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SpamBayes is a Bayesian spam filter written in Python which uses techniques laid out by Paul Graham in his essay "A Plan for Spam". It has subsequently been improved by Gary Robinson and Tim Peters, among others.
The analysis highlights Web filtering and Overview as prominent areas in the source structure around SpamBayes.
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
The extracted context around SpamBayes shows recurring relationship patterns in the source. For example, SpamBayes → Anti-SpamWinning, Archived, Bayesian, Comparison, Conference, E-mail, Graham's, Official, War, Wayback MachineExplaining Another extracted example is SpamBayes → English only. Use these groups to spot repeated connection types before inspecting the individual relationships.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
spam ham message low score high classified bayesian filtering filter python unsure written tim peters paul essay filters web original
TTTA extracted 22 structured relationships around SpamBayes. Examples in this analysis include SpamBayes → Available in → English only and SpamBayes → License → PSFL. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| SpamBayes | Available in | English only | 1.00 | infobox |
| SpamBayes | License | PSFL | 1.00 | infobox |
| SpamBayes | Original author | Tim Peters | 1.00 | infobox |
| SpamBayes | Platform | Cross-platform | 1.00 | infobox |
| SpamBayes | Preview release | 1.1a6 / December 6, 2008 (2008-12-06) | 1.00 | infobox |
| SpamBayes | Release | September 2002 | 1.00 | infobox |
| SpamBayes | Stable release | 1.0.4 / March 2005 | 1.00 | infobox |
| SpamBayes | Type | E-mail filtering | 1.00 | infobox |
| SpamBayes | Website | spambayes.sourceforge.net | 1.00 | infobox |
| SpamBayes | Written in | Python | 1.00 | infobox |
| SpamBayes | is a | Bayesian spam filter written in Python which uses techniques laid out by Paul Graham in his essay | 0.90 | text |
| SpamBayes | related to External links | Official | 0.60 | section |
The concept neighborhoods around SpamBayes bring nearby vocabulary together. In this analysis, examples include E-mail, Essay and Original. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For SpamBayes, one of the stronger structural bridges in this analysis connects SpamBayes with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
TTTA analyzes the structure around SpamBayes to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Web filtering & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — SpamBayes · EN edition · Analysis: TopicsToTalkAbout