Ravi Parikh’s “How to Lie with Data Visualization”

- General Comments

  • I’m surprised by the pie chart that doesn’t add up to 100%. This reminds me of some interesting chapters in “Storytelling with Data” by Cole Nussbaumer Knaflic about both 3D visualizations (how “warped” perspectives and focal points make some figure appear more relevant than they really are, and others less relevant) and pie charts (people tend to overweight different colors and slices in relation to one another, based on visual hierarchy, etc.).
  • It seems that deceptive visualizations are being used commonly in very large-scale information reporting systems (e.g., Fox News, sports, business communications), and so we might consider that they are incentivized if they can lead to particular outcomes. How can we de-incentivize such visualizations, especially since there do not appear to be any strong legal boundaries to their propogation? - Most Deceptive Techniques
  • Manipulating axes (y) to shape viewer’s opinions. There was a similar example recently with housing market pricing (I think in WSJ), but they tried to do the opposite (show that prices weren’t rising as much as they really were) by excessively inflating the y axis.
  • Cumulative graphs: they’ll naturally make it seem like your numbers are going up because that’s how positive number cumulations work. Every new number added to the chart includes its predecessor, so the numbers have to go up, which warps how we view, for example, revenue growth.

Anshul Vikram Pandey’s “How Deceptive Are Deceptive Visualizations?”

- General Comments- I’m particularly interested in the authors’ classifications of deceptions as either message reversal (“what”) or message exaggeration/understatement (“how much”). These seem very useful metrics when looking, also, at other forms of visual communications that might not be seeking to communicate or translate data through scale (e.g., memes, videos). These two designations also, essentially, tell us the subject and the scale of a subject’s value(s), which seems to be a framework that could be applied widely.

  • For their definition, I assume that “actual message” refers to the true numerical value or meaning, of data, rather than to a subjective interpretation of data: “[A deceptive visual is] a graphical depiction of information, designed with or without an intent to deceive, that may create a belief about the message and/or its components, which varies from the actual message”. - Most Deceptive Techniques
  • For me, the inverted axis is the most upsetting visual deception technique mentioned here, because it uses our implicit assumptions, which are needed for ease of data access and interpretability, against us. There are few things within our everyday visual calculations that we might assume more readily than “up = more” and “down = less”, for example; this has even made its way into human gesture and body language.