Multidistrict litigation (MDL) has become a major component of the U.S. civil litigation system. MDLs now make up over half of the total civil caseload in the U.S., with product liability cases comprising the vast majority of MDLs. These cases are great for plaintiffs’ counsel, who, in one example, “netted $26.5 million in fees for a deal that yielded $6.2 million for their clients.” But are they good for anyone else?

Not only do defendant companies have to deal with the expense of MDLs, but these cases are typically protracted, adding to their expense. And that’s not the worst of it: somewhere around a third of cases filed as MDLs “turn out (often at the settlement stage) to be unsupportable.” Companies end up negotiating and settling with a pool of plaintiffs who don’t belong at the table in the first place. Some defense counsel have gone so far as to opine that “a mass tort hardly needs a trigger anymore,” since prospective plaintiffs are all too happy to jump on the lawsuit bandwagon even if they never used the exact product in question. This is obviously bad news for companies, but it’s even worse for those who were actually injured by a product, as their settlement is diluted by hundreds or thousands of improper plaintiffs.

Let’s look at why meritless claims are such a problem and what defendant companies can do to fight back.

Why Are There So Many Meritless Claims in MDLs?

The short answer: because no one is both motivated and empowered to identify those meritless claims early on.

With class actions, there’s a filtering system. Under Federal Rule of Civil Procedure 23, the court must evaluate the case before designating it as a class action. Rule 23(b)(3) requires, in part, that “the court finds that the questions of law or fact common to class members predominate over any questions affecting only individual members.” Further, the court must find “that a class action is superior to other available methods for fairly and efficiently adjudicating the controversy.”

None of that process exists in multidistrict litigation. The court doesn’t participate in the identification of valid claims, nor does it certify the class of prospective plaintiffs.

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But surely plaintiffs’ counsel are screening their clients, right?

Well, kind of. Because counsel are allowed to advertise for injured parties—or, rather, for people who self-identify as injured parties—the net for potential plaintiffs is flung wide. Counsel may then find themselves boxed in, even going so far as to claim that “the volume of cases filed made it difficult for [them] to track their own clients.”

And why not? Potential plaintiffs have every reason to count themselves in, and often, these cases skate by without ever being individually examined. Nor do plaintiffs’ lawyers have a strong incentive to look too closely, as their paycheck is generally tied to the number of clients they represent. Plus, it’s hard to figure out who’s a genuinely injured party—and whose injury is tied to the product in question—and who’s just along for the ride.

Plaintiffs’ lawyers are, of course, running a risk when they don’t screen their clients carefully. And while courts can—and do—use Rule 11 to sanction plaintiffs’ counsel for bringing meritless claims in MDLs, those sanctions are too little, too late to help defendant companies. By the time an individual claim is recognized as frivolous, the case may be practically over.

Wouldn’t it be nice to identify these meritless claims earlier?

A Solution: Better Early Screening Using Online and Social Media Investigation Tools

What’s needed is a source of information about these plaintiffs and a way to conduct more comprehensive early screening into their cases. Enter online data, especially social media information.

Once you start looking, you’ll be absolutely stunned by what people say—and how much of their lives they reveal—online, in publicly available posts. (For those concerned about privacy, never fear: we’re talking only about open source investigations here, using publicly shared information. We’re not prying; we’re looking at what plaintiffs have stated for all to see on the internet.)

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As an example, let’s consider a mass tort case involving injuries, such as unbearable pain, from a medical device such as a hip implant. The major screening factors include the following:

• relationship—did this plaintiff have a hip implant from the defendant?
• injury—does this plaintiff have symptoms related to the hip implant?
• causation—did the hip implant cause the plaintiff’s symptoms?
• timing—did the plaintiff’s symptoms arise after the hip implant?
• statute of limitations—did the plaintiff file the claim sufficiently soon after learning that she suffered an injury caused by the defendant?

Suppose you find an Instagram photo of the plaintiff out skiing the slopes in Aspen or a YouTube video of him surfing in Maui after receiving this hip implant and suffering the “unbearable” pain claimed in the MDL.

Maybe you find a Twitter missive from before the plaintiff’s implant surgery where she’s complaining of intractable hip pain that—obviously—has nothing to do with the implant.

Or—and we’ve seen this exact situation—you may find a Facebook post stating that the subject has realized his injury was caused by a particular product. How does that help the defendant? When the post occurred two and a half years before the initiation of a lawsuit, though the statute of limitations is only two years, you have a plaintiff that has waited too long to file and an easy summary judgment for the defendant

This type of contradictory evidence can negate the timeline of injury or causation, effectively using the plaintiff’s own words to exclude him from litigation.

Okay, but there’s the million-dollar question: how do you find this information? After all, it’s practically impossible—or at least too time-consuming and expensive to be practicable—to comb through the list of plaintiffs, identify each one’s social media and other online profiles, and then search through those posts for potentially relevant information.

If only there were technology that could help!

Oh, wait. There is.

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Smart Online Investigations Rely on Artificial Intelligence

News flash: the internet is huge, and it’s getting bigger every second. (That’s right, it’s bigger now. And now. And now!) People simply can’t keep up; we can’t search fast enough to match the growth in new content.

That’s why we’ve tapped into the power of artificial intelligence (AI) for our online investigative tools. With Hanzo Dynamic Investigator, we gather basic information about a plaintiff and then put our AI to work, letting it scour the internet to identify every social media profile—not to mention blog or website or other content—associated with that specific person. From there, our software can sift through those pages, using keywords and wildcards to identify potentially relevant posts for your attention.

That means you can see where a plaintiff traveled and everything she posted (again, publicly) while she was there. You can see when her posts included key search terms (like “hip” or “pain” or “ski”) that may be related to her claims. And you have the dates of postings at your fingertips, enabling you to quickly determine if the statute of limitations has lapsed.

That gives you the ammunition you need to get frivolous cases knocked out early, interrupting the bandwagon effect before it gets truly rolling. That can greatly reduce your client’s liability, reigning in the size of a global settlement and eliminating unnecessary motion practice and discovery expenses along the way.

The bottom line is this: you don’t have to stand idly by, waiting to see how a particular MDL will unfold.