Extract from the StreemView article, “Hiding Below the Surface: StreemView Uncovers 500% More Relevant Messages.”
An AM Law 200 firm engaged StreemView to analyze a Slack data population comprising over 700,000 messages across 5,400 distinct conversations and channels. The goal was to apply negotiated Boolean search terms in a defensible, efficient manner to identify responsive evidentiary content without overrunning downstream review processes. The court had mandated a ±10-message context window around each search hit—a requirement that exposed a fundamental problem with how traditional eDiscovery tools handle modern chat data.
The Problem with 24-Hour RSMF Search
The client needed to apply negotiated Boolean and proximity search terms against a data population of nearly three-quarters of a million messages scattered across thousands of channels. The conventional approach would have been to convert that data into 24-hour RSMF transcripts and run keyword searches against them.
That approach has a structural flaw that becomes critical when Boolean or proximity operators are involved. When conversations are segmented into arbitrary 24-hour blocks, AND and proximity operators can only match terms that appear within the same daily transcript. A conversation that begins on Tuesday evening and continues Wednesday morning becomes two separate documents. A search for “Project X AND settlement” will miss the hit entirely if “Project X” appears in Tuesday’s transcript and “settlement” appears in Wednesday’s.
