“Would I pay $1 to watch this again?” → Yes = high entertainment. No = low, regardless of trends.
Abstract In an age of algorithmic recommendations, users often feel a loss of agency over what they watch, listen to, and read. This paper presents a self-research methodology for quantifying personal entertainment consumption and its relationship to externally defined “trending” content. By developing a measurement system based on time, engagement, and emotional valence, an individual can map their unique entertainment landscape, identify discrepancies between personal taste and algorithmic pushes, and make more intentional media choices. 1. Introduction The terms “entertainment” and “trending” are often conflated. Platforms like TikTok, YouTube, and Netflix label content as “trending” to suggest popularity, yet an individual’s genuine entertainment is subjective. This paper asks: How can one systematically measure what they truly enjoy versus what is merely popular? measuring my cum
Google Form (manual) + Apple Health (heart rate) + Last.fm (music) + Netflix viewing history export. “Would I pay $1 to watch this again
The goal is to create a repeatable, quantifiable framework that transforms vague feelings (“I liked that show”) into actionable data (“That show delivered 8.2 enjoyment units per minute”). Before measurement, we must operationalize our two core constructs. 2.1 Measuring “My Entertainment” Entertainment is defined here as the subjective hedonic value derived from a media unit. We measure it using three proxies: and emotional valence
“Would I pay $1 to watch this again?” → Yes = high entertainment. No = low, regardless of trends.
Abstract In an age of algorithmic recommendations, users often feel a loss of agency over what they watch, listen to, and read. This paper presents a self-research methodology for quantifying personal entertainment consumption and its relationship to externally defined “trending” content. By developing a measurement system based on time, engagement, and emotional valence, an individual can map their unique entertainment landscape, identify discrepancies between personal taste and algorithmic pushes, and make more intentional media choices. 1. Introduction The terms “entertainment” and “trending” are often conflated. Platforms like TikTok, YouTube, and Netflix label content as “trending” to suggest popularity, yet an individual’s genuine entertainment is subjective. This paper asks: How can one systematically measure what they truly enjoy versus what is merely popular?
Google Form (manual) + Apple Health (heart rate) + Last.fm (music) + Netflix viewing history export.
The goal is to create a repeatable, quantifiable framework that transforms vague feelings (“I liked that show”) into actionable data (“That show delivered 8.2 enjoyment units per minute”). Before measurement, we must operationalize our two core constructs. 2.1 Measuring “My Entertainment” Entertainment is defined here as the subjective hedonic value derived from a media unit. We measure it using three proxies:
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