Every Risk Label in Your Report, Explained

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canonical_url: https://digital-footprint-health.shop/blog/risk-labels-explained
title: Every Risk Label in Your Report, Explained
tags:

• twitter
• privacy

- security

The first time you open a footprint report, the tag wall is real: phone, email, address, location, sensitive, identity, all in different colors. I stared at mine the same way. This post is what I wish someone had told me then. Labels aren't verdicts, they're a map. Each one has a trigger, a risk level, and an action.

phone and email are the shortest paths to you. The trigger is a regex match: an 11-digit string, or a standard email pattern. Risk is high because contact details are how a stranger reaches you directly. One edge case matters: if someone else posted the number and you reposted it, it still counts, since your account's association ties that number to you. Verify the original text, then delete.

address vs location is a real distinction. address is home-type info: door numbers, neighborhood names, a street plus a city. Direct exposure, near P0. location is broader: check-in geotags, your office, places you frequent. One post is harmless, fifty are a movement map. That's why the report ranks address above location when both fire on the same tweet.

sensitive needs a human. It catches politically sensitive remarks, region or gender jokes, attack-style comments. The label isn't saying you were wrong. It's saying the tweet, ripped from context, travels really well as a screenshot. A rant aimed at a friend reads like a different story when it circulates on its own. Medium-high risk, and the fix is reading the original and deciding yourself.

identity fires on combinations, not single matches. Real name plus company. A personal email built on your real name. Birth date with a scanned ID. One data point is limited, several together pinpoint you. When identity shows up, treat it like P0 and check each hit individually.

Labels stack. "Third day at Acme, here's my badge" carries identity (name plus badge photo) and possibly location if the office is geotagged. The report grades the whole tweet by its highest-risk label, so combined risks don't slip through when you sort by tier.

False positives happen. A birthday starting with 111 can match the phone pattern. That's why every label links back to the original tweet for confirmation, which is also why "no label" doesn't mean safe. The regex only catches what a machine can recognize. A coworker's full name dropped in passing won't trigger anything.

The scoring side is worth understanding too, because it's what turns a list of tags into a work order. Each tweet gets a per-label risk weight, and the weights compound when multiple labels fire on the same post. A reposted number with a name attached scores higher than a standalone number, because both reach and identifiability are higher. The report then grades the whole tweet by its highest-risk label, so a post tagged identity and location gets treated as identity, which is the tier you'd want to act on first. That single rule, grade by the worst label, is what keeps combined risks from sliding past a tier-based sweep.

When you integrate this into a routine rather than a one-off purge, the labels become a feedback loop. After a cleanup, the categories that keep coming back tell you what to stop posting. If location keeps showing up, you stop checking in. If sensitive keeps showing up, you start thinking before you joke. The report isn't just a cleanup tool, it's a list of the habits that produced the exposure in the first place.

The reference table and FAQ: https://digital-footprint-health.shop/blog/risk-labels-explained