# Ameba Post Likes Scraper (`scrapingmonkey/ameba-post-likes-scraper`) Actor

Export public users who liked Ameba posts with author names, blog titles, profile images, and available like timestamps.

- **URL**: https://apify.com/scrapingmonkey/ameba-post-likes-scraper.md
- **Developed by:** [ScrapingMonkey](https://apify.com/scrapingmonkey) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

**Ameba Post Likes Scraper** — Export public users who liked Ameba posts with author names, blog titles, profile images, and available like timestamps. Add supported inputs and start a run to get structured public data without providing a Ameba account.

Export public like records for an Ameba staff announcement. Keep account links and available reaction timestamps to research the visible audience.

| At a glance | Details |
|---|---|
| 📥 Input | Enter full public Ameblo post URLs. Each page contains up to 10 like records. |
| 📤 Output | One like per success row, with source input retained |
| 📄 Pagination | `pagesPerPost` limits result pages per input, with a default of 1. The source may end sooner. |
| 🔐 Login required | No |
| ⚡ Processing | Up to 5 HTTP requests concurrently with automatic retries |
| 💾 Delivery | One Apify dataset view, useful flat columns and complete nested JSON |

### What the ameba post likes scraper collects 📊

Export public users who liked Ameba posts with author names, blog titles, profile images, and available like timestamps.

Data can include:

- Source identity and public links
- Available id, ameba id, url, post id, post url, source ameba id, nickname, blog title
- Original input retained with every result
- One consistent success or failed record format

### How to collect likes from Ameba 🚀

1. Enter one or more supported inputs in `inputList`.
2. Set the result-page budget for each input.
3. Start the Actor.
4. Open the **Likes** dataset view and review the individual records.
5. Export the dataset or retrieve it from your application.

```json
{
  "inputList": [
    "https://ameblo.jp/staff/entry-12590320297.html"
  ],
  "pagesPerPost": 1
}
```

`pagesPerPost` limits result pages per input, with a default of 1. The source may end sooner.

### Ameba likes data and complete output 📦

| Field | Type | Meaning |
|---|---|---|
| `input` | string | Original submitted input. |
| `status` | string | Result status: success or failed. |
| `id` | string or null | Stable identifier of this result. |
| `ameba_id` | string or null | Ameba blog ID / username. |
| `url` | string or null | Public result URL. |
| `post_id` | string or null | ID of the requested source article. |
| `post_url` | string or null | URL of the requested source article. |
| `source_ameba_id` | string or null | Ameba ID of the source blog. |
| `nickname` | string or null | Public author display name. |
| `blog_title` | string or null | Public blog title. |
| `profile_image_url` | string or null | Public profile image URL. |
| `liked_at` | string or null | Like timestamp when supplied by Ameba. |
| `valid_account` | boolean or null | Whether the liker account is still valid according to Ameba. |

Nested arrays and media references stay in their parent record. Downloading source media files is not part of this Actor.

Every top-level output field appears in these complete examples. The success example is normalized from a public response. Long strings and arrays are shortened here; the Actor keeps the complete available values. Values are a snapshot and can change. Nested arrays remain inside their parent result.

Representative success result with every output field (long text and arrays shortened for readability):

```json
{
  "input": "https://ameblo.jp/staff/entry-12590320297.html",
  "status": "success",
  "id": "naokoma0815",
  "ameba_id": "naokoma0815",
  "url": "https://ameblo.jp/naokoma0815/",
  "post_id": "12590320297",
  "post_url": "https://ameblo.jp/staff/entry-12590320297.html",
  "source_ameba_id": "staff",
  "nickname": "naokoma",
  "blog_title": "ヨシダ　ナオミ　〜 意識を変えて人生を良くする！〜",
  "profile_image_url": "https://stat.profile.ameba.jp/profile_images/20191105/20/40/8g/j/o08040804p_1572951621976_ajqus.jpg",
  "liked_at": "2025-05-04T09:41:27.112Z",
  "valid_account": true
}
```

Complete failed result:

```json
{
  "input": "invalid input",
  "status": "failed",
  "id": null,
  "ameba_id": null,
  "url": null,
  "post_id": null,
  "post_url": null,
  "source_ameba_id": null,
  "nickname": null,
  "blog_title": null,
  "profile_image_url": null,
  "liked_at": null,
  "valid_account": null
}
```

A failed row preserves `input`, sets `status` to `failed`, and sets every other top-level field to `null`. The run log records the reason. Optional successful fields can be null or empty when Ameba does not supply them. Object fields are exposed through useful columns in the single **Likes** view; arrays are not expanded into extra result rows.

### Input requirements and pagination settings ⚙️

| Parameter | Type | Required | Default | Rules |
|---|---|---|---|---|
| `inputList` | array of strings | Yes | None | Enter full public Ameblo post URLs. Each page contains up to 10 like records. At least `1` item. |
| `pagesPerPost` | integer | No | `1` | Maximum result pages per input. Setup requests do not count as pages. Stops when the source has no next page or repeats a continuation. Actual rows per page can vary. Minimum `1`. |

Enter full public Ameblo post URLs. Each page contains up to 10 like records.

Examples of supported inputs: `https://ameblo.jp/staff/entry-12590320297.html`.

Duplicate normalized inputs are processed once. Within the collection for one source input, repeated records are removed while distinct interactions or objects remain separate. The same object returned for different source inputs retains its source relationship.

The first result page counts as page 1. Public-page preparation and metadata lookups do not consume result pages. Collection stops at your page budget or the end of the available list. A short page alone does not mean that the list has ended.

### Ameba likes use cases 🎯

#### Collect likers of an Ameba announcement

Export public like records for an Ameba staff announcement. Keep account links and available reaction timestamps to research the visible audience.

#### Review recent visible likes on an Ameba post

Collect the first page of public likes on an Ameba article. Keep timestamps and account validity flags to review the latest returned reactions.

#### Compare visible likers of Ameba articles

Export public like records from selected Ameba articles. Keep source posts and account identifiers for your own audience comparison across the articles.

### Pricing and saved-result behavior 💰

Check the Actor’s **Pricing** tab for the active pricing model and current rate. Store settings can change, so this README does not claim a fixed price or runtime.

Under dataset-item pricing:

- Each successful result represents one like for its source input.
- An invalid or unavailable input, an input with no accessible results, or an exhausted request can produce a `failed` row.
- Retry attempts and supporting requests do not create extra dataset rows by themselves.
- Nested media, profile information and other arrays remain part of their parent row.
- A normal empty continuation after earlier successes creates no additional row.
- Saved failed rows are not assumed to be free; check their treatment in the active pricing configuration.

More inputs and larger page budgets can produce more saved rows. Start with a small run and check actual usage before increasing the workload.

### Ameba likes API 🔌

Replace `$ACTOR_ID` with the identifier shown in this Actor’s **API** tab and `$APIFY_TOKEN` with your Apify token.

```bash
curl -X POST "https://api.apify.com/v2/acts/$ACTOR_ID/runs?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputList":["https://ameblo.jp/staff/entry-12590320297.html"],"pagesPerPost":1}'
```

Download the dataset in JSON, CSV, Excel or other formats available in the Console, or retrieve it through the Apify API. Use schedules, webhooks and Apify integrations to connect results to Google Sheets, Make, Zapier, cloud storage or your own application. These connections are configured separately by the user.

### Reliability, retries, and public-data limits ⚠️

Collection uses pure HTTP with up to five concurrent requests. Invalid syntax and confirmed missing, removed or unavailable targets stop without unnecessary retries. Temporary network or proxy failures, timeouts, blocking responses, malformed data, throttling and server errors allow up to five total attempts per request.

Successful earlier pages remain saved if a later request fails. A normal end after saved results is not retried. An input with no accessible results can receive a failed row; that alone does not prove that its underlying source does not exist. An exhausted later request can add a failed row while preserving previous output.

Only publicly returned like records are available. Removed accounts can have valid\_account=false and missing profile fields.

Ameba controls public availability, ranking and optional fields. Result counts can be affected by duplicates, removed content and changes during a run. A page budget is a collection limit, not a promise of exhaustive coverage.

An invalid string within a valid input list does not stop other inputs. Invalid overall configuration, such as a non-string list item or a wrong page-count type, exits before source requests. Startup failures, unavailable dataset storage or an unrecoverable result-save error can still stop the whole run. A failed save is not retried as a new scraping request.

### Frequently asked questions ❓

#### What does one Ameba Post Likes Scraper result represent?

Each successful row represents one like. The source input is retained, and nested fields stay in the same row.

#### Which source limits apply?

Only publicly returned like records are available. Removed accounts can have valid\_account=false and missing profile fields.

#### Does it download media files?

No. Where available, the Actor returns media URLs and metadata within the result row.

#### Does it require a Ameba account or personal cookies?

No. You do not need to provide a Ameba account, password or personal session cookie. Collection uses HTTP without opening a browser.

#### What happens to invalid or unavailable inputs?

Invalid individual inputs are recorded as failed without a source request. Confirmed missing or unavailable targets stop without unnecessary retries. Temporary failures allow up to five total attempts per request; other inputs and earlier saved results remain available.

#### Can I export results or run the same input again?

Yes. Export the default dataset or retrieve it through the Apify API. You can create an Apify schedule and connect completed runs to your own workflow.

### Support, responsible use, and related actors 🛟

For a reproducible issue, use the Actor’s **Issues** tab and provide the run ID, approximate time, a safe public input, expected behavior and actual result. Include the mode or page budget when relevant. Never share access tokens, personal cookies or proxy credentials.

Use public data responsibly and follow applicable privacy, copyright, contractual and platform requirements before storing, analyzing or redistributing collected information.

# Actor input Schema

## `inputList` (type: `array`):

Enter full public Ameblo post URLs. Each page contains up to 10 like records.

## `pagesPerPost` (type: `integer`):

Maximum result pages per input. Setup requests do not count as pages. Stops when the source has no next page or repeats a continuation. Actual rows per page can vary.

## Actor input object example

```json
{
  "inputList": [
    "https://ameblo.jp/staff/entry-12590320297.html"
  ],
  "pagesPerPost": 1
}
```

# Actor output Schema

## `likes` (type: `string`):

One row per user who liked the source post.. Check success or failed status.

# 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 = {
    "inputList": [
        "https://ameblo.jp/staff/entry-12590320297.html"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapingmonkey/ameba-post-likes-scraper").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 = { "inputList": ["https://ameblo.jp/staff/entry-12590320297.html"] }

# Run the Actor and wait for it to finish
run = client.actor("scrapingmonkey/ameba-post-likes-scraper").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 '{
  "inputList": [
    "https://ameblo.jp/staff/entry-12590320297.html"
  ]
}' |
apify call scrapingmonkey/ameba-post-likes-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapingmonkey/ameba-post-likes-scraper"
        }
    }
}
```

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/VQFXQ7EXjoY0vsRM2/builds/MsvYKXbYZeMWZD9gC/openapi.json