mlsanityData to 7 Oct 2026

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

arXiv 2303.16694 ↗First posted 29 Mar 2023cs.SI cs.CLcited by 3

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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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 29 references that are in the map. First posted on arXiv 29 Mar 2023.

Builds on

11 of 29 references.

References · 11

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  2. 2020UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Trainingcited by 204
  3. 2020Fast WordPiece Tokenizationcited by 85
  4. 2021A Survey of Race, Racism, and Anti-Racism in NLPcited by 74
  5. 2022Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruningcited by 20
  6. 2020Discovering and Categorising Language Biases in Redditcited by 13
  7. 2014Predicting Successful Memes using Network and Community Structurecited by 11
  8. 2013Characterizing the Life Cycle of Online News Stories Using Social Media Reactionscited by 7
  9. 2020Characterizing Communities of Hashtag Usage on Twitter During the 2020 COVID-19 Pandemic by Multi-view Clusteringcited by 4
  10. 2015Local structure can identify and quantify influential global spreaders in large scale social networkscited by 3
  11. 2014Clustering memes in social media streamscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generationcited by 8
  2. 2026PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matchingcited by 2
  3. 2025Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunicationscited by 1