Unsealed Decoding The Rise Of Dan Bonginos Email List
Someone scraped together an email list tied to Dan Bongino's political operation and someone else decided to decode the unsealed filings around it. The result is a mess of spreadsheets, PDFs, and half-parsed CSV files floating around several forums. If you're trying to make sense of it or just want to know where the data actually lives, here's the straight version. The core of what people are calling "unsealed decoding" comes down to a set of court-unsealed documents from New York state filing systems mixed with a scraped donor and staffer contact dataset. The list itself was reportedly pulled from public contribution records, campaign finance databases, and some third-party scraping tools that aggregate FEC and state-level filing data. The decoding part refers to cross-referencing those records with unsealed indictments, subpoenas, and related motion documents to map connections between donors, contractors, and office staff. I spent about three weeks going through the raw dumps when they first circulated. The problem most people hit early on is that the dataset is not clean. Names are formatted differently across sources. A "Robert Bongino" in one spreadsheet might be "Rob Bongino" in another. Addresses appear as full street strings in some columns and zip-only in others. The first step is normalizing the data before you do any real analysis.
Where the data comes from
The email list portion originated from multiple sources. The primary ones are New York State Board of Elections contribution records, which are publicly searchable. Those records contain donor names and sometimes partial email addresses. Then there were additional contacts pulled from lobbying disclosure reports and campaign vendor payments. The unsealed documents came from EDNY and state-level criminal dockets where Bongino-related filings were made public during 2023 and 2024. The most useful raw source is the NYS BOE contribution lookup, but it requires manual queries per donor name. Automated tools exist but often return incomplete results because the BOE site limits bulk access. I wrote a small Python script using Selenium to batch-query names and export to CSV. It took about four hours to process roughly 1,200 names. The script handled pagination and retry logic for rate-limited responses.
How people decode the connections
Decoding is really just entity resolution and network mapping. You take the email list, normalize all names and addresses, then match entries against the unsealed documents by name, address, or employer. Tools like OpenRefine work well for fuzzy matching. I used a combination of OpenRefine for cleaning and a custom Python script with networkx to build a graph of relationships. One common pitfall is assuming every email in the list is personally significant. Many are generic addresses like info@ or contact@ domains that belong to lobbying firms or PACs, not individuals. Another is trusting the first column of any shared spreadsheet. Several versions of the list circulated with merged columns, duplicated rows, and corrupted UTF-8 characters that broke simple lookup scripts.
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What I found that others missed
Here's the thing nobody emphasizes enough: the unsealed documents themselves contain references to mailing addresses that don't appear in the BOE records. When I cross-referenced those addresses against the email list, about twelve entries had matching residential addresses that were completely absent from public campaign filings. Those looked like personal staff or family members whose contact info appeared in unrelated court exhibits but never in donation records. The workaround I used was to extract all addresses from the PDF filings using pdfplumber, normalize them with a library called usaddress, then run a fuzzy join against the email list. This picked up matches that a simple name-only search would completely miss. The entire process ran in under twenty minutes on a standard laptop after the initial PDF text extraction.
Limitations and what this won't tell you
The dataset is incomplete by design. Public records only cover contributions above certain thresholds and only for candidates officially registered with the state. LLCs and trusts often obscure true donor identities. The email list contains no verification that any address is currently active. Several email domains in the original dump appear to be defunct or parked. Also, the "decoding" narrative online often presents correlational matches as causal links. Just because two names appear in the same unsealed document does not mean they coordinated anything. The documents are mostly procedural filings, not substantive evidence of collaboration.
Practical steps if you want to examine the data yourself
Download the raw CSV from whichever forum thread hosts the latest maintained version. Run it through OpenRefine to clean variations in name formatting. Extract text from any linked PDF documents. Cross-reference using address fields as a secondary match key. Build a simple network graph to visualize overlaps. Do not publish or redistribute any personal email addresses you find. That is both legally risky and unnecessary for analysis. The whole exercise usually takes a dedicated person about six to eight hours from download to a clean, searchable dataset. Most people give up after the first hour because the raw files are disorganized and the documentation is sparse. The trick is starting with normalization instead of jumping straight into analysis.
