Commentaries

Every reading gets a short commentary, due at noon on the day of its lecture. A late submission is recorded as late, and the newest submission for a reading is the one that counts. First time? Read how to write a commentary below. When you submit, your commentary is checked against those guidelines and you’ll see which ones it meets. To see what the rest of the class argued, read the commentary themes.

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Commentary themes

What the class argued in its commentaries on each reading, grouped into themes with a few quotes for each, as the course staff post them. The quotes are anonymous: no names or initials. Reading them takes the same Stanford sign-in as submitting.

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How to write a commentary

The goal of paper commentaries are to get you to think critically about the research that a paper presents and why that research is important. You will write brief commentary reflections—around three to four paragraphs—for each reading in the course.

Writing a strong commentary means grappling with the core ideas being presented in the reading. The goal is not to summarize the paper — everyone reading your commentary will have already read the paper!

Suggested strategies for writing a commentary:

  • Don’t
    • Nitpick low-level details
    • Harp on already-acknowledged limitations or future work
    • Bring expectations from other HCI paper genres (“needs a user study!”)
    • Ask more of a paper than its claim requires. Many papers we read claim that something is possible and worth doing, not that it works for everyone, everywhere, for years. “Small sample,” “only four weeks,” and “only tested in a lab” are usually limits the authors already name. If a limit matters, say which assumption in the design it puts at risk and what you would build next.
    • Treat “nobody uses this today” as a verdict. A paper that shows an idea is possible and desirable is not promising it will be adopted; economics, last-mile product work, and timing all keep good ideas out of wide use. If the absence interests you, say what it reveals about the idea.
    • Stop at “companies will never build this.” Business models explain whether an idea gets adopted, not whether it is a good idea. If you raise them, say what they mean for the design: what would have to change, or what version of the idea survives them.
    • Spend too much time summarizing
    • Levy judgment (“I like this!”) without digging into the why or the implications of that agreement or disagreement
  • Do: engage with the core contributions. To achieve this, we suggest the following three step process:
    • Step 1 (Reflection): Ask yourself, what is the point that this paper is trying to make? (You don’t need to write out the answer to this question in your commentary, but you do need to know the answer in order to write a good commentary.) State the main point briefly, but then reflect on why the ideas in the reading made sense from the authors’ perspectives.
    • Step 2 (Synthesis): How effectively does it convince you of that argument? How could the argument be even more persuasive, on its own terms?
    • Step 3 (Future work): What are the implications of the argument? Given the ideas presented in the paper, what would you want to work on, or how would you modify those ideas?
  • Common issues leading to low-scoring commentaries:
    • Step 1(Reflection): We get lots of commentaries that are mostly summary. Don’t stop here. We’ve all read the paper. 1–2 sentences max.
    • Step 2 (Synthesis): It’s easy to just lob criticisms and negativity. Too many commentaries are just lists of complaints. Instead, focus on: what’s at the core of this idea, and why is it holding sway? What might be a better version of this idea, if you’re unconvinced?
    • Step 3 (Future work): Too few commentaries cover this! Instead, ask yourself: what are applications of these ideas, and what follow-up ideas might be worth exploring?

Commentaries are not graded on a scale. They are a completion requirement: submit one for every reading by its deadline, noon on the day of the lecture. Missing commentaries drop that week’s tutorial grade one step. Your course assistant reads them before your tutorial and uses them to shape the discussion, and you should expect follow-up questions on what you wrote. The qualities below are what makes a commentary useful in that room.

An example commentary

Written by Selaine Rodriguez for BubbleView (Kim et al., 2017):

I enjoyed learning about how the researchers used different approaches and compared-and-contrasted them in order to see how various tools categorized the importance of different graphical sections. I was most interested by the difference between Ground Truth BubbleView clicks and Predicted Importance projections. Specifically, I thought it was interesting that many of these graphics were magazine-esque layouts, with background images that projected the theme, but often didn’t have super specific information, and large amounts of text in various sizes, colors, and placements. With BubbleView, it seemed that lots of people would click on the text, as well as these background images, but Predicted Importance often thought that the background image wasn’t too important. I though this finding reiterated how important it is to choose background images that are intentional, and not just filler images to make the page important. If people’s attention goes there more than we’d expect, it’s crucial to spend time choosing images that accentuate your points rather than distract the reader.

One experience this reminded me of is other AI tools and algorithms that I have seen on social media sites, specifically those that try to decide the important areas of an image that should be showcased when cropped. Specifically, I remember similar cropping / importance algorithms being used on Twitter / X. However, these tools turned out to be extremely problematic, often cropping the images to focus on those who are white, thin, and female. Many articles dived into understanding this, and users themselves tested by adding photos with underrepresented populations vs. more privileged groups and saw the ethical issues themselves. Many tech people wrote articles (ex. Twitter’s Photo-Cropping Algorithm Favors Young, Thin Females [Links to an external site.]), as well as Twitter themselves (Sharing learnings about our image cropping algorithm [Links to an external site.]). The biases present in this algorithm were deeply connected to general biases in AI, and I would have loved to see the article dive into that possibility more. They do not touch on any biases in their training data, or any edge cases they see that might need further exploration.

Overall, I felt that future work for this article relies on more than just making the tool open source for people to explore themselves. Certain fonts, text patterns, and images grabbed the attention of the user greatly, and a guide to those recommendations would make these learnings even more applicable to the average designer. Although these guides of design tips may exist now, I am sure that at the time of the release of this article, these suggestions would have really changed how people presented media online.