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Mollom

Bright green graphic celebrating Mollom reaching 10,000 active users and blocking 100 million spam messages. — © Jamey Boje (aka graphicsguru)

A graph shows spam attempts blocked (orange) and non-spam accepted (green) over time, with most activity in blocked spam.

A newspaper article about Mollom, a startup that protects the internet from spam and unwanted content.

A moderation interface for filtering user content based on spam, profanity, and reputation scores, with adjustable settings.

Two people sign documents at a desk while another person smiles nearby in an office setting.

Screenshot of the redesigned Mollom website, showcasing its spam filtering and content quality evaluation features with call-to-action buttons. — The new Mollom.com website design.

The old Mollom.com design that we used from 2007 to early 2012.

Settings page for configuring comment protection using text analysis or CAPTCHA to filter spam and choose automatic or manual moderation.

A Drupal module interface displaying Mollom protection modes for forms, including CAPTCHA and text analysis options. — Different protection modes in the Drupal module for Mollom.

A configuration page for comment form protection with options for spam detection, profanity filtering, and text analysis accuracy settings.

Mollom end of life announcement — Mollom's end-of-life announcement that replaced the old https://mollom.com.

A flowchart showing how Mollom analyzes submitted content, blocks spam, and uses CAPTCHA for uncertain cases. — This image illustrates how the Drupal module for Mollom blocks comment spam. If you're working on a Mollom plugin for your favorite CMS, this is how we recommend that you use the Mollom API. Click this link for a larger version of the image that is easier to read.

A world map showing spam activity, with darker colors representing higher spam levels in different regions.

A spam message with placeholder text and variable word choices, likely generated by an automated script.

Code snippet highlighting "Drupal speak" and a "Spam link", demonstrating content reuse in spam techniques.

Highlighted HTML code snippet showing an embedded Flash file link, a technique commonly used in spam.

A spam message with random text, fake email, and suspicious links, likely generated for automated spam techniques.

Email screenshot requesting investment in a fiberglass factory, with capitalized text emphasizing urgency.

Highlighted spam technique using "google_ad_section_start" in a message promoting house projects.

Email spam message promoting a discount on jeans with an embedded image link, likely for phishing or scams.

A text-based message with empty fields and a short body saying, "thanks for all.

A screenshot shows OpenID links, illustrating potential spam techniques related to user authentication and online identity.

A spam message with a fake author and a suspicious link, likely used for phishing or malicious purposes.

Screenshot of the scorecard section on Molom.com.

© Bart Claeys

The Y-axis shows the number of requests per second. The X-axis shows the day of the month. The graph is for one Mollom server only.

Pie chart showing Mollom usage: 44% personal websites, 27% non-profits, 22% company websites, and 7% customer websites. — Based on a sample of roughly 2,000 Drupal sites that use Mollom.

The plot illustrates that having a classifier with three states avoids false positives and false negatives. The plot is generated from the actual data in Mollom's database.

The plot illustrates that having a binary classifier with only two states (ham and spam) is bound to make mistakes. The plot is generated from the actual data in Mollom's database.

A group of seven people stands together outside, smiling at the camera during a planning event.

Team december — A photo of the Mollom team at an offsite in 2011: it includes Daniel Kudwien, Benjamin Schrauwen, Cedric De Vleeschauwer, Thomas Meire, Johan Vos and Vicky Van Roeyen. Missing in the picture is Dries.

Stacks of printed documents and a folder are neatly arranged on a table in an office setting.

Three people sit at a table sorting and signing documents, with stacks of paper around them.

Mollom closing — Selling Mollom to Acquia involved signing a lot of documents. A photo of me signing the acquisition paperwork with Mary Jefts, Acquia's CFO at the time. It took three hours to sign all the paperwork.

Two people sit at a table reviewing and signing documents, possibly related to the closing of Mollom.

Two people sit at a table signing documents, with stacks of papers, coffee mugs, and snacks nearby.

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