Chapter 20 Exercises

Exercise 20.1 — Profile Analysis: Self-Presentation Strategies in Practice

Type: Observation and analysis | Time: 60–90 minutes | Format: Written response (500–700 words)

Overview

Goffman's dramaturgical model holds that self-presentation is strategic performance. Dating app profiles are unusually pure instances of this: unlike a job interview or a party, the profile is constructed entirely by the self-presenter, with no real-time feedback, for an unknown audience. This exercise asks you to apply the framework analytically.

Instructions

Using a dating app of your choice (or a sample profile dataset your instructor provides if you prefer not to use a live app), examine five profiles. For each profile:

  1. Identity claims: What specific claims does the profile make about the person's identity — their values, interests, ambitions, social position? Are these claims explicit ("I love hiking") or implicit (a photo at a marathon)?

  2. Self-enhancement vs. authenticity signals: Where does the profile seem to lean into idealization? Where does it seem deliberately unpolished or vulnerable? How does the tension between "attractive" and "authentic" show up?

  3. Audience targeting: Who does this profile seem to be speaking to? What assumptions does it seem to make about who will see it — in terms of age, education, relationship goal, cultural background?

  4. What's absent: What is conspicuously not in the profile? What social categories (race, body size, disability, religion) are visible or invisible, and how?

Reflection prompt (required): Across the five profiles, what patterns do you notice? What does the genre of "dating app profile" seem to require — what can't you say, what must you perform, what is punished by the format?

Exercise 20.2 — Python Exploration: The Swipe Right Dataset

Type: Computational analysis | Time: 45–90 minutes | Format: Code + written interpretation (300–500 words)

Prerequisites: Python 3, pandas, seaborn, matplotlib, scipy installed

Setup

Run swipe_right_explorer.py (located in the code/ directory) to generate the Swipe Right Dataset and its three visualizations. Read the inline comments carefully — they explain the distributional choices and cite the literature.

Tasks

Once the dataset (swipe_right_dataset.csv) is generated, use pandas to complete the following analyses:

  1. Match rate by education level: Calculate mean match_rate for each education category. Does higher education correlate with higher match rate? What might explain any patterns you find?

  2. Location effects: Compare match_rate and dates_per_month across Urban, Suburban, and Rural location types. Why would you expect location to matter? Do the synthetic data reflect your expectations?

  3. Satisfaction paradox: Create a scatter plot of match_rate vs. reported_satisfaction. Is the relationship as strong as you would expect? What might explain any cases where high match rates don't translate to high satisfaction?

  4. Sexuality and response rate: Compare message_response_rate across sexuality categories. Note any patterns and consider what explanations the literature might offer.

Stretch task (optional): Run a simple OLS regression predicting reported_satisfaction from match_rate, dates_per_month, and months_on_platform. Interpret the coefficients. What predicts satisfaction better — matches or dates?

Written response: In 300–500 words, describe your most interesting finding from the dataset and connect it to at least one concept from the chapter. Remember to note the synthetic nature of the data when interpreting results.

Exercise 20.3 — Ethical Analysis: A Dating App Feature Under the Microscope

Type: Applied ethics analysis | Time: 45–60 minutes | Format: Structured written analysis (500–700 words)

Overview

This exercise asks you to apply an ethical framework — of your choice — to a specific dating app design feature.

Choose one feature to analyze:

  • The Tinder "superlike" (or Hinge "rose")
  • Bumble's "women message first" rule in heterosexual matching
  • Any premium subscription feature that shows who has already liked you (Tinder Gold, Bumble's Beeline)
  • The swipe-per-day limit and the "boost" that temporarily increases profile visibility
  • The lack of robust identity verification on most platforms

For your chosen feature, address:

  1. What behavior does this feature design for? What does it incentivize, and for whom?

  2. Who benefits and who is harmed? Consider benefits and harms across different user groups — by gender, by relationship goal, by subscription tier, by desirability rank.

  3. Whose interests does this serve? User interest in forming relationships? Platform interest in engagement and revenue? Are these aligned or in conflict?

  4. Apply your framework: Using at least one ethical framework (consequentialism, deontology, virtue ethics, Rawlsian fairness) evaluate whether this feature is ethically defensible as designed. What would make it better?

Exercise 20.4 — Personal Reflection: Digital Courtship and Your Own Experience of Attraction

Type: Reflective writing | Time: 30–45 minutes | Format: Ungraded reflection (400–600 words)

Note: This exercise is personal and will not be read by classmates without your permission. If you prefer, you may write about a hypothetical experience or a close friend's experience rather than your own.

Prompts (choose two):

  1. If you have used a dating app, describe an experience that surprised you — about another person, about the medium, or about yourself. What did the experience reveal that you wouldn't have predicted?

  2. How has the existence of dating apps — whether you use them or not — changed the broader ecology of romantic possibility in your social world? Has meeting through apps normalized or stigmatized?

  3. Barry Schwartz argues that more choice produces less satisfaction. Thinking about your own experience of romantic possibility (on apps or otherwise), do you identify as a "maximizer" or a "satisficer"? How does that affect your approach to dating?

  4. The chapter describes three ways apps can feel alienating: the commodification of the self (being evaluated like a product), the volume problem (too many options), and the identity fit problem (apps not built for who you are). Which of these resonates most with your experience, and why?

Instructor note: Exercise 20.2 requires data science tools. If Python is not available, a simplified Excel-based version of the analysis can be provided using the same CSV file.