# Hashtag Trend Radar - Rising & Emergent Tags (`yzy001/fediverse-hashtag-trends`) Actor

Find hashtags that are rising right now across the fediverse, not just a trending list. Each tag carries seven days of daily uses and accounts, a spike ratio, an emergent flag for tags with no prior history, and a cross-instance count that separates network trends from single-server noise.

- **URL**: https://apify.com/yzy001/fediverse-hashtag-trends.md
- **Developed by:** [宗諭 楊](https://apify.com/yzy001) (community)
- **Categories:** Social media, Business, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 hashtags

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Hashtag Trend Radar — Rising & Emergent Tags

Most hashtag tools hand you a trending list. This one tells you **which tags are actually
rising**, and separates a network-wide trend from one server's local noise.

Every tag arrives with **seven days of daily uses and accounts**, so the rise is computed,
not guessed:

| Field | What it answers |
|---|---|
| **`spikeRatio`** | today's uses ÷ the previous six days' mean |
| **`isEmergent`** | zero uses in the previous six days — a tag that did not exist until now |
| **`trend`** | `emergent` / `rising` / `steady-high` / `peaked` / `declining` |
| **`peakDayIndex`** | 0 means still at the top; higher means it already peaked |
| **`instanceCount`** / `isCrossInstance` | how many servers saw it — the network-vs-local test |
| `historyUses` / `historyAccounts` | the whole daily series, newest first |
| `usesPerAccountToday` | high means a few accounts posting a lot, not broad adoption |

Two dataset views: **Rising now** (ranked, emergent first) and **Daily series** (the shapes).

### The one thing a seven-day window cannot do

The source gives **seven days of history and no more**. That means **weekly ritual tags look
exactly like trends**. Measured on 14 of the largest servers:

| Tag | Daily uses (newest first) | What it really is |
|---|---|---|
| `#caturday` | `852, 7188, 626, 285, 431, 252, 313` | every Saturday |
| `#silentsunday` | `6762, 216, 96, 79, 66, 51, 631` | every Sunday |
| **`#fragrantplaces`** | **`2824, 0, 0, 0, 0, 0, 0`** | **genuinely new** |
| `#curiousabook` | `25, 2691, 0, 0, 0, 0, 0` | appeared yesterday |

A spike ratio alone promotes `#caturday` every weekend. **`isEmergent` is the one signal a
ritual cannot fake**, because a recurring tag never has an all-zero history. If you want
"risen in the last few days", filter on `emergent`.

### Spike ratios need an absolute floor — and the floor is smaller than you think

A tag going from 1 use to 20 has a spike ratio of 20 and means nothing. So pair the ratio
with `minPeakUses`, `minUsesToday` or `minAccountsToday`.

But mind the scale. Measured across 14 of the largest servers:

| | single server | aggregated |
|---|---|---|
| median daily uses | 7 | 23 |
| p90 | 48 | 189 |
| p99 | — | 2,691 |
| **max** | **666** | **7,188** |

Of 58 tags: **24 cleared a 100 peak, 12 cleared 200, 7 cleared 500, and none cleared
10,000.** The whole network has roughly **3 million** users, so an Instagram-scale cutoff
returns an empty result rather than "the big trends". For genuinely large trends, try
`minPeakUses` between **200 and 500** — and every run reports `observedScale` with the
distribution it actually saw, so you can calibrate against live data instead of these
numbers.

`minAccountsToday` is the harder floor to game: one account can post a tag many times, but
it cannot be many accounts.

### Cross-server confirmation

The network is federated, and each server shows trends from its own vantage point. Measured
across 9 servers: 46 distinct tags, **3 appeared on all 9, and 14 appeared on only one**.
A tag on one server is a local phenomenon; `isCrossInstance` marks three or more.

Pick servers by size (`topInstances`), or by `instanceLanguage` / `instanceCategory` to
watch a specific language or community — the official directory lists **298 servers**, 175
with user counts totalling about 3 million.

### Tracking your own tags

`trackTags` follows specific hashtags whether or not a server has them trending. Those go
through the single-tag endpoint, so they are not limited to each server's curated list — a
trending list is partly editorial, and "not on the list" is not the same as "nobody is using
it".

### How it checks itself

| Check | What it catches |
|---|---|
| Too many servers failed | Below half responding, the aggregate no longer represents the network, so the run stops rather than presenting a thin sample as a network trend. |
| Servers answered but no tags | The trends endpoint changed shape. Reported as a structural problem, never as "no trends today". |
| History shape changed | The seven-day series is the denominator of every rise metric. A tag whose history loses its shape is skipped; if the directory itself is unavailable the run stops. |
| Unknown trend filter | Rejected rather than silently ignored. |

### Notes on the data

- Aggregation **sums** each server's daily figures. Federated views overlap, so the totals
  are an **upper bound**, not a de-duplicated count. Compare tags against each other rather
  than treating the number as absolute volume.
- Trending lists are curated by each server, often with moderation.
- This is a trend-detection tool. It returns tags and counts, not post content.

# Actor input Schema

## `topInstances` (type: `integer`):

How many of the largest fediverse servers to poll, taken from the official directory (298 servers, 175 with user counts totalling about 3 million). More servers means better cross-server confirmation and more requests.

## `instances` (type: `array`):

Poll these servers instead of the largest ones, for example mastodon.social or infosec.exchange. Overrides the server count above.

## `instanceLanguage` (type: `string`):

Only poll servers whose primary language is this, for example en, ja or de. The default selection leans English and general-purpose.

## `instanceCategory` (type: `string`):

Only poll servers in this category, for example tech, art, journalism or regional.

## `minInstanceUsers` (type: `integer`):

Skip servers with fewer registered users than this.

## `trackTags` (type: `array`):

Follow specific hashtags whether or not they are trending. These go through the single-tag endpoint, so they are not limited to each server's curated trending list.

## `onlyTrends` (type: `array`):

Keep only tags in these states. 'Emergent' means no uses at all in the previous six days - that is the one state a weekly ritual tag cannot fake.

## `minInstances` (type: `integer`):

Only keep tags seen on at least this many servers. Measured across 9 servers: 14 of 46 tags appeared on only one server, which makes them local noise rather than network trends.

## `minUses7d` (type: `integer`):

Drop tags with fewer total uses than this across the seven days.

## `minPeakUses` (type: `integer`):

Absolute floor on the busiest single day. A spike ratio alone promotes a tag that went from 1 use to 20, so pair it with this. Mind the scale: measured across 14 of the largest servers, aggregated daily uses run p50=23, p90=189, p99=2691 and max 7188 - a single server peaks at 666. Of 58 tags, 24 cleared 100, 12 cleared 200, 7 cleared 500 and NONE cleared 10000. The whole fediverse has roughly 3 million users, so Instagram-scale cutoffs return nothing. For genuinely large trends try 200 to 500; every run reports the distribution it actually saw.

## `minUsesToday` (type: `integer`):

Absolute floor on today's uses. Use this when you only care about what is large right now rather than at any point in the window.

## `minAccountsToday` (type: `integer`):

Absolute floor on distinct accounts using the tag today. Harder to game than uses - one account can post many times, but it cannot be many accounts.

## `tagsPerInstance` (type: `integer`):

How many trending tags to take from each server.

## `maxResults` (type: `integer`):

Maximum number of tag rows to return after ranking.

## `delaySeconds` (type: `number`):

Pause between server requests. These are small public endpoints, so keep it polite.

## Actor input object example

```json
{
  "topInstances": 12,
  "instances": [],
  "minInstanceUsers": 0,
  "trackTags": [],
  "onlyTrends": [],
  "minInstances": 2,
  "minUses7d": 0,
  "minPeakUses": 200,
  "minUsesToday": 0,
  "minAccountsToday": 0,
  "tagsPerInstance": 20,
  "maxResults": 500,
  "delaySeconds": 0.4
}
```

# Actor output Schema

## `tags` (type: `string`):

Tag, trend state, spike ratio, emergent flag, uses and accounts today and over seven days, the full daily series, and how many servers saw it.

## `summary` (type: `string`):

Servers polled and which failed, trend-state and cross-server distributions, the emergent tag list, and the seven-day limit and ritual caveats.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "topInstances": 12,
    "minInstanceUsers": 0,
    "minInstances": 2,
    "minUses7d": 0,
    "minPeakUses": 200,
    "minUsesToday": 0,
    "minAccountsToday": 0,
    "tagsPerInstance": 20,
    "maxResults": 500,
    "delaySeconds": 0.4
};

// Run the Actor and wait for it to finish
const run = await client.actor("yzy001/fediverse-hashtag-trends").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "topInstances": 12,
    "minInstanceUsers": 0,
    "minInstances": 2,
    "minUses7d": 0,
    "minPeakUses": 200,
    "minUsesToday": 0,
    "minAccountsToday": 0,
    "tagsPerInstance": 20,
    "maxResults": 500,
    "delaySeconds": 0.4,
}

# Run the Actor and wait for it to finish
run = client.actor("yzy001/fediverse-hashtag-trends").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "topInstances": 12,
  "minInstanceUsers": 0,
  "minInstances": 2,
  "minUses7d": 0,
  "minPeakUses": 200,
  "minUsesToday": 0,
  "minAccountsToday": 0,
  "tagsPerInstance": 20,
  "maxResults": 500,
  "delaySeconds": 0.4
}' |
apify call yzy001/fediverse-hashtag-trends --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,yzy001/fediverse-hashtag-trends"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/KjcJ81ovfI7o6cSLF/builds/JhhFU9CMiecOIGrul/openapi.json