The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Information diffusion through blogspace
Abstract
We study the dynamics of information propagation in environments of low-overhead personal publishing, using a large collection of WebLogs over time as our example domain. We characterize and model this collection at two levels. First, we present a macroscopic characterization of topic propagation through our corpus, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community. Second, we present a microscopic characterization of propagation from individual to individual, drawing on the theory of infectious diseases to model the flow. We propose, validate, and employ an algorithm to induce the underlying propagation network from a sequence of posts, and report on the results.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Maximizing the spread of influence through a social network
Kempe, D., Kleinberg, J., & Tardos, É. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956769 · Cited by 7,326
Error and attack tolerance of complex networksPreprint
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,467
Threshold models of collective behavior
Granovetter, M. (1978) · American Journal of Sociology · doi:10.1086/226707 · Cited by 6,072
Epidemic Spreading in Scale-Free Networks
Pastor-Satorras, R., & Vespignani, A. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.3200 · Cited by 5,731
Agrawal, R., & Srikant, R. (2002) · Proceedings of the Eleventh International Conference on Data Engineering · doi:10.1109/icde.1995.380415 · Cited by 5,155
Spread of epidemic disease on networks
Newman, M. E. J. (2002) · Physical Review E · doi:10.1103/physreve.66.016128 · Cited by 3,218
Cited by
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
The dynamics of viral marketing
Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007) · ACM Transactions on the Web · doi:10.1145/1232722.1232727 · Cited by 2,032
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Chen, W., Wang, C., & Wang, Y. (2010) · Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1835804.1835934 · Cited by 1,731
Everyone's an influencer: quantifying influence on twitter
Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689
Meme-tracking and the dynamics of the news cycle
Leskovec, J., Backstrom, L., & Kleinberg, J. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557077 · Cited by 1,530
Metadata
- Authors
- Gruhl, D., Liben-Nowell, D., Guha, R., & Tomkins, A.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2004-12
- Volume
- 6
- Issue
- 2
- Pages
- 43-52
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2113889316
- Cited by
- 927
- References
- 31
- FWCI
- 25.93
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Information diffusion through blogspace. https://app.cochat.ai/papers/10.1145/1046456.1046462
- The paper
- Gruhl, D., Liben-Nowell, D., Guha, R., & Tomkins, A. (2004). Information diffusion through blogspace. ACM SIGKDD Explorations Newsletter. https://doi.org/10.1145/1046456.1046462
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.