Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

The Impact of Elicitation and Contrasting Narratives on Engagement, Recall and Attitude Change with News Articles Containing Data Visualization (2401.05511v1)

Published 10 Jan 2024 in cs.HC

Abstract: News articles containing data visualizations play an important role in informing the public on issues ranging from public health to politics. Recent research on the persuasive appeal of data visualizations suggests that prior attitudes can be notoriously difficult to change. Inspired by an NYT article, we designed two experiments to evaluate the impact of elicitation and contrasting narratives on attitude change, recall, and engagement. We hypothesized that eliciting prior beliefs leads to more elaborative thinking that ultimately results in higher attitude change, better recall, and engagement. Our findings revealed that visual elicitation leads to higher engagement in terms of feelings of surprise. While there is an overall attitude change across all experiment conditions, we did not observe a significant effect of belief elicitation on attitude change. With regard to recall error, while participants in the draw trend elicitation exhibited significantly lower recall error than participants in the categorize trend condition, we found no significant difference in recall error when comparing elicitation conditions to no elicitation. In a follow-up study, we added contrasting narratives with the purpose of making the main visualization (communicating data on the focal issue) appear strikingly different. Compared to the results of study 1, we found that contrasting narratives improved engagement in terms of surprise and interest but interestingly resulted in higher recall error and no significant change in attitude. We discuss the effects of elicitation and contrasting narratives in the context of topic involvement and the strengths of temporal trends encoded in the data visualization.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. K. Josh. You draw it: Just how bad is the drug overdose epidemic? https://www.nytimes.com/interactive/2017/04/14/upshot/drug-overdose-epidemic-you-draw-it.html.
  2. B. C. Wagner and R. E. Petty, “The elaboration likelihood model of persuasion: Thoughtful and non-thoughtful social influence,” Theories in social psychology, pp. 96–116, 2011.
  3. FiveThirtyEight. fivethirtyeight.com. https://fivethirtyeight.com.
  4. Upshot. the new york times. https://www.nytimes.com/section/upshot.
  5. D. Markant, M. Rogha, A. Karduni, R. Wesslen, and W. Dou, “When do data visualizations persuade? the impact of prior attitudes on learning about correlations from scatterplot visualizations,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, ser. CHI ’23.   New York, NY, USA: Association for Computing Machinery, 2023, p. 1–12.
  6. J. Heyer, N. K. Raveendranath, and K. Reda, “Pushing the (visual) narrative: The effects of prior knowledge elicitation in provocative topics,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, ser. CHI ’20.   New York, NY, USA: Association for Computing Machinery, 2020, p. 1–14.
  7. A. V. Pandey, A. Manivannan, O. Nov, M. Satterthwaite, and E. Bertini, “The persuasive power of data visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 12, pp. 2211–2220, 2014.
  8. J. Liem, C. Perin, and J. Wood, “Structure and empathy in visual data storytelling: Evaluating their influence on attitude,” Computer Graphics Forum, vol. 39, no. 3, pp. 277–289, 2020.
  9. A. Karduni, D. Markant, R. Wesslen, and W. Dou, “A bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 978–988, 2021.
  10. A. Sanborn and T. Griffiths, “Markov chain monte carlo with people,” in Advances in Neural Information Processing Systems, J. Platt, D. Koller, Y. Singer, and S. Roweis, Eds., vol. 20.   Curran Associates, Inc., 2007.
  11. A. N. Sanborn, T. L. Griffiths, and R. M. Shiffrin, “Uncovering mental representations with markov chain monte carlo,” Cognitive Psychology, vol. 60, no. 2, pp. 63–106, 2010.
  12. S. Mahajan, B. Chen, A. Karduni, Y.-S. Kim, and E. Wall, “Vibe: A design space for visual belief elicitation in data journalism,” Computer Graphics Forum, vol. 41, no. 3, pp. 477–488, 2022.
  13. R. Koonchanok, P. Baser, A. Sikharam, N. K. Raveendranath, and K. Reda, “Data prophecy: Exploring the effects of belief elicitation in visual analytics,” in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1–12.
  14. R. Koonchanok, G. Y. Tawde, G. R. Narayanasamy, S. Walimbe, and K. Reda, “Visual belief elicitation reduces the incidence of false discovery,” arXiv preprint arXiv:2301.12512, 2023.
  15. C. Xiong, C. Stokes, Y.-S. Kim, and S. Franconeri, “Seeing what you believe or believing what you see? belief biases correlation estimation,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 493–503, 2022.
  16. P. Mantri, H. Subramonyam, A. L. Michal, and C. Xiong, “How do viewers synthesize conflicting information from data visualizations?” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 1005–1015, 2023.
  17. Y.-S. Kim, K. Reinecke, and J. Hullman, “Data through others’ eyes: The impact of visualizing others’ expectations on visualization interpretation,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 760–769, 2018.
  18. Y.-S. Kim, L. A. Walls, P. Krafft, and J. Hullman, “A bayesian cognition approach to improve data visualization,” in Proceedings of the 2019 chi conference on human factors in computing systems, 2019, pp. 1–14.
  19. S. Gupta, A. Karduni, and E. Wall, “Belief decay or persistence? a mixed-method study on belief movement over time,” Computer Graphics Forum, vol. 40, no. 1, pp. 207–217, 2021.
  20. C. M. McColeman, L. Harrison, M. Feng, and S. Franconeri, “No mark is an island: Precision and category repulsion biases in data reproductions,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1063–1072, 2020.
  21. C. R. Ceja, C. M. McColeman, C. Xiong, and S. L. Franconeri, “Truth or square: Aspect ratio biases recall of position encodings,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1054–1062, 2021.
  22. Y.-S. Kim, K. Reinecke, and J. Hullman, “Explaining the gap: Visualizing one’s predictions improves recall and comprehension of data,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, ser. CHI ’17.   New York, NY, USA: Association for Computing Machinery, 2017, p. 1375–1386.
  23. D. Kirsh, “Thinking with external representations,” AI Soc., vol. 25, no. 4, p. 441–454, nov 2010.
  24. G. Brod, M. Hasselhorn, and S. A. Bunge, “When generating a prediction boosts learning: The element of surprise,” Learning and Instruction, vol. 55, pp. 22–31, 2018.
  25. L. K. Fazio and E. J. Marsh, “Surprising feedback improves later memory,” Psychonomic Bulletin & Review, vol. 16, no. 1, pp. 88–92, 2009.
  26. H. Ritchie and M. Roser. (1999) Drug Use. https://ourworldindata.org/drug-use.
  27. Q. V. Liao and W.-T. Fu, “Beyond the filter bubble: Interactive effects of perceived threat and topic involvement on selective exposure to information,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ser. CHI ’13.   New York, NY, USA: Association for Computing Machinery, 2013, p. 2359–2368.
  28. D. Christie and S. P. Neill, “8.09 - measuring and observing the ocean renewable energy resource,” in Comprehensive Renewable Energy (Second Edition), T. M. Letcher, Ed.   Oxford: Elsevier, 2022, pp. 149–175.
  29. H. L. O’Brien, P. Cairns, and M. Hall, “A practical approach to measuring user engagement with the refined user engagement scale (ues) and new ues short form,” International Journal of Human-Computer Studies, vol. 112, pp. 28–39, 2018.
  30. P.-C. Bürkner, “Brms: An R package for Bayesian multilevel models using Stan,” Journal of statistical software, vol. 80, pp. 1–28, 2017.
  31. A. Vehtari, A. Gelman, and J. Gabry, “Practical bayesian model evaluation using leave-one-out cross-validation and waic,” Statistics and computing, vol. 27, pp. 1413–1432, 2017.
  32. D. Makowski, M. S. Ben-Shachar, S. H. A. Chen, and D. Lüdecke, “Indices of Effect Existence and Significance in the Bayesian Framework,” Frontiers in Psychology, vol. 10, p. 2767, Dec. 2019.
  33. J. K. Kruschke and T. M. Liddell, “The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective,” Psychonomic Bulletin & Review, vol. 25, no. 1, pp. 178–206, Feb. 2018.
  34. P. J. Curran and A. M. Hussong, “Integrative data analysis: The simultaneous analysis of multiple data sets.” Psychological Methods, vol. 14, no. 2, pp. 81–100, 2009.
  35. A. Karduni, D. Markant, R. Wesslen, and W. Dou, “A bayesian cognition approach for belief updating of correlation judgement through uncertainty visualizations,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 978–988, 2020.
  36. P. Briñol and R. E. Petty, “Individual differences in attitude change,” The handbook of attitudes, pp. 575–615, 2005.
  37. G. Haddock and G. R. Maio, “Inter-individual differences in attitude content: Cognition, affect, and attitudes,” in Advances in experimental social psychology.   Elsevier, 2019, vol. 59, pp. 53–102.
  38. B. Wallace-Wells. Is there a case for legalizing heroin? https://www.newyorker.com/news/annals-of-populism/is-there-a-case-for-legalizing-heroin.
  39. R. E. Petty, M. A. Fleming, J. R. Priester, and A. H. Feinstein, “Individual versus group interest violation: Surprise as a determinant of argument scrutiny and persuasion,” Social Cognition, vol. 19, no. 4, pp. 418–442, 2001.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (5)
  1. Milad Rogha (3 papers)
  2. Subham Sah (2 papers)
  3. Alireza Karduni (12 papers)
  4. Douglas Markant (3 papers)
  5. Wenwen Dou (12 papers)
Citations (2)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets