MemeTracker: News Cycle Phrase Cluster Network
A Web (news sites and blogs, Aug 2008–Apr 2009) dataset of 71,568 phrase clusters, 310,457 unique phrase nodes, 238,889 directed root→variant edges, 71,568 phrases in text corpus, collected by Leskovec et al. via web crawl of 1.5M articles/day, August 2008 to April 2009, distributed by SNAP. Each record contains phrase cluster graph where nodes are natural language phrases extracted from news articles and blogs; directed edges from root phrase to variant phrases within each cluster; edge weight = variant phrase frequency; covers the 2008 US election, financial crisis, and other major news events. The dataset is notable for its foundational information diffusion dataset capturing how phrases mutate and propagate across mainstream media and blogs; the cluster graph encodes phrase similarity and co-occurrence structure, enabling study of linguistic variation in news cycles. Intended for research into information diffusion modeling, meme propagation analysis, phrase clustering, linguistic variation in news media, temporal network analysis, and graph-based NLP research.
Cite this dataset at
memetracker.echo.directory
Analytics
The full graph has 310,457 nodes; this view shows the 1,000-node sample the pipeline drew for display. Node size is centrality, colour is community.
Topic 0
Topic 1
Topic 2
Topic 3
Topic 4
Topic 5
Topic 6
Topic 7
Topic 8
Topic 9
Topic 10
Topic 11
Show all 26 topics
Topic 12
Topic 13
Topic 14
Topic 15
Topic 16
Topic 17
Topic 18
Topic 19
Topic 20
Topic 21
Topic 22
Topic 23
Topic 24
Topic 25
Sampled: text metrics from 50K of 71.6K records; topic model from 20K.
Record
- Catalog key
memetracker- Authors
- Leskovec, Jure; Backstrom, Lars; Kleinberg, Jon
- Year
- 2009
- Source paper
- Meme-tracking and the Dynamics of the News Cycle
- DOI / arXiv
- 10.1145/1557019.1557077
- License
- Unknown — SNAP public research dataset
- Records
- 310,457
- Nodes
- 310,457
- Edges
- 238,889 (directed)
- Nodes are
- phrase / cluster
- Edges are
- cluster membership
- Attributes
- nodes have no attributes; edges have attributes
- Text corpus
- news article text
- Languages
- en
- Collected
- August 2008 to April 2009 (2000s)
- Tasks
- From the graph: link prediction, community detection
- Region
- United States · North America (inferred: named in the description)
Provenance
- Originally published at
- https://snap.stanford.edu/memetracker/data.html link checked
- Hosted copy
- Mirrored on echo.directory on 2026-10-21, 13.6 GB across 10 files. Checksums in MANIFEST.json.
Citations
Meme-tracking and the dynamics of the news cycle
J. Leskovec, Lars Backstrom, J. Kleinberg
Journal Article · Knowledge Discovery and Data Mining · 2009
Cited by
- Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Alexandra Olteanu, Carlos Castillo et al. · Frontiers in Big Data · 2019 · cited 771× - Can Large Language Models Transform Computational Social Science?
Caleb Ziems, William B. Held et al. · International Conference on Computational Logic · 2023 · cited 661× - Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster
Naeemul Hassan, Fatma Arslan et al. · Knowledge Discovery and Data Mining · 2017 · cited 396× - The diffusion of misinformation on social media: Temporal pattern, message, and source
Jieun Shin, Lian Jian et al. · Computers in Human Behavior · 2018 · cited 370× - Applications of Topic Models
Jordan L. Boyd-Graber, Yuening Hu et al. · Foundations and Trends in Information Retrieval · 2017 · cited 333×
Show all 10
- Social Media and the Formation of Organizational Reputation
M. Etter, D. Ravasi et al. · Academy of Management Review · 2017 · cited 332× - Topic Modeling Using Latent Dirichlet allocation
Uttam Chauhan, Apurva Shah · ACM Computing Surveys · 2021 · cited 330× - On the Origins of Memes by Means of Fringe Web Communities
Savvas Zannettou, T. Caulfield et al. · ACM/SIGCOMM Internet Measurement Conference · 2018 · cited 313× - Accelerating dynamics of collective attention
Philipp Lorenz-Spreen, B. Mønsted et al. · Nature Communications · 2019 · cited 296× - Automated identification of media bias in news articles: an interdisciplinary literature review
Felix Hamborg, Karsten Donnay et al. · International Journal on Digital Libraries · 2018 · cited 255×
Citation data from Semantic Scholar, retrieved 2026-10-19.
How to cite
BibTeX
@misc{echo2026,
title = {ECHO: Exploratory Compendium of Humanity Online},
author = {Switzer, Braden and Zafarani, Reza},
year = {2026},
url = {https://echo.directory},
note = {Syracuse University}
}
@inproceedings{leskovec2009memetracking,
title = {Meme-tracking and the Dynamics of the News Cycle},
author = {Leskovec, Jure and Backstrom, Lars and Kleinberg, Jon},
booktitle = {Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
year = {2009},
publisher = {ACM},
doi = {10.1145/1557019.1557077}
} APA
Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory
Leskovec, J., Backstrom, L., & Kleinberg, J. (2009). MemeTracker: News Cycle Phrase Cluster Network [Data set]. https://doi.org/10.1145/1557019.1557077
MLA
Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.
Leskovec, Jure, et al. MemeTracker: News Cycle Phrase Cluster Network. 2009, doi.org/10.1145/1557019.1557077.