# Semantic Journeys: Quantifying Change in Emoji Meaning from 2012-2018

arXiv: https://arxiv.org/abs/2105.00846 · HTML: https://mlsanity.com/paper/2105.00846/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 3 May 2021.

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

- [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)
- [SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detection](https://mlsanity.com/paper/2007.11464/) (2020)
- [Diachronic word embeddings and semantic shifts: a survey](https://mlsanity.com/paper/1806.03537/) (2018)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Survey of Computational Approaches to Lexical Semantic Change](https://mlsanity.com/paper/1811.06278/) (2018)
- [Temporal Analysis of Language through Neural Language Models](https://mlsanity.com/paper/1405.3515/) (2014)
- [Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic Change](https://mlsanity.com/paper/1906.01688/) (2019)
- [Sentiment of Emojis](https://mlsanity.com/paper/1509.07761/) (2015)
- [GASC: Genre-Aware Semantic Change for Ancient Greek](https://mlsanity.com/paper/1903.05587/) (2019)
- [EmojiNet: An Open Service and API for Emoji Sense Discovery](https://mlsanity.com/paper/1707.04652/) (2017)
- [Temporal Effects on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach](https://mlsanity.com/paper/1701.01276/) (2017)
- [Frequency patterns of semantic change: Corpus-based evidence of a near-critical dynamics in language change](https://mlsanity.com/paper/1703.00203/) (2017)

## Built on by (most cited first, 4 of 4)

- [Evolving linguistic divergence on polarizing social media](https://mlsanity.com/paper/2309.01659/) (2023)
- [On the Context-Free Ambiguity of Emoji](https://mlsanity.com/paper/2201.06302/) (2022)
- [How to Do Things without Words: Modeling Semantic Drift of Emoji](https://mlsanity.com/paper/2110.04093/) (2021)
- [FinMoji: A Framework for Emoji-driven Sentiment Analysis in Financial Social Media](https://mlsanity.com/paper/2605.09469/) (2026)

## Abstract (arXiv)

The semantics of emoji has, to date, been considered from a static perspective. We offer the first longitudinal study of how emoji semantics changes over time, applying techniques from computational linguistics to six years of Twitter data. We identify five patterns in emoji semantic development and find evidence that the less abstract an emoji is, the more likely it is to undergo semantic change. In addition, we analyse select emoji in more detail, examining the effect of seasonality and world events on emoji semantics. To aid future work on emoji and semantics, we make our data publicly available along with a web-based interface that anyone can use to explore semantic change in emoji.