# Clustering memes in social media streams

arXiv: https://arxiv.org/abs/1411.0652 · HTML: https://mlsanity.com/paper/1411.0652/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2014.

## Built on by (most cited first, 2 of 2)

- [TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore](https://mlsanity.com/paper/2305.17911/) (2023)
- [Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications](https://mlsanity.com/paper/2303.16694/) (2023)

## Abstract (arXiv)

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant of Online K-means that incorporates a memory mechanism, used to "forget" old memes and replace them over time with the new ones. The evaluation of our framework is carried out by using a dataset of Twitter trending topics. Over a one-week period, we systematically determined whether our algorithm was able to recover the trending hashtags. We show that the proposed method outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network. We finally show that our online learning framework is flexible, due to its independence of the adopted clustering algorithm, and best suited to work in a streaming scenario.