# Characterizing Human Actions in the Digital Platform by Temporal Context

arXiv: https://arxiv.org/abs/2206.09535 · HTML: https://mlsanity.com/paper/2206.09535/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity](https://mlsanity.com/paper/1506.02594/) (2015)
- [Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours](https://mlsanity.com/paper/2008.11308/) (2020)
- [Quantifying social organization and political polarization in online platforms](https://mlsanity.com/paper/2010.00590/) (2020)
- [Training Temporal Word Embeddings with a Compass](https://mlsanity.com/paper/1906.02376/) (2019)
- [Smartphone App Usage Prediction Using Points of Interest](https://mlsanity.com/paper/1711.09337/) (2017)
- [Human Languages with Greater Information Density Increase Communication Speed, but Decrease Conversation Breadth](https://mlsanity.com/paper/2112.08491/) (2021)
- [Understanding the Role of Intermediaries in Online Social E-commerce: An Exploratory Study of Beidian](https://mlsanity.com/paper/2010.08612/) (2020)

## Abstract (arXiv)

Recent advances in digital platforms generate rich, high-dimensional logs of human behavior, and machine learning models have helped social scientists explain knowledge accumulation, communication, and information diffusion. Such models, however, almost always treat behavior as sequences of actions, abstracting the inter-temporal information among actions. To close this gap, we introduce a two-scale Action-Timing Context(ATC) framework that jointly embeds each action and its time interval. ATC obtains low-dimensional representations of actions and characterizes them with inter-temporal information. We provide three applications of ATC to real-world datasets and demonstrate that the method offers a unified view of human behavior. The presented qualitative findings demonstrate that explicitly modeling inter-temporal context is essential for a comprehensive, interpretable understanding of human activity on digital platforms.