Platform Enclosure of Human Behavior and its Measurement: Using Behavioral Trace Data against Platform Episteme

tags

Metrics Enclosure

Platform data do not provide a direct window into human behavior. Rather, they are direct records of how we behave under platforms’ influence. (blog)

Notes

platform giants’ two-fold enclosure of first the user ecology and subsequently the previously open market for user attention

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long-acknowledged shortcoming of self-reported survey data

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data representativeness and sampling biases, which are often linked to existing socioeconomic inequalities

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subjective decision-making typically hidden in the production of data

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Computational social scientists tend to treat these two measurement regimes similarly since they both provide passively logged behavioral traces

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glosses over essential differences between audience measurement in traditional media and user analytics provided by digital platforms

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platform companies measure for their own administrative purposes

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investigate measurement as an institutionally constituted technology for managing events

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platform datafication represents a disrupture, for the institutional dynamics of which they are a part are qualitatively different.

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deploy select data as visible metrics

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These social media metrics, at the same time, are inflated by entrepreneurial users

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prioritize content and products whose sales brings in more revenue, and change recommendation systems to value “time-on-platform”

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platform governance and its user ecosystem co-evolve

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platform metrics, unlike their third party counterparts, do not qualify as a common currency. They instead are produced in-house in a highly opaque, individualized manner

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third party companies have no administrative investment in the metric they are producing

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questioning their returns on investments (Joseph, 2018). Yet at the same time they realize that these platforms cannot be audited for advertising effectiveness

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Studying platform user behavior while maintaining the positivist commitments requires the researcher to position herself as an outsider to this behavior, and to achieve this entails taking hold not only of platform log data, but also methodical data about platform architectures and internal administrations. The absence of the latter, usually the case with academics, effectively undercuts the epistemic integrity of quantitative social science.

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academic social sciences’ dependence on platform data as prone to inhabiting the platform’s “standpoint.”

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using YouTube, or more fantastically Netflix data, to discern media preferences, when these platforms’ entire business rests on nudging sequences of viewing

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application of pattern recognition techniques, therefore, functions to obscure platform power.

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patterns reflected structural conditions such as program schedules and people’s socially situated availability to watch rather than their content preferences

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third party log data allow social scientists to model the role of structural influences

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