# Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter

arXiv: https://arxiv.org/abs/2308.02142 · HTML: https://mlsanity.com/paper/2308.02142/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 4 Aug 2023.

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

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [TimeLMs: Diachronic Language Models from Twitter](https://mlsanity.com/paper/2202.03829/) (2022)
- [Statistically Significant Detection of Linguistic Change](https://mlsanity.com/paper/1411.3315/) (2014)
- [TweetNLP: Cutting-Edge Natural Language Processing for Social Media](https://mlsanity.com/paper/2206.14774/) (2022)
- [Human language reveals a universal positivity bias](https://mlsanity.com/paper/1406.3855/) (2014)
- [Short-Term Meaning Shift: A Distributional Exploration](https://mlsanity.com/paper/1809.03169/) (2018)
- [Training Temporal Word Embeddings with a Compass](https://mlsanity.com/paper/1906.02376/) (2019)
- [Twitter Topic Classification](https://mlsanity.com/paper/2209.09824/) (2022)
- [TempoWiC: An Evaluation Benchmark for Detecting Meaning Shift in Social Media](https://mlsanity.com/paper/2209.07216/) (2022)
- [Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter](https://mlsanity.com/paper/2007.12988/) (2020)
- [Just Another Day on Twitter: A Complete 24 Hours of Twitter Data](https://mlsanity.com/paper/2301.11429/) (2023)
- [That's sick dude!: Automatic identification of word sense change across different timescales](https://mlsanity.com/paper/1405.4392/) (2014)

## Built on by (most cited first, 3 of 3)

- [Relational Prompt-based Pre-trained Language Models for Social Event Detection](https://mlsanity.com/paper/2404.08263/) (2024)
- [From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter](https://mlsanity.com/paper/2402.14187/) (2024)
- [EUR-USD Exchange Rate Forecasting Based on Information Fusion with Large Language Models and Deep Learning Methods](https://mlsanity.com/paper/2408.13214/) (2024)

## Abstract (arXiv)

This paper introduces a large collection of time series data derived from Twitter, postprocessed using word embedding techniques, as well as specialized fine-tuned language models. This data comprises the past five years and captures changes in n-gram frequency, similarity, sentiment and topic distribution. The interface built on top of this data enables temporal analysis for detecting and characterizing shifts in meaning, including complementary information to trending metrics, such as sentiment and topic association over time. We release an online demo for easy experimentation, and we share code and the underlying aggregated data for future work. In this paper, we also discuss three case studies unlocked thanks to our platform, showcasing its potential for temporal linguistic analysis.