# Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications

arXiv: https://arxiv.org/abs/2303.16694 · HTML: https://mlsanity.com/paper/2303.16694/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 29 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training](https://mlsanity.com/paper/2002.12804/) (2020)
- [Fast WordPiece Tokenization](https://mlsanity.com/paper/2012.15524/) (2020)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruning](https://mlsanity.com/paper/2207.02463/) (2022)
- [Discovering and Categorising Language Biases in Reddit](https://mlsanity.com/paper/2008.02754/) (2020)
- [Predicting Successful Memes using Network and Community Structure](https://mlsanity.com/paper/1403.6199/) (2014)
- [Characterizing the Life Cycle of Online News Stories Using Social Media Reactions](https://mlsanity.com/paper/1304.3010/) (2013)
- [Characterizing Communities of Hashtag Usage on Twitter During the 2020 COVID-19 Pandemic by Multi-view Clustering](https://mlsanity.com/paper/2008.01139/) (2020)
- [Local structure can identify and quantify influential global spreaders in large scale social networks](https://mlsanity.com/paper/1509.03484/) (2015)
- [Clustering memes in social media streams](https://mlsanity.com/paper/1411.0652/) (2014)

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

- [BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation](https://mlsanity.com/paper/2502.01697/) (2025)
- [PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching](https://mlsanity.com/paper/2603.18363/) (2026)
- [Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications](https://mlsanity.com/paper/2509.25736/) (2025)
- [One Model Is Not a Crowd: Multi-LLM and Aspect-Conditioned Diverse Comment Generation](https://mlsanity.com/paper/2609.33666/) (2026)

## Abstract (arXiv)

Online discourse covers a wide range of topics and many actors tailor their content to impact online discussions through carefully crafted messages and targeted campaigns. Yet the scale and diversity of online media content make it difficult to evaluate the impact of a particular message. In this paper, we present a new technique that leverages semantic similarity to quantify the change in the discussion after a particular message has been published. We use a set of press releases from environmental organisations and tweets from the climate change debate to show that our novel approach reveals a heavy-tailed distribution of response in online discourse to strategic communications.