Ruin Life Tactics - Data Mining 02

Ruin Life Tactics
Data Mining Lesson 02: Target Name

Introduction:

If you've read the previous post you'll probably be asking yourself why I've bothered to cover this seemingly redundant topic: "I already (should) have a name because I've been stalking online monikers for the past hour!" Yes, by now you probably do, but some people are definitely more secretive than others. There are some occasions where a person will only reveal a partial name or will use an online only pseudonym. This can be extremely frustrating when trying to connect points of data only to realize halfway through that you've somehow entirely fucked up the nomenclature.

The other possibility is that this person has a fairly common name such as Jones, Smith, or Johnson. You might also be attempting to establish familial ties for verification purposes with someone that has an unusual last name shared by a several people that are only distantly related. For example, a person I happened to examine with Greek ancestry presented a problem. This person had a fairly unique first name shared with one other person and a fairly large collection of distant (irrelevant) relations that made certain geographical correlations sketchy and inaccurate.

This article will seek to cover all of the above.


Victim Name:

What you'll want to do is consider all of the statistical information you've been able to gather about your target and lay it out. You can use specialized tools for this but I prefer to cover all of this in Notepad++; my text editor of choice. I use + and - signs to separate likely matching points of data from unlikely, * for designating emphasis or significance and the various marking and highlighting tools to group information.

Section I

Assuming that you have no name or a partial name:

First, collect all the information you've gathered directly from the target. Sift through this and categorize by geological data, hobbies, interests, skills, tasks, responsibilities, political/personal outlook, ad nauseum. The more you can categorize and assign groups to this data the more you are able to define the target as a set of statistics and stereotypes. This is an essential skill, and it must be learned.

Second, try to do the same for all of the data you've collected from sources external to the target (or from a search you are currently performing) and try to filter out the data that is unlikely to be applicable. Save this data, because you might need it later. I keep it within the same file I'm using until I have a confirmation- remember, people change over the years so unlikely might not mean irrelevant.

Now take all of this matching data and focus on anyone related to this data in your search. While people are generally a hodgepodge of stereotypes the weight, function, and prevalence of these differs uniquely per person. Another thing to consider is that people often tend to clique together and form social groups based on similar views and interests. Finding people that closely match these data clusters in your search opens the possibility that they might know your target.

EXPLOIT THIS POSSIBILITY!

Using your disposable identities and crafting your online presence to become more favorable to these people will allow you to create a network that you can use to ensnare your target and lower their predisposition toward obfuscation. People are often more likely to disclose personal information when they feel surrounded by peers that share converging interests- it makes them feel safe. On a related note you might find a person that will blab to no end about your target and give you all the information you need.

Section II

Assuming that you have a name that is common or problematic:

Sometimes a problem arises when you have a name that for one reason or another is indistinct from a large group of others. Jonathon Smith, Michael Jones, Paula Johnson- these are just an inkling of very common names. A far easier form of this is when you have a regionally unique surname with multiple first names that match. The former is much harder to trace, while the latter groups tend to collect in certain cities across the continent as family groups have gradually been dispersed over the years.

The same methods are used in both cases- follow the names you have with the information used in the first section to assist you in your cross-examination of the list of names you'll collect from various databases.

You'll want to start with a basic search at Intellius, USSearch* or a similar engine and simply record all of the results. You'll sometimes be lucky enough to obtain the ages and relatives of a few of these people- this is important. While people may share the same name, the likelihood of being born on the same year, month or day grows further apart as the time frame narrows. People with the exact same name don't often live in the same city, share matching relatives, etc.

*Do not under any circumstances pay for this data. It's already free and in the public domain. You'll also leave a trail pointing to your financial data. <-very easy to trace!

Start by grouping this new data as directed above. The people listed in the data will grow more distinct as you begin to sort through your list. Keep irrelevant data as it will help you distinguish false positives in your online research. When looking for a 31 year old male it's nice to know that a 66 year old match is the only person that's lived in Seattle, so any information relating to Seattle can be tossed aside.

Now that you have a list of names and locations it's time to explore public records. These are the most important sources, so make sure you write these down:

County Clerk of Courts: Land, sometimes criminal records, marriage records, occasional family court data

County Assessor: Definitive land info, address, aerial photos.

County Appraiser: The same as above, but look for either.

State Department of Commerce/Corporations: Business info, fictitious names, registered agents (Addresses!)

Phone books: Name, number, address (somewhat unreliable)

Wikipedia: Crap, but useful for finding out which county a city is in. I'm almost embarrassed to add this.

I’ll cover more of the above sites in upcoming articles, but this mostly concludes the name portion of the series. Remember to always compare new data with existing data as you receive it, and continually narrow and refine your search parameters as you close in on your target.