2009. Argumentation scheme and shared online diagramming in case-based collaborative learning. (2009).Google ScholarGoogle Scholar
  15. Cédric Gossart. 2014. Can digital technologies threaten democracy by creating information cocoons?In Transforming politics and policy in the digital age. IGI Global, 145–154.Google ScholarGoogle Scholar
  16. Todd Graham and Tamara Witschge. 2003. In search of online deliberation: Towards a new method for examining the quality of online discussions. COMMUNICATIONS-SANKT AUGUSTIN THEN BERLIN- 28, 2 (2003), 173–204.Google ScholarGoogle Scholar
  17. Sharath Chandra Guntuku, David B Yaden, Margaret L Kern, Lyle H Ungar, and Johannes C Eichstaedt. 2017. Detecting depression and mental illness on social media: an integrative review. Current Opinion in Behavioral Sciences 18 (2017), 43–49.Google ScholarGoogle ScholarCross RefCross Ref
  18. Ivan Habernal and Iryna Gurevych. 2017. Argumentation mining in user-generated web discourse. Computational Linguistics 43, 1 (2017), 125–179.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Mijail Kabadjov, Josef Steinberger, Emma Barker, Udo Kruschwitz, and Massimo Poesio. 2015. Onforums: The shared task on online forum summarisation at multiling’15. In Proceedings of the 7th forum for information retrieval evaluation. 21–26.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Rune Karlsen, Kari Steen-Johnsen, Dag Wollebæk, and Bernard Enjolras. 2017. Echo chamber and trench warfare dynamics in online debates. European Journal of Communication 32, 3 (2017), 257–273.Google ScholarGoogle ScholarCross RefCross Ref
  21. Robin H Kay. 2006. Developing a comprehensive metric for assessing discussion board effectiveness. British Journal of Educational Technology 37, 5 (2006), 761–783.Google ScholarGoogle ScholarCross RefCross Ref
  22. Sunyoung Kim and Eric Paulos. 2012. A subscription-based authoring tool for mobile citizen science campaigns. In CHI’12 Extended Abstracts on Human Factors in Computing Systems. 2135–2140.Google ScholarGoogle Scholar
  23. Sung-Chul Lee, Jaeyoon Song, Eun-Young Ko, Seongho Park, Jihee Kim, and Juho Kim. 2020. SolutionChat: Real-time Moderator Support for Chat-based Structured Discussion. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 1–12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Tianyi Li, Kurt Luther, and Chris North. 2018. Crowdia: Solving mysteries with crowdsourced sensemaking. Proceedings of the ACM on Human-Computer Interaction 2, CSCW(2018), 1–29.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Raymond Liaw, Ari Zilnik, Mark Baldwin, and Stephanie Butler. 2013. Maater: Crowdsourcing to improve online journalism. In CHI’13 Extended Abstracts on Human Factors in Computing Systems. 2549–2554.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Marco Lippi and Paolo Torroni. 2016. MARGOT: A web server for argumentation mining. Expert Systems with Applications 65 (2016), 292–303.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Cindy-Pamela Lopez, Marco Segura, and Marco Santórum. 2019. Data Analytics and BI Framework based on Collective Intelligence and the Industry 4.0. In Proceedings of the 2019 2nd International Conference on Information Science and Systems. 93–98.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Elijah Mayfield and Alan W Black. 2019. Analyzing Wikipedia Deletion Debates with a Group Decision-Making Forecast Model. Proceedings of the ACM on Human-Computer Interaction 3, CSCW(2019), 1–26.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Constantin Orasan, Ruslan Mitkov, and Laura Hasler. 2003. CAST: a computer-aided summarisation tool. In 10th Conference of the European Chapter of the Association for Computational Linguistics.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Kasey Panetta. 2017 (accessed January 3, 2021). Deep learning and natural-language generation will become standards in analytics. https://www.gartner.com/smarterwithgartner/nueral-networks-and-modern-bi-platforms-will-evolve-data-and-analytics/.Google ScholarGoogle Scholar
  31. Peter Pirolli and Stuart Card. 2005. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proceedings of international conference on intelligence analysis, Vol. 5. McLean, VA, USA, 2–4.Google ScholarGoogle Scholar
  32. Abigail See, Peter J Liu, and Christopher D Manning. 2017. Get to the point: Summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368(2017).Google ScholarGoogle Scholar
  33. Simon Buckingham Shum and Nick Hammond. 1994. Argumentation-based design rationale: what use at what cost?International journal of human-computer studies 40, 4 (1994), 603–652.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. H Steigler and M De Jong. 2015. Facilitating personal deliberation online: Effects of two consider. it variations. Computers in Human Behaviour 51 (2015), 461–469.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Cass R Sunstein. 2018. # Republic: Divided democracy in the age of social media. Princeton University Press.Google ScholarGoogle Scholar
  36. Sunny Tian. 2020. Integrating Discussion and Summarization in Collaborative Writing. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems. 1–6.Google ScholarGoogle Scholar
  37. Ramine Tinati, Max Van Kleek, Elena Simperl, Markus Luczak-Rösch, Robert Simpson, and Nigel Shadbolt. 2015. Designing for citizen data analysis: A cross-sectional case study of a multi-domain citizen science platform. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. 4069–4078.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Daniela Tsaneva and Jianhua Shao. 2018. Assisting investors with collective intelligence. In Proceedings of the First International Conference on Data Science, E-learning and Information Systems. 1–6.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. András Vargha and Harold D Delaney. 1998. The Kruskal-Wallis test and stochastic homogeneity. Journal of Educational and behavioral Statistics 23, 2(1998), 170–192.Google ScholarGoogle ScholarCross RefCross Ref
  40. Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020. Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training. arXiv preprint arXiv:2001.04063(2020).Google ScholarGoogle Scholar
  41. Amy X Zhang and Justin Cranshaw. 2018. Making sense of group chat through collaborative tagging and summarization. Proceedings of the ACM on Human-Computer Interaction 2, CSCW(2018), 1–27.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Amy X Zhang, Lea Verou, and David Karger. 2017. Wikum: Bridging discussion forums and wikis using recursive summarization. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. 2082–2096.Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020. Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In International Conference on Machine Learning. PMLR, 11328–11339.Google ScholarGoogle Scholar

Index Terms

  1. Making Sense of Online Discussions: Can Automated Reports help?
            Index terms have been assigned to the content through auto-classification.

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              CHI EA '21: Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
              May 2021
              2965 pages
              ISBN:9781450380959
              DOI:10.1145/3411763

              Copyright © 2021 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 8 May 2021

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • poster
              • Research
              • Refereed limited

              Acceptance Rates

              Overall Acceptance Rate6,164of23,696submissions,26%

              Upcoming Conference

              CHI '24
              CHI Conference on Human Factors in Computing Systems
              May 11 - 16, 2024
              Honolulu , HI , USA

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader

            HTML Format

            View this article in HTML Format .

            View HTML Format