# Reading Responses (Set 2)

- [ ] Checklist for a [good reading response](https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Writing_Responses.html) of 250-350 words
- [ ] Begin with a punchy start.
- [ ] Mention specific ideas, details, and examples from the text and earlier classes.
- [ ] Offer something novel that you can offer towards class participation.
- [ ] Check for writing for clarity, concision, cohesion, and coherence.
- [ ] Send to professor with “hackmd” in the subject, with URL of this page and markdown of today’s response.

## Mar 20th, 2026 - Ads & social graph background

The ad is not the product; the user is. In Abrams' (2020) video and Stokes'(2014) online advertising chapter, they claim that online advertising is an infrastructure for identifying, sorting, and predicting people. The chapter categorizes the main forms of online ads: display ads (such as banners, interstitials, floating ads, wallpaper ads, and map ads); search advertising; social media advertising; and mobile advertising. Furthermore, it explains the payment logics behind them, including CPM, CPC, CPA, and CPE. On paper, these terms may seem like neutral marketing formats. However, in practice, the more consequential distinction is targeting.

The video clarifies this by tracing the role of cookies. Lou Montulli describes first-party cookies as a way to give the web memory, allowing carts, logins, and site preferences to persist. But that same mechanism became the basis for third-party tracking, pixels, and cross-site behavioral profiling. The article complements this by explaining how ad servers and exchanges target users through contextual advertising, geo-targeting, social serving, behavioral targeting, and remarketing. These systems enable advertisers to infer who someone is, what they want, and what they may be vulnerable to.

While reading Stokes' (2014) article and watching Abrams' (2020) video, I kept thinking about how online advertising doesn't require discriminatory intent to produce discriminatory outcomes. When advertisers or platforms optimize ad delivery based on factors such as past engagement, location, browsing history, or inferred interests, unequal outcomes will result. For example, a housing ad or political message may be shown more often to some users than to others because the algorithm predicts they are "relevant."

This general idea made me think about where the real ethical weight lies. Is the problem the content in the ads themselves, or in the systems that classify individuals and determine who is seen as relevant? The latter might be doing more damage than we typically tend to acknowledge.

**Please note this section was copied and pasted from a note that was not linked to my account. Here is the link: https://hackmd.io/s/ByQ29Q5cbl**

## Mar 24th, 2026 - Manipulated

"Let truth loose," Jeff Bezos once said when referring to Amazon's approach to customer reviews. Bezos's early vision for Amazon reviews suggested that user-generated ratings and comments could solve informational asymmetry. Informational asymmetry occurs when one party in a transaction has more or better information than the other, creating an imbalance. As illustrated by Reagle (2015), a buyer of a car cannot fully evaluate the product before purchasing it; therefore, the seller knows more about the product's true quality than the buyer does. Reviews allow consumers to evaluate products they cannot directly experience.

However, Reagle (2015) complicates this assumption by showing that reviews are systems that can be manipulated. Businesses can write their own positive reviews, attack competitors, or pay third parties to generate feedback at scale. Additionally, Fowler (2023) reinforces the scale of this issue. He notes that a large portion of online reviews may be fabricated, potentially with entire markets built around producing this deceptive feedback.

One of the most concerning aspects of these readings was that the metrics intentionally designed to signal trust (ratings, helpfulness scores, and rankings) became targets of manipulation. Reagle analyzes this through the concept of Goodhart's Law: once a measure becomes a target, it ceases to be a good measure. This transforms the concept of reviews from a tool of transparency into a tool of competition. Even attempts to "fix" the system, such as filtering suspicious reviews or emphasizing "verified" purchases, can be gamed or remain opaque to users.

As a result, online review systems do not eliminate informational asymmetry as Bezos once envisioned. However, they do restructure it. There is now a more complex asymmetry between users and the systems that curate and rank information. Given this contorted reality, consumers must approach online reviews with an awareness of manipulability; treating them as neutral or fully trustworthy would not only be naive but costly.

## Mar 27th, 2026 - Bemused

Online reviews are supposed to reduce uncertainty, but they can actually have the opposite effect. Reagle (2015) suggests that online reviews reveal what a reviewer expects it to be rather than simply what the product is. While ratings may appear objective, they are filtered through individual standards of what the product or experience should deliver. Reagle (2015) illustrates this with an example of a carbon monoxide alarm that saved a woman's son's life, yet she gave it only 4/5 stars.

While reading this chapter, I kept thinking back to a prior phone call with my mom. She operates three separate Airbnbs that all have very distinct value propositions. Her first 2 are all positioned as more premium experiences, featuring lakefront views, retro flooring, and strong branding. Her Airbnb averages for these properties are all 4.9. However, in the past few years, she has opened a budget and high-value option (~50/night) in a highly desirable Portland, Oregon location. Despite this, reviews often judge the lower-cost unit against expectations that align more closely with her luxury listings or even hotels.

For example, one guest gave 2 stars for value, citing issues such as how she had to park a few houses down due to a lack of parking, even though comparable listings in the same area range from $100-$185/night. In this case, the dissatisfaction seems less about objective failure and more about a misalignment between price and expectation framing, as Reagle (2015) notes that reviewers often operate under the "confounded assumption that others have similar expectations and competencies."

Furthermore, Reagle's argument suggests that review systems collapse multiple dimensions (price, comfort, amenities) into a single rating, which creates distortion. So, for example, a $50 listing is being evaluated as an idealized stay rather than as a budget-friendly option. But with ratings being so subjective, it raises the question of whether they truly reduce informational asymmetry or just replace it with a different type of noise.