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6

labels-dataset

#3
by ekmpa - opened
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  1. .gitattributes +0 -3
  2. README.md +9 -64
  3. april2025/edges.csv.gz +0 -3
  4. april2025/vertices.csv.gz +0 -3
  5. content_embedding/dec2024/embeddinggema-300m/dec2024_dqr_embeddinggemma-300m.pkl +0 -3
  6. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_0_199.pkl +0 -3
  7. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_10300_12800.pkl +0 -3
  8. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_1400_2799.pkl +0 -3
  9. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_200_599.pkl +0 -3
  10. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_20700_30599.pkl +0 -3
  11. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_2800_5300.pkl +0 -3
  12. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_30600_52100.pkl +0 -3
  13. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_5300_7800.pkl +0 -3
  14. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_600_1399.pkl +0 -3
  15. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_7800_10300.pkl +0 -3
  16. content_embedding/dec2024/embeddinggema-300m/dec2024_wetcontent_domains_index.pkl +0 -3
  17. content_embedding/nov2024/embeddinggema-300m/nov2024_dqr_embeddinggemma-300m.pkl +0 -3
  18. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_0_100.pkl +0 -3
  19. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_10000_24999.pkl +0 -3
  20. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_1000_1999.pkl +0 -3
  21. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_100_199.pkl +0 -3
  22. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_2000_2999.pkl +0 -3
  23. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_200_299.pkl +0 -3
  24. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_25000_40499.pkl +0 -3
  25. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_3000_3999.pkl +0 -3
  26. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_300_499.pkl +0 -3
  27. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_4000_4799.pkl +0 -3
  28. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_4800_9999.pkl +0 -3
  29. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_500_999.pkl +0 -3
  30. content_embedding/nov2024/embeddinggema-300m/nov2024_wetcontent_domains_index.pkl +0 -3
  31. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_0_99.pkl +0 -3
  32. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_10000_11999.pkl +0 -3
  33. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_1000_1999.pkl +0 -3
  34. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_100_499.pkl +0 -3
  35. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_12000_17999.pkl +0 -3
  36. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_18000_26999.pkl +0 -3
  37. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_2000_4999.pkl +0 -3
  38. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_27000_34499.pkl +0 -3
  39. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_34500_56299.pkl +0 -3
  40. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_5000_7999.pkl +0 -3
  41. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_500_799.pkl +0 -3
  42. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_8000_9999.pkl +0 -3
  43. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_800_999.pkl +0 -3
  44. content_embedding/oct2024/embeddinggema-300m/oct2024_wetcontent_domains_index.pkl +0 -3
  45. dec2024/components/edge_index.pt +0 -3
  46. dec2024/components/feature_matrix.pt +0 -3
  47. jan2025/edges.csv.gz +0 -3
  48. jan2025/vertices.csv.gz +0 -3
  49. labels.csv +0 -3
  50. march2025/edges.csv.gz +0 -3
.gitattributes CHANGED
@@ -57,6 +57,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
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- labels.csv filter=lfs diff=lfs merge=lfs -text
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- content_embedding filter=lfs diff=lfs merge=lfs -text
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- content_embedding/ filter=lfs diff=lfs merge=lfs -text
 
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  # Video files - compressed
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  *.mp4 filter=lfs diff=lfs merge=lfs -text
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  *.webm filter=lfs diff=lfs merge=lfs -text
 
 
 
README.md CHANGED
@@ -6,7 +6,10 @@ license: cc-by-4.0
6
 
7
  <!-- Provide a quick summary of the dataset. -->
8
 
9
- CrediBench is a large-scale, temporal webgraph constituted of web data pulled from [Common Crawl](https://commoncrawl.org/overview).
 
 
 
10
 
11
  ## Dataset Details
12
 
@@ -18,60 +21,11 @@ In these webgraphs, the nodes represent a website domain (e.g, `google.com`) and
18
  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.
19
  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.
20
 
21
-
22
- Dataset Statistics:
23
- | Month | V | E | Min. deg. | Mean deg. | Max. deg. | Leaves (deg. = 1) | Edge Density |
24
- | -- | -- | -- | -- | -- | -- | -- | -- |
25
- | October 2024 | 50,288,479 | 1,074,971,387 | 1 | 42.75 | 17,112,352 | 30,278 | 4.3e-07 |
26
- | November 2024 | 50,684,724 | 1,164,563,814 | 1 | 45.95 | 17,328,063 | 31,388 | 4.5e-07 |
27
- | December 2024 | 45,030,252 | 1,014,523,551 | 1 | 45.06 | 14,719,077 | 28,857 | 5.0e-07 |
28
- | January 2025 | 45,626,949 | 1,060,163,646 | 1 | 46.471 | 15,398,279 | 23,130 | 5.0e-07 |
29
- | February 2025 | 49,639,664 | 1,167,748,533 | 1 | 47.05 | 17,078,954 | 24,430 | 4.7e-07 |
30
- | March 2025 | 50,162,733 | 1,212,826,396 | 1 | 48.36 | 16,691,193 | 22,629 | 4.8e-07 |
31
- | April 2025 | 50,050,221 | 1,237,519,870 | 1 | 49.45 | 16,679,192 | 26,639 | 4.9e-07 |
32
- | May 2025 | 50,517,253 | 1,227,682,479 | 1 | 48.604 | 16,771,274 | 29,973 | 4.8e-07 |
33
- <!-- | June 2025 | 9,974,275 | 152,449,542 | 1 | 30.57 | 3,381,364 | 25,447 | 1.5e06 | -->
34
-
35
-
36
-
37
- **Content Embedding:**
38
-
39
- Domain-level content embeddings are generated using multiple LLM-based embedding models with varying LLM-model sizes and embedding dimensions.
40
- The embeddings are intended to support feature initialization for downstream GNN models.
41
- For each domain, the textual content is first extracted and then encoded into dense vector representations using the selected embedding model.
42
-
43
- The dataset is organized by month under the content_embeddings directory.
44
-
45
- Each pickled file stores a dictionary:
46
- ```
47
- { domain1:[[page_url1, embedding_vector1],[page_url2, embedding_vector2], ...],
48
- domain2:[[page_url1, embedding_vector1],[page_url2, embedding_vector2], ...],
49
- ...
50
- }
51
- ```
52
-
53
- | Month | Embedding-model | Emb-dim| Total-files-size|
54
- | -- | -- | -- | -- |
55
- | October 2024 | embeddinggemma-300m | 256 | 30GB|
56
- | November 2024 | embeddinggemma-300m | 256 | 30GB|
57
- | December 2024 | embeddinggemma-300m | 256 | 30GB|
58
-
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-
60
- **Proportion of content**
61
-
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-
63
- | 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(%) |
64
- |---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
65
- | Regression | Train | — | 5,164 | 5,158 | 2,699 | 0.52 | 5,158 | 2,626 | 0.51 | 5,158 | 2,637 | 0.50 |
66
- | Regression | Val | — | 1,722 | 1,719 | 869 | 0.51 | 1,719 | 842 | 0.49 | 1,719 | 841 | 0.49 |
67
- | Regression | Test | — | 1,722 | 1,721 | 874 | 0.51 | 1,721 | 847 | 0.49 | 1,721 | 843 | 0.49 |
68
- | Binary Classification | Train | Credible | 41,671 | 39,824 | 23,170 | 0.58 | 34,040 | 20,379 | 0.60 | 39,445 | 21,853 | 0.55 |
69
- | 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 |
70
- | Binary Classification | Val | Credible | 3,117 | 2,960 | 1,669 | 0.56 | 2,503 | 1,446 | 0.58 | 2,940 | 1,566 | 0.53 |
71
- | 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 |
72
- | Binary Classification | Test | Credible | 3,117 | 2,967 | 1,739 | 0.59 | 2,555 | 1,543 | 0.60 | 2,947 | 1,629 | 0.55 |
73
- | 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 |
74
-
75
 
76
  ### Resources
77
 
@@ -106,15 +60,6 @@ which builds a month's worth of data by pulling from Common Crawl, builds the gr
106
  Each edge has a timestamp, given as the date of the first day of week of the crawl, in format YYYYMMDD.
107
 
108
 
109
- ## Acknowledgements
110
-
111
- - **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.
112
- - **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant:
113
- *Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI
114
- Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada.
115
- - **License:** CC-BY-4.0 (as retributed from Common Crawl).
116
-
117
-
118
  ## Citation
119
 
120
  <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
6
 
7
  <!-- Provide a quick summary of the dataset. -->
8
 
9
+ CrediBench 1.1 is a large-scale, temporal webgraph constituted of web data pulled from [Common Crawl](https://commoncrawl.org/overview).
10
+ 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.
11
+ 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).
12
+
13
 
14
  ## Dataset Details
15
 
 
21
  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.
22
  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.
23
 
24
+ - **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.
25
+ - **Funding:** This research was supported by the Engineering and Physical Sciences Research Council (EPSRC) and the AI Security Institute (AISI) grant:
26
+ *Towards Trustworthy AI Agents for Information Veracity and the EPSRC Turing AI World-Leading Research Fellowship No. EP/X040062/1 and EPSRC AI
27
+ Hub No. EP/Y028872/1*. This research was also enabled in part by compute resources provided by Mila (mila.quebec) and Compute Canada.
28
+ - **License:** CC-BY-4.0 (as retributed from Common Crawl).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
29
 
30
  ### Resources
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  Each edge has a timestamp, given as the date of the first day of week of the crawl, in format YYYYMMDD.
61
 
62
 
 
 
 
 
 
 
 
 
 
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  ## Citation
64
 
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  <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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