The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email
Open accessAbstract
Previous work in social network analysis (SNA) has modeled the existence of links from one entity to another, but not the attributes such as language content or topics on those links. We present the Author-Recipient-Topic (ART) model for social network analysis, which learns topic distributions based on the direction-sensitive messages sent between entities. The model builds on Latent Dirichlet Allocation (LDA) and the Author-Topic (AT) model, adding the key attribute that distribution over topics is conditioned distinctly on both the sender and recipient---steering the discovery of topics according to the relationships between people. We give results on both the Enron email corpus and a researcher's email archive, providing evidence not only that clearly relevant topics are discovered, but that the ART model better predicts people's roles and gives lower perplexity on previously unseen messages. We also present the Role-Author-Recipient-Topic (RART) model, an extension to ART that explicitly represents people's roles.
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Cited by
A Survey of Topic Modeling in Text Mining
Alghamdi, R., & Alfalqi, K. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.060121 · Cited by 377
Echoes of power: language effects and power differences in social interaction
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Prospects for Personalization on the Internet
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Metadata
- Authors
- McCallum, A., Wang, X., & Corrada-Emmanuel, A.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 2007-10-13
- Volume
- 30
- Pages
- 249-272
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2131689821
- Cited by
- 447
- References
- 39
- FWCI
- 20.09
- Type
- journal article
- Language
- en
- Open access
- diamond
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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.
