# More Data Types More Problems: A Temporal Analysis of Complexity, Stability, and Sensitivity in Privacy Policies

arXiv: https://arxiv.org/abs/2302.08936 · HTML: https://mlsanity.com/paper/2302.08936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [Privacy Policies over Time: Curation and Analysis of a Million-Document Dataset](https://mlsanity.com/paper/2008.09159/) (2020)
- [A network approach to topic models](https://mlsanity.com/paper/1708.01677/) (2017)
- [Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies](https://mlsanity.com/paper/2004.11131/) (2020)
- [The Privacy Policy Landscape After the GDPR](https://mlsanity.com/paper/1809.08396/) (2018)
- [Statistical inference of assortative community structures](https://mlsanity.com/paper/2006.14493/) (2020)
- [Limits of Individual Consent and Models of Distributed Consent in Online Social Networks](https://mlsanity.com/paper/2006.16140/) (2020)
- [Sentiment and structure in word co-occurrence networks on Twitter](https://mlsanity.com/paper/2110.00587/) (2021)
- [Network compression with configuration models and the minimum description length](https://mlsanity.com/paper/2204.08444/) (2022)
- [Privacy Risk Assessment: From Art to Science, By Metrics](https://mlsanity.com/paper/1709.03776/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Longitudinal Measurement of Privacy Policy Evolution for Large Language Models](https://mlsanity.com/paper/2511.21758/) (2025)

## Abstract (arXiv)

Collecting personally identifiable information (PII) on data subjects has become big business. Data brokers and data processors are part of a multi-billion-dollar industry that profits from collecting, buying, and selling consumer data. Yet there is little transparency in the data collection industry which makes it difficult to understand what types of data are being collected, used, and sold, and thus the risk to individual data subjects. In this study, we examine a large textual dataset of privacy policies from 1997-2019 in order to investigate the data collection activities of data brokers and data processors. We also develop an original lexicon of PII-related terms representing PII data types curated from legislative texts. This mesoscale analysis looks at privacy policies overtime on the word, topic, and network levels to understand the stability, complexity, and sensitivity of privacy policies over time. We find that (1) privacy legislation correlates with changes in stability and turbulence of PII data types in privacy policies; (2) the complexity of privacy policies decreases over time and becomes more regularized; (3) sensitivity rises over time and shows spikes that are correlated with events when new privacy legislation is introduced.