I’ve noticed that Gmail is really good at figuring out which parts of an email are new and which are from previous messages in the thread. It’s pretty impressive how it can hide the older stuff and give you the option to “show quoted text” if you want to see it.
Does anyone know how this works? Are there algorithms out there that can spot similar chunks of text in emails? It seems tricky because different email clients handle quoting in their own ways:
Some put > at the start of quoted lines
Some put new text above the old stuff, others put it below
Some don’t even bother marking quotes at all
I’m curious about how Gmail manages to sort this out so well. Any ideas on the tech behind this or similar solutions?
gmail uses smart tricks to spot old vs new text. it scans for symbols like ‘>’ and dates, compares text slices, and uses machine learnin from many emails. dunno all details, but its clever in handling diffrent email styles.
As someone who’s dabbled in email client development, I can shed some light on Gmail’s thread management. It’s not just one trick, but a combination of clever techniques.
Text fingerprinting is a big part of it. Essentially, Gmail creates a unique ‘fingerprint’ for each chunk of text. When a new email comes in, it compares these fingerprints to identify repeated content. This works even if the text has been slightly altered or rearranged.
Another key aspect is analyzing email headers. These contain a wealth of information about the message’s history, including unique identifiers for each email in the thread. Gmail likely uses these to track how content evolves across replies.
From my experience, the real magic happens in the machine learning models. They’re trained on millions of email threads, learning to recognize patterns in how people quote and respond. This allows Gmail to adapt to all sorts of quoting styles and email clients.
It’s a complex system, but it’s what allows Gmail to provide such a seamless threading experience.
Gmail’s ability to identify and filter previously sent content is indeed impressive. From what I understand, it uses a combination of techniques to achieve this. One key method is likely text comparison algorithms that can detect similarities between chunks of text, even if they’re not exact matches. This allows Gmail to identify quoted content across different email clients and quoting styles.
Another important factor is probably machine learning. Gmail has access to vast amounts of email data, which it can use to train models to recognize patterns in how people quote and respond to messages. This helps it adapt to different email clients and individual user habits.
Additionally, Gmail likely looks for common markers like ‘>’ characters, ‘On [date], [name] wrote:’, and other typical quote indicators. It probably also considers the structure of the email, looking for patterns in how new content is typically added in relation to older content.
While the exact algorithms are proprietary, these are likely some of the core principles behind Gmail’s effective thread management.