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Community | data.blog | Dec. 28, 2016

A Brand Image Analysis of WordPress and Automattic on Twitter

Very interesting data driven visualizations of WordPress space by Demet Dagdalen.

A Brand Image Analysis of WordPress and Automattic on Twitter

Community | data.blog | Dec. 28, 2016

As a data scientist, I spend a lot of time analyzing how our users interact with WordPress.com. However, WordPress.com isn’t the only place to gain insight into how people use and talk about our services. Many WordPress.org and WordPress.com discussions take place on social media. Analyzing these discussions can help us understand what our users are saying about WordPress[*] and Automattic, the topics closely associated with our services, and who is leading these discussions. In every social network, there are people who steer the topic and sentiment of the conversation. These influencers usually have large followings and are positioned centrally within the network. Brands often reach out to influencers to organize focus groups or invite them to events, since they’re usually knowledgeable about the brand and can offer insight into how consumers use the product and potential improvements.
At Automattic, we don’t do traditional influencer marketing. However, since the discussions around WordPress in general can offer insight into our users’ experience with our products, I explored the topics, influencers, and ecosystem around WordPress and Automattic products on

14 min read Matt Cromwell
Development | data.blog | Mar. 15, 2017

Improving Relevance and Elasticsearch Query Patterns

Data on how WP.org is improving the Plugin Directory search results with ElasticSearch. Cool stuff!

Improving Relevance and Elasticsearch Query Patterns

Development | data.blog | Mar. 15, 2017

The WordPress.org plugin directory has been significantly rebuilt over the past year and should go live soon (test site). Many from across the WordPress community helped with this effort. I focused on improving plugin search relevancy. This was a great learning experience on how to build more relevant searches for a couple of reasons: There is a decent volume of search traffic (100,000 searches per day and over 500k unique search queries per month).
The repo is small enough to iterate easily (45k total plugins) and yet has enough users and use cases that it can be pretty complex. We went through five major iterations on how to index the data.
A lot of people care and have opinions about how plugin search can be better. This makes for a great opportunity to learn because it is easy to get lots of feedback.
Despite building search engines with Elasticsearch for many years, my opinion on how to structure an Elasticsearch query and index content changed a lot because of this project. This post describes my latest opinions.
Background on Plugin Search
In surveys about WordPress, the community regularly rates the plugin ecosystem as both a top strength and a top weakness of WordPress. Plugins