Disclosure: This article is published by Datamagnet. Vendor claims are self-reported unless otherwise noted.
New content_filters Parameter: Keyword-Filter LinkedIn, Reddit, and X Signals
Datamagnet just shipped a content_filters parameter on the Signals API, and it solves a problem every GTM team monitoring social platforms already knows: too many alerts, not enough relevant ones. You can now keyword-search LinkedIn, Reddit, and X (Twitter) signals at query time, so noise gets cut before it ever reaches your inbox.
TL;DR
content_filterslets you pass include/exclude keyword lists that run against raw post and comment content on LinkedIn, Reddit, and X — before ICP scoring even starts.- Social outreach already gets a 42% response rate versus 26% for email and 23% for phone (HubSpot, 2025 State of Sales Report), so the channel is worth monitoring closely.
- 32% of software buyers have used Reddit to research vendors, versus 23% of B2B decision-makers overall (SurveyMonkey & Reddit, "The Hidden B2B Journey," March 2026) — a real reason to filter Reddit signals, not just LinkedIn.
- No extra credits:
content_filtersis included in the cost of the underlying signal type.
What Happened?
Datamagnet added content_filters to the Signals API this week, extending keyword engagement signals beyond LinkedIn to Reddit and X. The parameter accepts include and exclude keyword lists, evaluated against post and comment text at query time — not against a lead's profile after the fact.
Key facts:
content_filtersships onkeyword_engagementsignals today, across all three supported platforms.- Filtering runs before ICP scoring, so irrelevant content never reaches the scoring stage.
- The change is backward compatible — existing signals keep working without it.
Most "signal" tools filter after a lead is matched — by job title, seniority, or company size. content_filters filters the source content itself first, which is a different failure mode entirely: it stops a mention of your product name in an unrelated context (a job posting, a meme, a customer complaint about someone else) from ever becoming a lead in the first place.
Why Does This Matter?
content_filters matters because social platforms are where B2B buying conversations already happen, and most teams are still monitoring them by hand. Social media outreach earns a 42% response rate, compared to 26% for email and 23% for phone (HubSpot, 2025 State of Sales Report). That gap is why sales teams keep adding social channels to their signal stack — and why the noise problem keeps getting worse.
In 2026, Reddit's business team and SurveyMonkey jointly found that 83% of B2B decision-makers research vendors on their own before ever talking to sales, and they trust peer recommendations (73%) far more than social media in general (36%) (SurveyMonkey & Reddit, "The Hidden B2B Journey," March 2026). That's the tension content_filters is built to resolve: the channels are valuable, but only the specific mentions that match your keywords are worth a rep's attention.
What Does This Mean for GTM Teams?
For GTM teams already monitoring LinkedIn, Reddit, and X, the immediate impact of content_filters is fewer, more relevant alerts across every platform you track under one signal setup. Social outreach alone earns a 42% response rate versus 26% for email (HubSpot, 2025 State of Sales Report), so cutting noise here directly protects rep time on the channel that already converts best. Here's how that plays out day to day, broken into three concrete impacts below.
Impact 1: Competitor mentions get isolated automatically
Instead of scanning a raw keyword feed for your competitor's name, content_filters lets you require terms like "switching from" or "migrating off" alongside the competitor name, so you only see posts that signal active dissatisfaction — a pattern already covered in the Competitor Hijack cookbook.
Impact 2: Technology-adoption chatter stops burying real leads
Exclude lists strip out the noise that always rides along with a product-category keyword — job postings, recruiting spam, unrelated news roundups — before it counts against your credit usage or clutters a webhook payload.
Impact 3: Reddit becomes worth monitoring, not just LinkedIn
Reddit now sees 121 million daily active users, up 19% year over year, and 32% of software buyers specifically say they've used it to research a vendor (SurveyMonkey & Reddit, 2026). Without keyword filtering, monitoring Reddit at that volume for a niche product category isn't practical for a small team — content_filters makes it one config change instead of a manual subreddit crawl.
The Datamagnet product team built this after watching support tickets pile up from customers running keyword engagement signals that fired on every loose mention of a broad term. The fix wasn't smarter scoring — it was letting customers define what "irrelevant" meant before scoring even ran.
How Does content_filters Work?
content_filters works by evaluating raw post and comment text against your include and exclude keyword lists before ICP scoring ever runs, so irrelevant content never reaches the scoring stage. A content_filters object sits alongside your existing signal configuration on signal creation or signal update calls. It accepts include_keywords, exclude_keywords, a match_type of "any" or "all", and an optional platforms array, shown in the example below:
{
"signal_type": "keyword_engagement",
"keywords": ["cold outreach automation", "sales engagement platform"],
"platforms": ["linkedin", "reddit", "twitter"],
"content_filters": {
"include_keywords": ["competitor migration", "switching from"],
"exclude_keywords": ["job posting", "hiring", "we're hiring"],
"match_type": "any",
"case_sensitive": false
},
"icp": {
"job_titles": ["VP Sales", "Head of Revenue"],
"seniority_levels": ["vp", "c_level"]
},
"webhook_url": "https://yourapp.com/webhooks/datamagnet"
}
The distinction from the existing mandatory_keywords and negative_keywords fields on your ICP filter matters: ICP keyword fields evaluate a lead's profile text after a match is found. content_filters evaluates the post or comment content itself, before a lead is ever generated. Use both together for the tightest possible funnel — content filtering to catch the right conversation, ICP filtering to confirm the right person is having it.
What Should You Do Now?
If you're already running signals on Datamagnet, content_filters is a low-risk addition since it costs no extra credits and works alongside your existing ICP filters without recreating any signal. Here are four concrete steps to take this week, ordered from immediate to ongoing, so you can start cutting noise on your highest-volume signal today rather than waiting for a full rollout.
- Today: Add
content_filtersto any existing keyword engagement signal that's producing more noise than leads — start with anexclude_keywordslist of the false positives you already recognize by pattern. - This week: Extend a LinkedIn-only signal to Reddit and X by adding both platforms to the
platformsarray, then check your credit balance after a run to confirm volume is manageable. - This month: Pair
content_filterswith an ICP filter for competitor-mention tracking — see the Competitor Hijack cookbook for a ready-made pattern, or explore the full Signal API for other monitor types. - Ongoing: Review your
exclude_keywordslist monthly. Language on Reddit and X shifts faster than LinkedIn's more formal tone, so a filter that worked in January can quietly stop matching by summer.
Do NOT:
- Set
match_type: "all"on a long include list expecting broader coverage — it narrows results, since every keyword must appear together. - Assume Reddit and X signals need the same keyword list as LinkedIn. Platform tone differs enough that a separate, shorter list usually performs better.
The Bigger Picture
content_filters is part of a broader shift in buying-signal tools: from "monitor everything and sort later" to "define relevance before the data even arrives." Datamagnet's own July 2026 changelog shows the same pattern in People and Company enrichment — optional, filtered data instead of one-size-fits-all payloads.
Expect keyword-level filtering to keep spreading across the signal stack — industry, company, and person engagement signals are natural next candidates, since they share the same underlying content pipeline as keyword engagement signals today.
Frequently Asked Questions
What is the content_filters parameter?
content_filters is a new object on Datamagnet's Signals API that accepts include and exclude keyword lists, evaluated against raw LinkedIn, Reddit, and X post or comment content at query time — before ICP scoring runs, not after a lead is already matched.
Which platforms does content_filters support?
LinkedIn, Reddit, and X (Twitter) today, on keyword_engagement signals. Reddit joins this release partly because 32% of software buyers already research vendors there, versus 23% of B2B decision-makers overall (SurveyMonkey & Reddit, 2026).
How is content_filters different from ICP mandatory_keywords?
mandatory_keywords on your ICP filter checks a lead's profile text after a match. content_filters checks the post or comment content before a lead exists. Combine both for the tightest funnel: content relevance first, then person relevance.
Does content_filters cost extra credits?
No. content_filters is included in the cost of the underlying keyword_engagement signal — you pay the same rate whether or not you add filtering, and tighter filtering typically reduces the number of low-value leads you're charged for downstream.
Can I add content_filters to a signal I already have running?
Yes. Use the signal update endpoint with action: "update_targets" to add or change content_filters on any existing keyword_engagement signal without recreating it.
Sources
- The Hidden B2B Journey - SurveyMonkey & Reddit, March 2026
- 2025 State of Sales / Sales Trends Report - HubSpot, September 2025
- Signals API - Signal Creation - Datamagnet, retrieved 2026-07-20
- Keyword Engagement Signal - Datamagnet, retrieved 2026-07-20
- Changelog: July 2026 - Datamagnet, retrieved 2026-07-20
