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Gravatar (a portmanteau of globally recognized avatar) is a service for providing globally unique avatars and was created by Tom Preston-Werner. Since 2007, it has been owned by Automattic, having integrated it into their WordPress.com blogging platform.
The analysis highlights History, Functionality and Security concerns and data breaches as prominent areas in the source structure around Gravatar.
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 Gravatar shows recurring relationship patterns in the source. For example, Gravatar → Drupal, If, JSON, MODX, On Gravatar, PHP, Portable Contacts, QR, Redmine, Support, The, WordPress, XML Another extracted example is Gravatar → Besides, February, For, Gravatar Premium, Gravatar's, Gravatars, June, On, Support, The, Tom Preston-Werner. 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.
email md5 data avatar automattic web addresses service 2007 tom preston-werner users account address user also gravatars hashes since security
TTTA extracted 47 structured relationships around Gravatar. Examples in this analysis include Gravatar → Commercial → Yes and Gravatar → Created by → Tom Preston-Werner. The table shows each extracted connection, where it came from and its confidence.
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| Gravatar | Commercial | Yes | 1.00 | infobox |
| Gravatar | Created by | Tom Preston-Werner | 1.00 | infobox |
| Gravatar | Owner | Automattic | 1.00 | infobox |
| Gravatar | Registration | Optional | 1.00 | infobox |
| Gravatar | Type of site | Avatar hosting | 1.00 | infobox |
| Gravatar | URL | gravatar.com | 1.00 | infobox |
| Drupal | instance of | Support for Gravatar is also provided via third-party modules for web content management systems | 0.80 | text |
| MODX.A user's profile data is available in a number of metadata standards | instance of | Support for Gravatar is also provided via third-party modules for web content management systems | 0.80 | text |
| including hCard | instance of | Support for Gravatar is also provided via third-party modules for web content management systems | 0.80 | text |
| JSON | instance of | Support for Gravatar is also provided via third-party modules for web content management systems | 0.80 | text |
| XML | instance of | Support for Gravatar is also provided via third-party modules for web content management systems | 0.80 | text |
| PHP | instance of | Support for Gravatar is also provided via third-party modules for web content management systems | 0.80 | text |
The concept neighborhoods around Gravatar bring nearby vocabulary together. In this analysis, examples include Email, User and Associated. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
For Gravatar, one of the stronger structural bridges in this analysis connects Gravatar with Functionality. 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 Gravatar to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Functionality & Security concerns and data breaches, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
Source: Wikipedia — Gravatar · EN edition · Analysis: TopicsToTalkAbout