What Is an eDiscovery Slop Search? And Is It Better Than a Regular Search?

05 September 2019 by Anith Mathai eDiscovery file-family

Takeaway: The best eDiscovery searches are flexible. And ‘slop searches’ are the perfect way to ‘loosen’ your eDiscovery search. It tells your software that it’s okay if your search words aren’t right next to each other. Or even that they don’t have to be in any particular order. So, make sure your software can do slop searches.

When we think ‘eDiscovery search,’ we think ‘keywords.’

In the old days of ‘paper’ discovery, we’d spend weeks reading through hundreds (or thousands) of documents, noting important facts to help build our case. Now, eDiscovery search engines take just seconds to find files with our keywords. So, for example, if we’re looking for the word ‘acquitted’, the search engine shows us the places in our documents where the word ‘acquitted’ popped up.

But keywords come with their own set of problems.

Things are straightforward when you’re looking for a single keyword. But what about when you’re searching for a key phrase? For example, say you’re looking for information about the Sanderson pitch. You’ll type ‘Sanderson pitch’ into your eDiscovery search engine. And it’ll pull up a bunch of files. But only those with an exact match. So, it’ll miss ‘Sanderson draft pitch’ or ‘Sanderson rejected the pitch.’ Which means you’ll miss potential goldmines of evidence.

This is where the ‘slop search’ comes in.

Here, we tell our search engine that it’s okay if our search words aren’t right next to each other. We do this by giving our search a ‘slop value’ – i.e., a number between 1 and 255.

  • A slop value of ‘1’ signals that it’s okay if there’s one extra word between our search terms. In our earlier example, a slop value of 1 would catch ‘Sanderson draft pitch’ – since there’s only one word between ‘Sanderson’ and ‘pitch’.
  • A slop value of ‘2’ signals that two in-between words are fine. So, we’d catch ‘Sanderson rejected the pitch’. And so on.
  • Slop searches can also find you instances where the keywords are in a different order. E.g., ‘The pitch was drafted by Sanderson.’ Here, ‘Sanderson’ and ‘pitch’ are separated by 3 words. But their order has been flipped (‘Sanderson’ comes after ‘pitch’). A slop search would catch this.
  • But remember: a larger slop value means a less specific search. So, you’ll likely get more irrelevant results.

(Learn how to do a slop search).

And slop searches are just the beginning. Here are some other eDiscovery search tools.

And they help us go beyond simple keyword searches. For example:

  • Boolean searches: Mix and match keywords using ‘Boolean’ operators like ‘AND’, ‘OR’ and ‘NOT’. So, for example, tell your eDiscovery software to “Find all emails John Anderson sent Sally Nedry, which mention the Pfizer meeting. And which were sent before 2015.” Learn more about Boolean searches.
  • Fuzzy searches: Automatically pull up words with almost-similar spellings. Perfect for catching misspellings. So it won’t matter if you type ‘Johnn’ by mistake.
  • Stemming: Your search engine trims a keyword down to its root – or stem – and then searches for variations of this stem. So, it’ll first trim ‘acquitted’ down to ‘acquit’, and then look for its variations. Which makes for more effective searches. (Note: Did you notice how Google suddenly became easier to use in the mid-2000s? One of the reasons is that it started stemming. Before 2004, Google wouldn’t have seen a connection between ‘acquit’ and ‘acquitted’.)
  • ‘Stop lists’: There are some words that pop up often but don’t add meaning to a search phrase. For example, “the,” “and,” “a,” “them,” etc. Search engines make a list of these ‘stop’ words and exclude them from searches – which saves a lot of time. That’s why the search terms “deposition” and “the deposition” will get you the same results.
  • Technology-assisted review (TAR): Also called CAR – Computer Assisted Review, or predictive coding. Here, your search engine studies the files you mark as ‘relevant’ and learns what you’re looking for. It then starts pulling up similar documents for you to review. Which saves a lot of time. It’s like how YouTube learns your taste in videos and then suggests new clips that you might like to watch.
  • Metadata searches. The search engine uses metadata to find the files you need. With documents, that could be things like when they were created, who created them, etc. With emails, it could be when they were sent, when they were opened, and who opened them.

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