HypeAuditor For TikTok
Endpoint
You can get the report of a TikTok channel if it’s ready or request the report if it’s not ready.
By Channel (Username)
GET https://hypeauditor.com/api/method/auditor.tiktok/?channel={channel}&type={type}
Channel is TikTok username (@littlebig) from the url of TikTok channel (https://www.tiktok.com/@littlebig).
By User ID
GET https://hypeauditor.com/api/method/auditor.tiktokByUserId/?user_id={user_id}&type={type}
The type (string) parameter identifies which type of report is being requested:
report- returns the mini report, costs 1 mini report credit;advanced_report- returns the advanced report, costs 1 report credit
If no type is identified (parameter not used), theadvanced_reportwill be unlocked by default.
Note:
littlebigreport is free, use it to test the API.- An advanced report request costs 1 report credit, a mini report request costs 1 mini report credit. If we cannot build a report due to reuqested social platform account restrictions or errors, 1 credit will be returned after the unsuccessful generation attempt. To check the number of remaining credits, please refer to
restTokensfield in the fetched response.- First check of the username costs 1 credit and the report will be accessible for 1 year. Next check after 365 days will also cost 1 credit.
Images (posts and avatars)
Pictures stored on our CDN side (cdn.hypeauditor.com/*) have a lifespan of 28 days. You should re-request them if you cache their URIs on your side.
Changelog
Important:
- The object
blogger_challenges_performanceis deprecated, thus the data is not updated.- The object
post_frequencyis deprecated, instead, please refer tomedia_per_weekobject.- The string
report_statehas added to Endpoint since 07/01/2025.- Since 10/02/2025, 1 credit is charged per report request and refunded if the report generation attempt was unsuccessful.
- The GET TikTok Report by
user_idendpoint was added on 27/03/2025.- The string
blogger_genderwas added to Endpoint on 15/10/2025- The
allkey in performance objects (e.g.:metrics.subscribers_count.performance.all) is deprecated. Instead, key365d, has been added. If your integration currently relies onall, please migrate to365d. Theallkey will be permanently removed on 01/06/2026. Updating your integration in advance will help prevent any service disruptions.- The parameter
**type**has been added to the Endpoint on 25/05/2026. Report can be now unlocked in its mini or advanced version.
Error codes
You will receive an error if the requested channel is not found, does not have videos or views.
CHANNEL_NOT_FOUNDchannel not found on TikTokNO_VIDEOSchannel does not have videosNO_VIEWSchannel does not have views
Response Object
| Attributes | Type | |
|---|---|---|
| report_state | string | TikTok report state |
| report_quality | string | TikTok report quality |
| report | object | TikTok report data |
report_state
String report_state identifies if the requested report is fully ready or lacks demographic data due to the low audience activity. List of report states:
| Report state | Description |
|---|---|
READY | Report is fully ready and contains all metrics |
READY_LOW_CONFIDENCE | Report lacks metrics audience_age_gender, audience_geo, audience_by_type |
NOT_READY | Report is still generating, check again later |
Report Object
| Attributes | Type | |
|---|---|---|
| basic | object | general information about the channel |
| metrics | object | channel metrics and calculated metrics |
| features | object | rich data about channel |
Basic Object
| Attributes | Type | |
|---|---|---|
| id | string | channel id |
| username | string | channel username |
| title | string | channel title |
| avatar_url | string | channel avatar url |
| description | string | channel description |
Metrics Object
Each metrics objects may contain value field and performance object. Performance object may contain 5 periods of data: (7d for 7 days data30d for 30, 90d for 90, 180d for 180 and 365d for 365). Each period object contains value computed for current period (7/30/90/180d) and value_prev computed for the same period before current. For example: on 4th of July value shows data for Jun 5 - Jul 4 and value_prev shows data for May 4 - Jun 4. Note: value and values in performance are not the same.
media_per_week
| Attributes | Type | |
|---|---|---|
| performance.value | float | Number of media content per week in a period of time |
| performance.mark | string | Mark for media content per week in a period of time, ex. “very_good” |
| performance.mark_tittle | string | Mark title for media content per week in a period of time, ex. “VERY_GOOD” |
| performance.similar | float | Number of media content per week in a period of time for similar accounts |
| performance.period | string | Period media, ex. “PER_WEEK” |
| value | float | Number of media content per week |
| mark | string | Mark for media content per week, ex. “average” |
| similar | float | Number of media content per week for similar accounts |
subscribers_count
| Attributes | Type | |
|---|---|---|
| value | int | Number of total subscribers |
| performance.value | int | number of new subscribers in a given period |
views_avg
| Attributes | Type | |
|---|---|---|
| performance.value | int | number of average views in a given period |
subscribers_growth_prc
| Attributes | Type | |
|---|---|---|
| performance.value | float | value in a given period |
| performance.mark | string | mark ex.”very_good” |
| performance.similar | float | value for similar accounts |
er
| Attributes | Type | |
|---|---|---|
| value | int | ER value |
| mark_title | string | quality mark_title |
| performance.value | float | er value in a given period |
| performance.mark | string | mark ex.”average” |
| performance.similar | float | value for similar accounts |
alikes_avg
| Attributes | Type | |
|---|---|---|
| performance.value | int | last observed average number of likes and dislikes |
| performance.value | int | average number of likes and dislikes in a given period |
| performance.min | int | |
| performance.max | int |
likes_count
| Attributes | Type | |
|---|---|---|
| value | int | Number of total likes |
| performance.value | int | Number of new likes in a given period |
| performance.value_prev | int | Number of new likes in a previous period |
media_count
| Attributes | Type | |
|---|---|---|
| value | int | Number of total media |
| performance.value | int | Number of new media in a given period |
| performance.mark | string | mark ex. “good” |
| performance.mark_tittle | string | mark title ex. “LOW” |
| performance.similar | int | Number of media in a given period of similar bloggers |
| performance.similar_min | int | Min number of media in a given period of similar bloggers |
| performance.similar_max | int | Max number of media in a given period of similar bloggers |
| performance.value_prev | int | Number of new media in a previous period |
likes_views_ratio
| Attributes | Type | |
|---|---|---|
| value | float | Likes views ratio through all time activity |
| performance.value | float | Likes views ratio in a given period |
| performance.mark | string | mark ex. “poor” |
| performance.mark_tittle | string | mark title ex. “GOOD” |
| performance.similar | float | Likes views ratio a in a given period of similar bloggers |
| performance.similar_min | float | Min likes views ratio in a given period of similar bloggers |
| performance.similar_max | float | Max likes views ratio in a given period of similar bloggers |
| performance.value_prev | float | Likes views ratio in a previous period |
comments_count
| Attributes | Type | |
|---|---|---|
| value | int | Number of total comments |
| performance.value | int | Number of new comments in a given period |
| performance.value_prev | int | Number of new comments in a previous period |
views_followers_ratio
views_followers_ratio contains object performance for given periods of time. Each object contains the following fields:
| Attributes | Type | |
|---|---|---|
| performance.value | float | Views followers ratio in a given period |
| performance.mark | string | mark ex. “fair” |
| performance.mark_title | string | mark title ex. “BELOW_AVERAGE” |
| performance.similar | float | Views followers ratio a in a given period of similar bloggers |
| performance.similar_min | float | Min views followers ratio in a given period of similar bloggers |
| performance.similar_max | float | Max views followers ratio in a given period of similar bloggers |
| performance.value_prev | float | Views followers ratio in a previous period |
shares_count
share_count contains object performance for given periods of time. Each object contains the following fields:
| Attributes | Type | |
|---|---|---|
| performance.value | int | number of new shares in a given period |
| performance.value_prev | int | number of new shares in a previous period |
following_count
| Attributes | Type | |
|---|---|---|
| value | int | Number of total followings |
comments_avg
comments_avg contains object performance for given periods of time.
| Attributes | Type | |
|---|---|---|
| value | float | last observed number of comments |
| performance.value | float | average number of comments in a given period |
| performance.min | float | minimum average number of comments in a given period |
| performance.min | float | maximum average number of comments in a given period |
shares_avg
shares_avg contains object performance for given periods of time.
| Attributes | Type | |
|---|---|---|
| value | float | last observed number of shares |
| performance.value | float | average number of shares in a given period |
| performance.min | float | minimum average number of shares in a given period |
| performance.min | float | maximum average number of shares in a given period |
comments_likes_ratio
comments_like_ratio contains object performance for given periods of time.
| Attributes | Type | |
|---|---|---|
| value | int | Comments to likes ratio value |
| mark_title | string | quality mark_title, ex. “AVERAGE” |
| performance.value | float | value in a given period |
| performance.mark | string | quality mark, ex. “average” |
| performance.similar | float | value for similar accounts in a given period |
post_frequency
IMPORTANT
The object
post_frequencyis deprecated, instead, please refer tomedia_per_weekobject.
| Attributes | Type | |
|---|---|---|
| performance.value | float | value in a given period |
| performance.mark | string | quality mark |
| performance.similar | float | similar accounts value |
subscribers_quality
Viral potential
| Attributes | Type | |
|---|---|---|
| value | float | value |
| mark | string | quality mark, ex. “average” |
| similar | float | similar accounts value |
audience_reachability
| Attributes | Type | |
|---|---|---|
| value | float | value |
| mark | string | quality mark, ex. “poor” |
| similar | float | similar accounts value |
Mark
poorfairaveragegoodexcellent
Features Object
audience_age_gender
Audience age gender distribution. If object is null that means no data available for channel.
| Attributes | Type | |
|---|---|---|
| data | object | Dict of age objects. Each age object contains two genders (male and female). |
Age objects are: 13-17, 18-24, 25-34, 35-44, 45-54, 55-64, 65+
audience_by_type
Audience type
| Attributes | Type | |
|---|---|---|
| data | object | keys: real, bots, infs, mass |
real = Generators mass = Consumers bots = Suspicious infs = Influencers
audience_geo
Audience geo. If object is null that means no data available for channel.
| Attributes | Type | |
|---|---|---|
| data | array | array of {code: string, prc: float} objects. Code is ISO Alpha-2 two letter country code. |
audience_languages
Audience languages
| Attributes | Type | |
|---|---|---|
| data | array | array of {title: string, prc: float} objects. Title is ISO Alpha-2 two letter language code. |
audience_races
Audience Ethnicity
| Attributes | Type | |
|---|---|---|
| data | object | keys: asian, caucasian, hispanic, indian, african, arabian |
aqs
Account Quality Score.
| Attributes | Type | |
|---|---|---|
| data.value | int | AQS value |
| data.mark | string | one of: poor, fair, average, good, very_good, excellent |
| data.description | object | pros & cons for aqs, possible keys (may be null): er, comments_likes_ratio, account_growth |
| data.description.{key}.mark | string | one of: poor, fair, average, good, very_good, excellent |
| data.description.{key}.description.title.key | string | description of key, ex. “GOOD_CL_RATIO” |
| data.description.{key}.description.title.text | string | description of text, ex. “Good comments to likes ratio” |
blogger_views_likes_chart
| Attributes | Type | |
|---|---|---|
| data | array of objects | Each dot on the plot is a video, X coordinate is views number, Y is likes number. Predicted number of views is green, “for you” videos (that receive more views) are violet. |
media_by_type
| Attributes | Type | |
|---|---|---|
| data.recommended | float | % of posts hit For-You tab |
| data.commercial | float | |
| data.potentially_commercial | float |
likes_distribution
| Attributes | Type | |
|---|---|---|
| data.{time_period}.value.{likes_group} | int | Count of media with given count of likes in a given period |
| data.{time_period}.value_prev.{likes_group} | int | Count of media with given count of likes in a previous period |
blogger_geo
| Attributes | Type | |
|---|---|---|
| data.country | string | Two character country code |
blogger_gender
| Attributes | Type | |
|---|---|---|
| male | string | The detected gender is male |
| female | string | The detected gender is female |
| unknown | string | No gender detected |
| null | - | The gender is not detected (for example, for brands accounts like @nasa) |
blogger_languages
| Attributes | Type | |
|---|---|---|
| data | array | List of two char language codes |
most_media
| Attributes | Type | |
|---|---|---|
| data.time_posted_desc.performance.{time_period}.media_ids | int | Ids list of media sorted by time posted descendent in given period |
| data.most_recent_media.performance.{time_period}.media_ids | int | Ids list of media sorted by most recent media in given period |
| data.er_desc.performance.{time_period}.media_ids | int | Ids list of media with max engagement rate in given period |
| data.most_engaging_media.performance.{time_period}.media_ids | int | Ids list of media sorted by most engaging media in given period |
| data.views_desc.performance.{time_period}.media_ids | int | Ids list of media sorted by views descendent in given period |
| data.most_viewed_media.performance.{time_period}.media_ids | int | Ids list of media with max views count in given period |
| data.comments_desc.performance.{time_period}.media_ids | int | Ids list of media sorted by number of comments in descendent in given period |
| data.most_commented_media.performance.{time_period}.media_ids | int | Ids list of media sorted by most commented media in given period |
| data.likes_desc.performance.{time_period}.media_ids | int | Ids list of media sorted by number of likes in descendent in given period |
| data.most_liked_media.performance.{time_period}.media_ids | int | Ids list of media sorted by most liked media in given period |
| data.shares_desc.performance.{time_period}.media_ids | int | Ids list of media sorted by number of shares in descendent in given period |
| data.most_viral_media.performance.{time_period}.media_ids | int | Ids list of media with max virality in given period |
blogger_challenges_performance
IMPORTANT
The object
blogger_challenges_performanceis deprecated, thus the data is not updated.
Challenges_stats object contains 6 periods of data: (7d for 7 days, 30d for 30 days, 90d for 90 days, 180d for 180 days, 365d for 365 days).
| Attributes | Type | |
|---|---|---|
| data.posts | array | |
| data.challenges | array | |
| data.has_launched_advertising | bool | |
| data.challenges_stats.performance.{time_period}.commercial_count | int | Count of challenges in a period |
| data.challenges_stats.performance.{time_period}.post_ids | array | Ids posts with challenges |
| data.challenges_stats.performance.{time_period}.basic_stats.challenge | array | Hashtags of challenges |
| data.challenges_stats.performance.{period}.commercial_count | int | count of commercial challenges |
| data.challenges_stats.performance.{period}.post_ids | array | list of posts (from posts lists) with commercial challenge for {period} |
| data.posts.{post_id}.basic.id | string | id of post |
| data.posts.{post_id}.basic.text | string | post text |
| data.posts.{post_id}.basic.is_commercial | bool | is commercial flag |
| data.posts.{post_id}.basic.is_potentially_commercial | bool | is potentially commercial flag |
| data.posts.{post_id}.basic.comment_setting | bool | are comments allowed for the post: 0 |
| data.posts.{post_id}.basic.thumbnail | string | thumbnail image link |
| data.posts.{post_id}.basic.thumbnail_gif | string | gif thumbnail image link |
| data.posts.{post_id}.basic.time_posted | string | when post was posted |
| data.posts.{post_id}.basic.exact_create_time | int | when post was posted (timestamp) |
| data.posts.{post_id}.basic.is_recommended | bool | was post in a “For you” section |
| data.posts.{post_id}.basic.challenges | array | list of challenges objects |
| data.posts.{post_id}.basic.challenges.id | string | id of challange |
| data.posts.{post_id}.basic.challenges.name | string | name of challenge |
| data.posts.{post_id}.basic.challenges.is_commercial | bool | is this challenge commercial |
| data.posts.{post_id}.metrics.views_count.value | int | count of views |
| data.posts.{post_id}.metrics.likes_count.value | int | count of likes |
| data.posts.{post_id}.metrics.comments_count.value | int | count of comments |
| data.posts.{post_id}.metrics.shares_count.value | int | count of shares |
| data.posts.{post_id}.metrics.length_sec.value | int | length of video in seconds |
| data.posts.{post_id}.metrics.virality.value | float | virality (%) (shares / views) |
| data.posts.{post_id}.metrics.er.value | float | er value |
| data.posts.{post_id}.metrics.challenges_count.value | int | count of challenges |
| data.challenges.{challenge_id}.basic.id | string | id of challenge |
| data.challenges.{challenge_id}.basic.name | string | name of challenge |
| data.challenges.{challenge_id}.basic.description | string | description of challenge |
| data.challenges.{challenge_id}.basic.thumbnail | string | thumbnail link |
| data.challenges.{challenge_id}.basic.is_commercial | bool | is challenge commercial flag |
| data.challenges.{challenge_id}.basic.is_potentially_commercial | bool | is challenge potentially commercial flag |
| data.challenges.{challenge_id}.metrics.views_count.value | int | count of total challenge views |
| data.challenges.{challenge_id}.metrics.users_count.value | int | count of total challenge users |
| data.has_launched_advertising | bool | flag is this influencer already has launched advertising |
blogger_prices
| Attributes | Type | |
|---|---|---|
| data.post_price | int | Forecasted price of post |
| data.post_price_from | int | post price limit from |
| data.post_price_to | int | post price limit to |
| data.cpm | float | cost per mille value |
| data.cpm_from | float | cost per mille value limit from |
| data.cpm_to | float | cost per mille value limit to |
| data.cpm_mark | string | cost per mille mark. ex. ”poor” |
| data.cpm_similar | float | cost per mille value for similar accounts |
blogger_thematics
| Attributes | Type | |
|---|---|---|
| data | array | List of ids blogger categories |
blogger_emv
| Attributes | Type | |
|---|---|---|
| data.emv | int | Forecasted EMV |
| data.emv_from | int | EMV limit from |
| data.emv_to | int | EMV limit to |
| data.emv_per_dollar | int | EMV limit from per dollar value |
| data.emv_similar | int | EMV of similar bloggers |
| data.emv_mark | int | mark of EMV |
Mark:
- excellent
- very_good
- good
- fair
- poor
blogger_emails
| Attributes | Type | |
|---|---|---|
| data | array | List of blogger emails |
audience_sentiments
| Attributes | Type | |
|---|---|---|
| data.sentiments.POSITIVE.count | int | count of positive comments |
| data.sentiments.POSITIVE.prc | float | prc of positive comments |
| data.sentiments.NEUTRAL.count | int | count of neutral comments |
| data.sentiments.NEUTRAL.prc | float | prc of neutral comments |
| data.sentiments.NEGATIVE.count | int | count of negative comments |
| data.sentiments.NEGATIVE.prc | float | prc of negative comments |
| data.score | int | final score |
| data.comments_count | int | count of comments |
| data.posts_count | int | count of posts |
social_networks
Links to other blogger social networks . If object is null that means no data available for channel.
| Attributes | Type | |
|---|---|---|
| data | array | array of {type: int, title: string, social_id: string, avatar_url: string, subscribers_count: int} objects.Type is integer code of social network,social_id is identifier in this network, title is name in social network, avatar_url is link to avatar, subscribers_count is count of subscribers. |
Social networks ids map:
| Id | Social network |
|---|---|
| 1 | |
| 2 | YouTube |
| 3 | TikTok |
| 4 | Twitch |
| 5 | |
| 7 | Snapchat |
Requests
Set YOUR_ID and YOUR_TOKEN from HypeAuditor.
curl -X POST \
https://hypeauditor.com/api/method/auditor.tiktok \
-H 'content-type: application/x-www-form-urlencoded' \
-H 'x-auth-id: %YOUR_ID%' \
-H 'x-auth-token: %YOUR_TOKEN%' \
-d channel=littlebig \
<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => 'https://hypeauditor.com/api/method/auditor.tiktok',
CURLOPT_CUSTOMREQUEST => 'POST',
CURLOPT_POSTFIELDS => [
'channel' => 'littlebig'
],
CURLOPT_HTTPHEADER => [
'x-auth-id: YOUR_ID',
'x-auth-token: YOUR_TOKEN'
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo 'cURL Error #:' . $err;
} else {
echo $response;
}
Note:
You don’t need to pre-request the report from web, you can request it directly from API.
Advanced report
Sample request
By Channel Name
GET https://hypeauditor.com/api/method/auditor.tiktok/?channel=littlebig?type=advanced_report
By user ID
GET https://hypeauditor.com/api/method/auditor.tiktokByUserId/?user_id=6557821753438371845?type=advanced_report
Sample response
{
"result": {
"report_state": "READY",
"report_quality": "NO_POSTS_7D",
"report": {
"basic": {
"id": "6557821753438371845",
"username": "littlebig",
"title": "littlebig",
"avatar_url": "https://cdn.hypeauditor.com/img/tiktok/user/6557821753438371845.jpg?w=150&till=1775001540&sign=2b6e94dff2bc6eee20a421d33aa01d6b",
"description": "No bio yet"
},
"metrics": {
"media_per_week": {
"value": 1,
"value_prev": 0,
"mark": "low",
"similar": 2.678568,
"performance": {
"7d": {
"value": 0,
"similar": 3,
"mark": "poor",
"mark_title": "LOW",
"period": "PER_WEEK"
},
"30d": {
"value": 0,
"similar": 4.9,
"mark": "poor",
"mark_title": "LOW",
"period": "PER_WEEK"
},
"90d": {
"value": 0.47,
"similar": 4.74,
"mark": "poor",
"mark_title": "LOW",
"period": "PER_WEEK"
},
"180d": {
"value": 0.66,
"similar": 2.68,
"mark": "poor",
"mark_title": "LOW",
"period": "PER_WEEK"
},
"365d": {
"value": 0.94,
"similar": 0.71,
"mark": "good",
"mark_title": "GOOD",
"period": "PER_WEEK"
}
}
},
"subscribers_count": {
"value": 5400000,
"performance": {
"30d": {
"value": 0
},
"90d": {
"value": 0
},
"180d": {
"value": 0
}
}
},
"views_avg": {
"value": 161850,
"mark": "very_good",
"performance": {
"30d": {
"value": 0,
"mark": "poor"
}
}
},
"subscribers_growth_prc": {
"performance": {
"30d": {
"value": 0,
"mark": "average",
"similar": 0
},
"90d": {
"value": 0,
"mark": "average",
"similar": 0
},
"180d": {
"value": 0,
"mark": "average",
"similar": 0
},
"365d": {
"value": 2.41,
"mark": "good",
"similar": 0.240053
}
}
},
"er": {
"value": 4.55,
"mark_title": "BELOW_AVERAGE",
"performance": {
"30d": {
"value": 0,
"mark": "poor",
"similar": 5.681376
},
"90d": {
"value": 6.83,
"mark": "good",
"similar": 5.506273
},
"180d": {
"value": 4.28,
"mark": "fair",
"similar": 5.37049
}
}
},
"alikes_avg": {
"value": 6255.5,
"performance": {
"30d": {
"value": 0,
"min": null,
"max": null
},
"90d": {
"value": 16211,
"min": null,
"max": null
},
"180d": {
"value": 6742,
"min": null,
"max": null
},
"365d": {
"value": 15600,
"min": null,
"max": null
}
}
},
"likes_count": {
"value": 48200000,
"performance": {
"7d": {
"value": 0,
"value_prev": null
},
"30d": {
"value": 0,
"value_prev": 96984
},
"90d": {
"value": 161522,
"value_prev": 466614
},
"180d": {
"value": 628136,
"value_prev": 1081776
},
"365d": {
"value": 1709912,
"value_prev": 2779656
}
}
},
"media_count": {
"value": 246,
"performance": {
"7d": {
"value": 0,
"value_prev": null,
"mark": "poor",
"mark_title": "LOW",
"similar": 3,
"similar_min": 2,
"similar_max": 3
},
"30d": {
"value": 0,
"value_prev": 2,
"mark": "poor",
"mark_title": "LOW",
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{
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},
{
"challenge": "viral",
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}
]
}
}
},
"posts": [],
"challenges": [],
"has_launched_advertising": false
}
},
"blogger_prices": {
"data": {
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"cpm": 18.103182,
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"cpm_mark": "good",
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},
"status": {
"code": "OK"
}
},
"blogger_thematics": {
"data": [
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"3"
],
"status": {
"code": "OK"
}
},
"blogger_emv": {
"data": {
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"emv_to": 1700,
"emv_per_dollar": 0.34,
"emv_mark": "poor",
"emv_similar": 1.11
},
"status": {
"code": "OK"
}
},
"blogger_emails": {
"data": [
"[email protected]",
"[email protected]"
],
"status": {
"code": "OK"
}
},
"audience_sentiments": {
"data": {
"sentiments": {
"POSITIVE": {
"count": 15,
"prc": 32.61
},
"NEUTRAL": {
"count": 24,
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},
"NEGATIVE": {
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}
},
"score": 84,
"comments_count": 46,
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}
},
"audience_age_gender": {
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},
"data": {
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},
"18-24": {
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},
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},
"35-44": {
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},
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},
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},
"65+": {
"male": 0.27,
"female": 0
}
}
},
"audience_by_type": {
"status": {
"code": "OK"
},
"data": {
"real": 58.81,
"bots": 5.2,
"infs": 3.24,
"mass": 32.75
}
},
"audience_geo": {
"status": {
"code": "OK"
},
"data": {
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},
"countries": [
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},
{
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},
{
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},
{
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},
{
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]
}
},
"audience_languages": {
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},
"data": [
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},
{
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},
{
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},
{
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},
{
"title": "pl",
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},
{
"title": "Other",
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}
]
},
"audience_races": {
"status": {
"code": "OK"
},
"data": {
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"african": 2.81
}
},
"aqs": {
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"mark": "average",
"description": {
"er": {
"mark": "fair",
"description": {
"title": {
"key": "BELOW_AVERAGE_ER",
"text": "Audience engagement is below average"
}
}
},
"comments_likes_ratio": {
"mark": "excellent",
"description": {
"title": {
"key": "EXCL_CL_RATIO",
"text": "Excellent comments to likes ratio"
}
}
},
"account_growth": {
"mark": "fair",
"description": {
"title": {
"key": "BELOW_AVG_GROWTH",
"text": "Account growth rate below average"
}
}
},
"aqs_views_followers_ratio": {
"mark": "none",
"description": {
"title": {
"key": "AQS_LOW_VIEWS_FOLLOWERS_RATIO",
"text": "The average views count is abnormally low for the given audience size"
}
}
}
}
}
},
"media_by_type": {
"data": {
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"commercial": 0,
"potentially_commercial": 0
}
},
"blogger_views_likes_chart": {
"data": [
{
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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}
],
"confidence": {
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"value": 100,
"is_low_confidence": false
}
},
"blogger_reach": {
"status": {
"code": "OK"
},
"data": {
"reach": 129976,
"reach_from": 65000,
"reach_to": 195000,
"reach_mark": "very_good",
"reach_similar": 24329.239308
}
},
"social_networks": {
"data": [
{
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"title": "Little Big",
"social_id": "UCu7TZ_ATWgjgD9IrNLdnYDA",
"username": "littlebig",
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},
{
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"username": "littlebig",
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"subscribers_count": 5400000,
"er": 4.55,
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},
{
"type": 1,
"title": "LITTLE BIG",
"social_id": "3015279418",
"username": "littlebigband",
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]
}
}
},
"restTokens": 9925,
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}
}
highDemand field is “true” when HypeAuditor experiences unexpected high load that effects report generation time negatively. When you receive that field you may want to inform your users that the delay is temporary and we’re fixing it. Everything will go back to normal and all requests are safe.
Mini report
Sample request
By Channel Name
GET https://hypeauditor.com/api/method/auditor.tiktok/?channel=littlebig?type=report
By user ID
GET https://hypeauditor.com/api/method/auditor.tiktokByUserId/?user_id=6557821753438371845?type=report
Sample response
{
"result": {
"report_state": "READY",
"report_quality": "FULL",
"report": {
"basic": {
"id": "6557821753438371845",
"username": "littlebig",
"title": "littlebig",
"avatar_url": "https://cdn.hypeauditor.com/img/tiktok/user/6557821753438371845.jpg?w=150&till=1782657505&sign=1b7ee30d64c37ef581c074c566a88871",
"description": "🔥Europe Tour 2026🔥\n😍Tickets 👉🏼 tour.littlebig.band"
},
"metrics": {
"media_per_week": {
"value": 1,
"value_prev": 0,
"mark": "low",
"similar": 2.622132,
"performance": {
"7d": {
"value": 3,
"similar": 3,
"mark": "good",
"mark_title": "GOOD",
"period": "PER_WEEK"
},
"30d": {
"value": 3.03,
"similar": 4.9,
"mark": "fair",
"mark_title": "BELOW_AVERAGE",
"period": "PER_WEEK"
},
"90d": {
"value": 1.09,
"similar": 4.74,
"mark": "poor",
"mark_title": "LOW",
"period": "PER_WEEK"
},
"180d": {
"value": 0.62,
"similar": 2.68,
"mark": "poor",
"mark_title": "LOW",
"period": "PER_WEEK"
},
"365d": {
"value": 0.96,
"similar": 0.71,
"mark": "good",
"mark_title": "GOOD",
"period": "PER_WEEK"
},
"all": {
"value": 2.7,
"similar": 0.67,
"mark": "excellent",
"mark_title": "EXCELLENT",
"period": "PER_WEEK"
}
}
},
"subscribers_count": {
"value": 5500000,
"performance": {
"30d": {
"value": 100000
},
"90d": {
"value": 100000
},
"180d": {
"value": 100000
}
}
},
"views_avg": {
"value": 362950,
"mark": "excellent",
"performance": {
"30d": {
"value": 485400,
"mark": "excellent"
}
}
},
"subscribers_growth_prc": {
"performance": {
"30d": {
"value": 1.85,
"mark": "excellent",
"similar": 0
},
"90d": {
"value": 1.85,
"mark": "very_good",
"similar": 0
},
"180d": {
"value": 1.85,
"mark": "good",
"similar": 0
},
"365d": {
"value": 2.69,
"mark": "good",
"similar": 0.220334
}
}
},
"er": {
"value": 6.04,
"mark_title": "AVERAGE",
"performance": {
"30d": {
"value": 7.32,
"mark": "good",
"similar": 5.606467
},
"90d": {
"value": 7.16,
"mark": "good",
"similar": 5.393363
},
"180d": {
"value": 7.16,
"mark": "good",
"similar": 5.253502
}
}
},
"alikes_avg": {
"value": 20850,
"performance": {
"30d": {
"value": 24900,
"min": null,
"max": null
},
"90d": {
"value": 23000,
"min": null,
"max": null
},
"180d": {
"value": 23000,
"min": null,
"max": null
},
"365d": {
"value": 13550,
"min": null,
"max": null
}
}
},
"comments_avg": {
"value": 186.5,
"performance": {
"30d": {
"value": 231,
"min": null,
"max": null
},
"90d": {
"value": 215,
"min": null,
"max": null
},
"180d": {
"value": 215,
"min": null,
"max": null
}
}
},
"shares_avg": {
"value": 1020,
"performance": {
"30d": {
"value": 2230,
"min": null,
"max": null
},
"90d": {
"value": 2174,
"min": null,
"max": null
},
"180d": {
"value": 2174,
"min": null,
"max": null
}
}
},
"comments_likes_ratio": {
"value": 1.19,
"mark_title": "VERY_GOOD",
"performance": {
"30d": {
"value": 0.97,
"mark": "good",
"similar": 0.614782
},
"90d": {
"value": 0.98,
"mark": "very_good",
"similar": 0.598854
},
"180d": {
"value": 0.98,
"mark": "very_good",
"similar": 0.573963
}
}
},
"post_frequency": {
"performance": {
"30d": {
"value": 3.03,
"mark": "fair",
"similar": 4.9
}
}
},
"subscribers_quality": {
"value": 62.050000000000004,
"mark": "excellent",
"similar": 48.3136
},
"audience_reachability": {
"value": 94.8,
"mark": "poor",
"similar": 98.15956
}
},
"features": {
"blogger_geo": {
"data": {
"country": "us"
}
},
"blogger_gender": "unknown",
"blogger_languages": {
"data": [
"ru"
]
},
"most_media": {
"data": {
"time_posted_desc": {
"performance": {
"7d": {
"media_ids": [
"7644843802074647829",
"7644573469174287637",
"7642614173200960788",
"7642242271135845652",
"7641874929809427732",
"7641500849558064405",
"7641130160258878740",
"7640899659753393428"
]
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