Article published in:
Information Visualization
Edited by Marian Dörk and Isabel Meirelles
[Information Design Journal 25:1] 2019
► pp. 71–86

Feeling numbers

The emotional impact of proximity techniques in visualization

| MIT
| Northeastern University
For means of communication, persuasion is a natural and critical part of conveying a message. Data visualizations, being means of communication themselves, are used as rhetorical instruments, but how they persuade has yet to be fully understood. Based on George Campbell’s rhetorical theory, this paper presents the results of an empirical study testing the effectiveness of appeals to emotion through proximity techniques – the contextual framing of a visualization. The findings indicate that people feel greater interest towards a topic when the visualized data are more relevant to them, and that data representing events closer in time are more affecting.
Keywords: data visualization, rhetoric, pathos, emotion
Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 16 March 2020
https://doi.org/10.1075/idj.25.1.06cam

Full-text

Feeling numbers
References

References

Aristotle, Roberts, W. R., Bywater, I., & Solmsen, F.
(1954) Rhetoric. New York: Modern Library.Google Scholar
Bateman, S., Mandryk, R. L., Gutwin, C., Genest, A., McDine, D., & Brooks, C.
(2010) Useful junk?: The effects of visual embellishment on comprehension and memorability of charts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2573–2582). New York, NY, USA: ACM. CrossrefGoogle Scholar
Bateson, G.
(1972) Steps to an ecology of mind: Collected essays in anthropology, psychiatry, evolution, and epistemology. Chicago: University of Chicago Press.Google Scholar
Booth, W. C.
(1963) The rhetorical stance. College Composition and Communication, 14(3), 139–145. CrossrefGoogle Scholar
Borgo, R., Abdul-Rahman, A., Mohamed, F., Grant, P. W., Reppa, I., Floridi, L., & Chen, M.
(2012) An empirical study on using visual embellishments in visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2759–2768. CrossrefGoogle Scholar
Borkin, M. A., Bylinskii, Z., Kim, N. W., Bainbridge, C. M., Yeh, C. S., Borkin, D., … Oliva, A.
(2016) Beyond memorability: Visualization recognition and recall. IEEE Transactions on Visualization and Computer Graphics, 22(1), 519–528. CrossrefGoogle Scholar
Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H.
(2013) What makes a visualization memorable? IEEE Transactions on Visualization and Computer Graphics, 19(12), 2306–2315. CrossrefGoogle Scholar
Boy, J., Pandey, A. V., Emerson, J., Satterthwaite, M., Nov, O., & Bertini, E.
(2017) Showing people behind data: Does anthropomorphizing visualizations elicit more empathy for human rights data? In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 5462–5474). New York, NY, USA: ACM. CrossrefGoogle Scholar
Buchanan, R.
(1985) Declaration by design: Rhetoric, argument, and demonstration in design practice. Design Issues, 2(1), 4–22. CrossrefGoogle Scholar
Campbell, G.
(1776) The philosophy of rhetoric. (vol. 1). London: W. Strahan. Retrieved from http://​hdl​.handle​.net​/2027​/njp​.32101061812242
Dörk, M., Feng, P., Collins, C., & Carpendale, S.
(2013) Critical InfoVis: Exploring the politics of visualization. In CHI ’13 Extended Abstracts on Human Factors in Computing Systems (pp. 2189–2198). New York, NY, USA: ACM. CrossrefGoogle Scholar
Epstein, S.
(1994) Integration of the cognitive and the psychodynamic unconscious. American Psychologist, 49(8), 709–724. CrossrefGoogle Scholar
Funk, C., & Goo, S. K.
(2015) A look at what the public knows and does not know about science (p. 40). Pew Research Center.Google Scholar
Haushofer, K.
(1928) Die suggestive Karte. In K. Haushofer, E. Obst, H. Lautensach, & O. Maull (Eds.), Bausteine zur Geopolitik (pp. 343–348). Leipzig: Kurt Vowinckel Verlag.Google Scholar
Harley, J. B.
(1991) Can there be a cartographic ethics? Cartographic Perspectives, (10), 9–16. CrossrefGoogle Scholar
Haroz, S., Kosara, R., & Franconeri, S. L.
(2015) ISOTYPE visualization: Working memory, performance, and engagement with pictographs. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1191–1200). New York, NY, USA: ACM. CrossrefGoogle Scholar
Hoeken, H.
(2001) Anecdotal, statistical, and causal evidence: Their perceived and actual persuasiveness. Argumentation, 15(4), 425–437. CrossrefGoogle Scholar
Holmes, N.
(1984) Designer’s guide to creating charts and diagrams (2nd ed.). New York: Watson-Guptill.Google Scholar
Huff, D.
(1954) How to lie with statistics. New York: Norton.Google Scholar
Hullman, J., & Diakopoulos, N.
(2011) Visualization rhetoric: framing effects in narrative visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2231–2240. CrossrefGoogle Scholar
Johnson, B. T., & Eagly, A. H.
(1989) Effects of involvement on persuasion: A meta-analysis. Psychological Bulletin, 106(2), 290–314. CrossrefGoogle Scholar
Jones, G. E.
(2011) How to lie with charts. LaPuerta Books and Media.Google Scholar
Kogut, T., & Ritov, I.
(2005) The singularity effect of identified victims in separate and joint evaluations. Organizational Behavior and Human Decision Processes, 97(2), 106–116. CrossrefGoogle Scholar
Kostelnick, C.
(2016) The re-emergence of emotional appeals in interactive data visualization. Technical Communication, 63(2), 116–135.Google Scholar
Lee, S., Kim, S.-H., & Kwon, B. C.
(2017) VLAT: Development of a visualization literacy assessment test. IEEE Transactions on Visualization and Computer Graphics, 23(1), 551–560. CrossrefGoogle Scholar
Lee, S., Winterich, K. P., & Ross Jr., W. T.
(2014) I’m moral, but I won’t help you: The distinct roles of empathy and justice in donations. Journal of Consumer Research, 41(3), 678–696. CrossrefGoogle Scholar
Lumley, T., Diehr, P., Emerson, S., & Chen, L.
(2002) The importance of the normality assumption in large public health data sets. Annual Review of Public Health, 23(1), 151–169. CrossrefGoogle Scholar
Monmonier, M. S.
(1996) How to lie with maps. Chicago: University of Chicago Press. CrossrefGoogle Scholar
Muehlenhaus, I.
(2010) Lost in visualization: Using quantitative content analysis to identify, measure, and categorize political cartographic manipulations (Ph.D.). University of Minnesota, United States, Minnesota. Retrieved from https://​search​-proquest​-com​.ezproxy​.neu​.edu​/docview​/305207782​/abstract​/73A8DC04E3AD4FC2PQ​/1
(2012) If looks could kill: The impact of different rhetorical styles on persuasive geocommunication. The Cartographic Journal, 49, 361–375. CrossrefGoogle Scholar
(2013) The design and composition of persuasive maps. Cartography and Geographic Information Science, 40(5), 401–414. CrossrefGoogle Scholar
Offenhuber, D.
(2010) Visual anecdote. Leonardo, 43(4), 367–374. CrossrefGoogle Scholar
Pandey, A., Manivannan, A., Nov, O., Satterthwaite, M. L., & Bertini, E.
(2014) The persuasive power of data visualization. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2211–2220. CrossrefGoogle Scholar
Pandey, A., Rall, K., Satterthwaite, M. L., Nov, O., & Bertini, E.
(2015) How deceptive are deceptive visualizations? An empirical analysis of common distortion techniques. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 1469–1478). New York, NY, USA: ACM. CrossrefGoogle Scholar
Riche, N. H., Hurter, C., Diakopoulos, N., & Carpendale, S.
(Eds.) (2018) Data-driven atorytelling. Boca Raton, Florida: A K Peters/CRC Press. CrossrefGoogle Scholar
Scherer, K. R.
(2001) Emotions, psychological structure of. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social & Behavioral Sciences (pp. 4472–4477). Oxford: Pergamon. CrossrefGoogle Scholar
(2005) What are emotions? And how can they be measured? Social Science Information, 44(4), 695–729. CrossrefGoogle Scholar
Segel, E., & Heer, J.
(2010) Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139–1148. CrossrefGoogle Scholar
Shelter Animals Count. The National Database Project
(2017) Retrieved from https://​www​.shelteranimalscount​.org
Slovic, P.
(2007) “If I look at the mass I will never act”: Psychic numbing and genocide. Judgment and Decision Making, 2(2), 79–95.Google Scholar
Small, D. A., Loewenstein, G., & Slovic, P.
(2007) Sympathy and callousness: The impact of deliberative thought on donations to identifiable and statistical victims. Organizational Behavior and Human Decision Processes, 102(2), 143–153. CrossrefGoogle Scholar
Tactical Technology Collective
(2014) Visualising Information for Advocacy (2nd ed.). Retrieved from http://​visualisingadvocacy​.org
Thudt, A., Perin, C., Willett, W., & Carpendale, S.
(2017) Subjectivity in Personal storytelling with visualization. Information Design Journal, 23(1): 48–64. CrossrefGoogle Scholar
Tufte, E. R.
(1983) The visual display of quantitative information. Cheshire, CT: Graphics Press.Google Scholar
Tyner, J. A.
(1982) Persuasive cartography. Journal of Geography, 81, 140–144. CrossrefGoogle Scholar
Vande Moere, A., Tomitsch, M., Wimmer, C., Christoph, B., & Grechenig, T.
(2012) evaluating the effect of style in information visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2739–2748. CrossrefGoogle Scholar
Västfjäll, D., Slovic, P., Mayorga, M., & Peters, E.
(2014) Compassion fade: Affect and charity are greatest for a single child in need. PLoS ONE, 9(6). CrossrefGoogle Scholar
Viegas, F. B., & Wattenberg, M.
(2007) Artistic data visualization: Beyond visual analytics. Lecture Notes in Computer Science, (4564), 182–191. CrossrefGoogle Scholar
Watzlawick, P., Bavelas, J. B., & Jackson, D. D.
(1967) Pragmatics of human communication. W. W. Norton & Company.Google Scholar
Wood, D.
(1991) How maps work. Cartographica, 29(3/4), 66–74.Google Scholar
Cited by

Cited by 3 other publications

Alieva, Iuliia
2021. How American Media Framed 2016 Presidential Election Using Data Visualization: The Case Study of the New York Times and the Washington Post. Journalism Practice ► pp. 1 ff. Crossref logo
Morais, Luiz, Yvonne Jansen, Nazareno Andrade & Pierre Dragicevic
2021.  In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, ► pp. 1 ff. Crossref logo
Gabriella Murta, Claudia Mont'Alvão & Doris Kosminsky
2020.  In Blucher Design Proceedings, ► pp. 1600 ff. Crossref logo

This list is based on CrossRef data as of 26 june 2021. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.