diff --git a/.gitattributes b/.gitattributes index 049360517ea3b97bb5666ed8d8f0e54c7540bbc4..1ef325f1b111266a6b26e0196871bd78baa8c2f3 100644 --- a/.gitattributes +++ b/.gitattributes @@ -57,6 +57,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text -labels.csv filter=lfs diff=lfs merge=lfs -text -content_embedding filter=lfs diff=lfs merge=lfs -text -content_embedding/ filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md index 334c3dba4b914faafb11e104f2b52deff366fce8..a887d0b3e139c22a24de352ac87c47643accdad9 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,10 @@ license: cc-by-4.0 -CrediBench is a large-scale, temporal webgraph constituted of web data pulled from [Common Crawl](https://commoncrawl.org/overview). +CrediBench 1.1 is a large-scale, temporal webgraph constituted of web data pulled from [Common Crawl](https://commoncrawl.org/overview). +A prior version of the paper is [available here](https://arxiv.org/abs/2509.23340) (NPGML workshop @ NeurIPS 2025), with the latest version still under review. +CrediBench 1.0, presented in this prior work, constituted of a static webgraph with 1 month's data, while the current version contains 3 months of data (October to December 2024, surrounding the U.S Federal elections, a period of increased misinformation). + ## Dataset Details @@ -18,60 +21,11 @@ In these webgraphs, the nodes represent a website domain (e.g, `google.com`) and These webgraphs are supplemented with text attributes, partly from Common Crawl and from web scraping, as text features play an important role in misinformation detection. Additionally, we supplement them with credibility scores as made available by [Lin et al.](https://github.com/hauselin/domain-quality-ratings/tree/main/data), to enable supervised and semi-supervised learning as explained in our paper. - -Dataset Statistics: -| Month | V | E | Min. deg. | Mean deg. | Max. deg. | Leaves (deg. = 1) | Edge Density | -| -- | -- | -- | -- | -- | -- | -- | -- | -| October 2024 | 50,288,479 | 1,074,971,387 | 1 | 42.75 | 17,112,352 | 30,278 | 4.3e-07 | -| November 2024 | 50,684,724 | 1,164,563,814 | 1 | 45.95 | 17,328,063 | 31,388 | 4.5e-07 | -| December 2024 | 45,030,252 | 1,014,523,551 | 1 | 45.06 | 14,719,077 | 28,857 | 5.0e-07 | -| January 2025 | 45,626,949 | 1,060,163,646 | 1 | 46.471 | 15,398,279 | 23,130 | 5.0e-07 | -| February 2025 | 49,639,664 | 1,167,748,533 | 1 | 47.05 | 17,078,954 | 24,430 | 4.7e-07 | -| March 2025 | 50,162,733 | 1,212,826,396 | 1 | 48.36 | 16,691,193 | 22,629 | 4.8e-07 | -| April 2025 | 50,050,221 | 1,237,519,870 | 1 | 49.45 | 16,679,192 | 26,639 | 4.9e-07 | -| May 2025 | 50,517,253 | 1,227,682,479 | 1 | 48.604 | 16,771,274 | 29,973 | 4.8e-07 | - - - - -**Content Embedding:** - -Domain-level content embeddings are generated using multiple LLM-based embedding models with varying LLM-model sizes and embedding dimensions. -The embeddings are intended to support feature initialization for downstream GNN models. -For each domain, the textual content is first extracted and then encoded into dense vector representations using the selected embedding model. - -The dataset is organized by month under the content_embeddings directory. - -Each pickled file stores a dictionary: -``` -{ domain1:[[page_url1, embedding_vector1],[page_url2, embedding_vector2], ...], - domain2:[[page_url1, embedding_vector1],[page_url2, embedding_vector2], ...], - ... -} -``` - -| Month | Embedding-model | Emb-dim| Total-files-size| -| -- | -- | -- | -- | -| October 2024 | embeddinggemma-300m | 256 | 30GB| -| November 2024 | embeddinggemma-300m | 256 | 30GB| -| December 2024 | embeddinggemma-300m | 256 | 30GB| - - -**Proportion of content** - - -| Task | Split | Class | Global Labels | CDB-Oct24 Labels | CDB-Oct24 Has_Content | CDB-Oct24 content(%) | CDB-Nov24 Labels | CDB-Nov24 Has_Content | CDB-Nov24 content(%) | CDB-Dec24 Labels | CDB-Dec24 Has_Content | CDB-Dec24 content(%) | -|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:| -| Regression | Train | — | 5,164 | 5,158 | 2,699 | 0.52 | 5,158 | 2,626 | 0.51 | 5,158 | 2,637 | 0.50 | -| Regression | Val | — | 1,722 | 1,719 | 869 | 0.51 | 1,719 | 842 | 0.49 | 1,719 | 841 | 0.49 | -| Regression | Test | — | 1,722 | 1,721 | 874 | 0.51 | 1,721 | 847 | 0.49 | 1,721 | 843 | 0.49 | -| Binary Classification | Train | Credible | 41,671 | 39,824 | 23,170 | 0.58 | 34,040 | 20,379 | 0.60 | 39,445 | 21,853 | 0.55 | -| Binary Classification | Train | Non-Cred. | 9,353 | 8,272 | 5,734 | 0.69 | 5,496 | 4,034 | 0.73 | 7,831 | 5,040 | 0.64 | -| Binary Classification | Val | Credible | 3,117 | 2,960 | 1,669 | 0.56 | 2,503 | 1,446 | 0.58 | 2,940 | 1,566 | 0.53 | -| Binary Classification | Val | Non-Cred. | 3,117 | 2,717 | 1,929 | 0.71 | 1,784 | 1,321 | 0.74 | 2,619 | 1,694 | 0.65 | -| Binary Classification | Test | Credible | 3,117 | 2,967 | 1,739 | 0.59 | 2,555 | 1,543 | 0.60 | 2,947 | 1,629 | 0.55 | -| Binary Classification | Test | Non-Cred. | 3,117 | 2,713 | 1,920 | 0.71 | 1,782 | 1,328 | 0.75 | 2,620 | 1,704 | 0.65 | - +- **Curated by** a team of collaborators from the Complex Data Lab @ Mila - Quebec AI Institute, the University of Oxford, McGill University, Concordia University, UC Berkeley, University of Montreal, and AITHYRA. +- **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant: +*Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI +Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada. +- **License:** CC-BY-4.0 (as retributed from Common Crawl). ### Resources @@ -106,15 +60,6 @@ which builds a month's worth of data by pulling from Common Crawl, builds the gr Each edge has a timestamp, given as the date of the first day of week of the crawl, in format YYYYMMDD. -## Acknowledgements - -- **Curated by** a team of collaborators from the Complex Data Lab @ Mila - Quebec AI Institute, the University of Oxford, McGill University, Concordia University, UC Berkeley, University of Montreal, and AITHYRA. -- **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant: -*Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI -Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada. -- **License:** CC-BY-4.0 (as retributed from Common Crawl). - - ## Citation diff --git a/april2025/edges.csv.gz b/april2025/edges.csv.gz deleted file mode 100644 index 3ec9b6f4a046d9775dbfff87e77fa18f19061685..0000000000000000000000000000000000000000 --- a/april2025/edges.csv.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d1c591730c0588ec7b55d04ec00ec5f406b6129fe053cdfe018317de6ca0681e -size 12931680828 diff --git a/april2025/vertices.csv.gz b/april2025/vertices.csv.gz deleted file mode 100644 index 27402939859cd0d3bb0f97b8cdccc5c598ba1cba..0000000000000000000000000000000000000000 --- a/april2025/vertices.csv.gz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3543183b9ddc83a5e2b7e813108242bff65fd9238de33a39002df508aff4c56a -size 966067155 diff --git 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