Social Media Statements: Key Evidence and Often Exceptions to the Hearsay Rule

By John Patzakis

Here is a quick legal evidence quiz: Identify the three distinct hearsay exceptions in the following Tweet:

Accident 5

 

The first exception would be under Federal Rule of Evidence 803(2):

“Rule 803. Exceptions to the Rule Against Hearsay: . . . (2) Excited Utterance. A statement relating to a startling event or condition, made while the declarant was under the stress of excitement that it caused.”

Pretty clear here. The four OMGs are a good indication. So no one can argue that the phrase “OMG” never has any legal consequence.

The second exception would be under FRE 803(1): “Present Sense Impression. A statement describing or explaining an event or condition, made while or immediately after the declarant perceived it.”

And if the witness some time later did not recall details of the incident (two words: Vegas, hangover), the statement could be introduced as a recorded recollection under 803(5).

Another key hearsay exception are statements offered as evidence of the then state of mind of the declarant. While YouTube is known for cat videos, Twitter and Facebook are in large part a platform for statements like this:

Happy Tweet

 

In other words, to quote FRE 803(3): “Then-Existing Mental, Emotional, or Physical Condition. A statement of the declarant’s then-existing state of mind (such as motive, intent, or plan) or emotional, sensory, or physical condition (such as mental feeling, pain, or bodily health)”

While social media is a great place to find out what Kim Kardashian and Justin Bieber are thinking or feeling on a given day, the state of mind of a party or witness is a common issue in many legal matters. (See Gordon v. T.G.R. Logistics, Inc. (D. Wy. May 10, 2017) (Court orders production of entire Facebook Account history as relevant to mental and emotional state of Plaintiff)).

And finally, arguably the most compelling social media evidence stems from the propensity to self-incriminate oneself on Twitter, otherwise known as a Statement Against Interest under FRE 804(b)(3).  This takes multiple forms, including flat out admissions of liability, or previous statements that contradict or otherwise impugn the integrity of a declarant. For instance:

Trump tweet

 

The bottom line is that social media provides a treasure trove of evidence that also tends to fall under evidentiary hearsay exceptions, unlike other forms of out of court statements.

But if you are offering social media evidence under a hearsay exception in court, that would likely mean you have an uncooperative or otherwise unavailable party who authored the social media statement in question. In such cases, the authenticity of the post must be established through circumstantial evidence since direct testimony is not available, and you will need the right software to both identify such evidence and properly collect it utilizing best practices to ensure its admissibility in court.

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Filed under Authentication, Best Practices, Case Law, Case Study, eDiscovery, Social Media Investigations

In-Place Data Analytics For Unstructured Data is No Longer Science Fiction

By John Patzakis

AI-driven analytics supercharges compliance investigations, data security, privacy audits and eDiscovery document review.  AI machine learning employs mathematical models to assess enormous datasets and “learn” from feedback and exposure to gain deep insights into key information. This enables the identification of discrete and hidden patterns in millions of emails and other electronic files to categorize and cluster documents by concepts, content, or topic. This process goes beyond keyword searching to identify anomalies, internal threats, or other indicators of relevant behavior. The enormous volume and scope of corporate data being generated has created numerous opportunities for investigators seeking deep information insights in support of internal compliance, civil litigation and regulatory matters.

The most effective use of AI in investigations couple continuous active learning technology with concept clustering to discover the most relevant data in documents, emails, text and other sources.  As AI continues to learn and improve over time, the benefits of an effectively implemented approach will also increase. In-house and outside counsel and compliance teams are now relying on AI technology in response to government investigations, but also increasingly to identify risks before they escalate to that stage.

Stock Photo - Digital Image used in blog

However, logistical and cost barriers have traditionally stymied organizations from taking advantage of AI in a systematic and proactive basis, especially regarding unstructured data, which, according to industry studies, constitutes 80 percent or more of all data (and data risk) in the enterprise. As analytics engines ingest the text from documents and emails, the extracted text must be “mined” from their native originals. And the natives must first be collected and migrated to a centralized processing appliance. This arduous process is expensive and time consuming, particularly in the case of unstructured data, which must be collected from the “wild” and then migrated to a central location, creating a stand-alone “data lake.”

Due to these limitations, otherwise effective AI capabilities are utilized typically only on very large matters on a reactive basis that limits its benefits to the investigation at hand and the information within the captive data lake.  Thus, ongoing active learning is not generally applied across multiple matters or utilized proactively. And because that captive information consists of migrated copies of the originals, there is a very limited ability to act on data insights as the original data remains in its actual location in the enterprise.

So the ideal architecture for the enterprise would be to move the data analytics “upstream” where all the unstructured data resides, which would not only save up to millions per year in investigation, data audit and eDiscovery costs, but would enable proactive utilization for compliance auditing, security and policy breaches and internal fraud detection.  However, analytics engines require considerable computing resources, with the leading AI solutions typically necessitating tens of thousands of dollars’ worth of high end hardware for a single server instance. So these computing workloads simply cannot be forward deployed to laptops and multiple file servers, where the bulk of unstructured data and associated enterprise risk exists.

But an alternative architecture solves this problem. A process that extracts text from unstructured, distributed data in place, and systematically sends that data at a massive scale to the analytics platform, with the associated metadata and global unique identifiers for each item.  As mentioned, one of the many challenges with traditional workflows is the massive data transfer associated with ongoing data migration of electronic files and emails, the latter of which must be sent in whole containers such as PST files. This process alone can take weeks, choke network bandwidth and is highly disruptive to operations. However, the load associated with text/metadata only is less than 1 percent of the full native item. So the possibilities here are very compelling. This architecture enables very scalable and proactive compliance, information security, and information governance use cases. The upload to AI engines would take hours instead of weeks, enabling continual machine learning to improve processes and accuracy over time and enable immediate action to taken on identified threats or otherwise relevant information.

The only solution that we are aware of that fulfills this vision is X1 Distributed GRC. X1’s unique distributed architecture upends the traditional collection process by indexing at the distributed endpoints, enabling direct pipeline of extracted text to the analytics platform. This innovative technology and workflow results in far faster and more precise collections and a more informed strategy in any matter.

Deployed at each end point or centrally in virtualized environments, X1 Enterprise allows practitioners to query many thousands of devices simultaneously, utilize analytics before collecting and process while collecting directly into myriad different review and analytics applications like RelativityOne and Brainspace. X1 Enterprise empowers corporate eDiscovery, compliance, investigative, cybersecurity and privacy staff with the ability to find, analyze, collect and/or delete virtually any piece of unstructured user data wherever it resides instantly and iteratively, all in a legally defensible fashion.

X1 displayed these powerful capabilities with ComplianceDS in a recent webinar with a brief but substantive demo of our X1 Distributed GRC solution, emphasizing our innovative support of analytics engines through our game-changing ability to extract text in place with direct feed into AI solutions.

Here is a link to the recording with a direct link to the 5 minute demo portion.

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Filed under Best Practices, collection, compliance, Corporations, eDiscovery & Compliance, Enterprise eDiscovery, Enterprise Search, GDPR, Uncategorized