LinkedIn Posts
A person's posts on LinkedIn.
The LinkedIn Post insight provides comprehensive information about an individual's LinkedIn posts, offering valuable insights into their professional activities, interests, and engagement on the platform. This insight can be retrieved using either the contact's email address or their LinkedIn URL.
Note: the only current available parameters for
generate-insights
arecontactEmail
andcontactLinkedinUrl
.
- The system will pass back an array of insights, ranked by relevance.
- The below documentation still accurately covers our schema, and demonstrates what might exist in that array.
- Soon, you'll be able to specify certain insight types in your API request. Coming Q3 2024.
Retrieving LinkedIn Post Insight
To retrieve the LinkedIn Post insight using the Autobound Insights API, you need to make a POST request to the /generate-insights endpoint with the appropriate parameters.
import requests
url = 'https://api.autobound.ai/api/external/generate-insights/v1.0.0'
headers = {
'X-API-KEY': 'YOUR-API-KEY-HERE',
'Content-Type': 'application/json'
}
data = {
"contactEmail": "[email protected]",
"contactLinkedInUrl": "https://www.linkedin.com/in/johndoe",
# You can use either contactEmail, contactLinkedInUrl, or both
"insightType": "LINKED_IN_POST"
}
response = requests.post(url, headers=headers, json=data)
print(response.text)
Response Format
The API will respond with the LinkedIn Post insight in JSON format. The response will contain detailed information about the individual's LinkedIn posts, including:
- Post content and text
- Timestamp of when the post was made
- Associated media (images, videos, documents)
- Engagement metrics (likes, comments, reposts)
- Article details (title, subtitle, URL)
- Reshared post information
- Poster's LinkedIn URL
{
"data": [
{
"urn": "7050105661103210496",
"text": "❗️❗️We have our Autobound March Madness winner❗️❗️\n\n...drumroll please…\n\nRyan Du, Global Sales Enablement Specialist at Seismic 🎉 🎉\n\nRyan, thank you for putting in all your hard work over the last few weeks and #Du'ingItBig! \n\nRyan came in hot against the competition with only *four likes* standing between him and Jessica Daly's email... it was truly a battle to the finish (I saw the two exchange the lead position probably 4-5x in ~48 hours).\n\nA HUGE thanks to all those who participated in this tournament. It's been very encouraging to see so much thoughtfulness when it comes to personalization in sales. That's something I take quite seriously myself (so much so that I'm building a company around it) 😂\n\nOn that note, Autobound has a 50 spots left in our free beta program. We built an insanely powerful tool that uses AI to generated hyper-personalized emails in seconds, based on news events, shared experiences, company initiatives, and more. And we'd love your feedback on it! You can get started in <30 seconds at https://lnkd.in/giKCvEVd\n\nJoin the beta while you can... Autobound may or may not be launching the first genAI-powered Chat feature built **specifically** for b2b sales today 🤫\n\nDid you enjoy following our email-writing tournament?? Want to lock in your spot for next year?\n\nComment I'm in below and I'll add you to the list👇🏼\n\nLastly, something very cool that this tournament has produced, is a fantastic repository of A++ prospecting / personalization tactics.\n\nSome awesome ideas to borrow from in the emails from these posts. Here they are in one place (tag whoever helps write content for your team!)\n\n- https://lnkd.in/gYgrr_4G\n- https://lnkd.in/gRteuN8Q\n- https://lnkd.in/gVSMHWMH\n- https://lnkd.in/gYg8p4pP\n- https://lnkd.in/g98K6uzW\n- https://lnkd.in/gQdisjtG\n- https://lnkd.in/gWN35M4V\n\nUntil next year 🤘",
"time": "11mo",
"images": [],
"posted": "2023-04-07 14:02:54",
"document": {
"url": "https://media.licdn.com/dms/document/media/D561FAQG3VcvnUlUR0w/feedshare-document-url-metadata-scrapper-pdf/0/1680875087761?e=1709766000&v=beta&t=Cl3xwcdrYJXQvQS8E9D6NVZoHwdOkowEi8UiasaKpFE",
"title": "Autobound March Madness Winner 🔥",
"page_count": 7
},
"post_url": "https://www.linkedin.com/feed/update/urn:li:activity:7050105661103210496/",
"reshared": true,
"num_likes": 36,
"num_empathy": 4,
"num_praises": 5,
"num_reposts": 1,
"num_comments": 12,
"resharer_comment": "Did it for the love of the game but honored I could bring one home for Seismic. Couldn't have done it without the support of my network so thank you to everyone who voted throughout the competition! ❤️🏆",
"poster_linkedin_url": "https://www.linkedin.com/in/daniel-wiener"
},
{
"urn": "7127717386836725760",
"text": "The Season 1 finale of The Strong Mind Strong Will Podcast is out now! In Episode #10, I recap snippets from prior episodes with sales leaders Mark D. Orlando, Kerry Huffman, Geoffrey Price, Troy Burke and Julie Thomas. If you haven't had the chance to tune in to some of these episodes, I highly recommend checking this one out. A ton of applicable sales/business advice for those looking to begin their career and those who are already immersed in the sales industry! If you enjoyed this episode please subscribe, leave a review, and make sure to check out @strongmindstrongwill on Instagram to stay updated with all future episodes. Links to Spotify and Apple Podcasts will be listed below!\n\nSpotify: https://lnkd.in/g5gGmZZH\n\nApple Podcasts: https://lnkd.in/gmHZGxVY\n",
"time": "4mo",
"images": [],
"posted": "2023-11-07 18:04:11",
"post_url": "https://www.linkedin.com/feed/update/urn:li:activity:7127717386836725760/",
"reshared": true,
// this was initially posted by adam - knees and reshared by ryan
"num_likes": 18,
"num_praises": 3,
"num_reposts": 3,
"num_comments": 5,
"article_title": "Season 1 Sales Episodes Recap",
"num_interests": 1,
"article_subtitle": "open.spotify.com",
"poster_linkedin_url": "https://www.linkedin.com/in/adam-knees"
},
{
"urn": "7123013173649031169",
"text": "I'm thrilled to share Seismic's latest innovation: Seismic for Meetings!\n\nSeismic for Meetings powers better customer experiences by ensuring sales teams can more effectively prepare, present, and follow-up on every meeting to accelerate and win more deals.\n\nAI-driven insights and analysis drive smarter content, proactive enablement, and focused training.\n\nWith Seismic for Meetings, your team can…\n\n-Streamline seller workflows using auto-generated meeting summaries and next steps.\n\n-Measure the end-to-end impact of content programs via our AI-insights dashboard.\n\n-Understand true content performance down to the slide level.\n\n-Add meeting materials to a Digital Sales Room with one click.\n\nFor more details about Meetings and our 2023 Fall Release, head to our Blog: https://bit.ly/404Ri7f\n\n#SeismicForMeetings",
"time": "4mo",
"video": {
"duration": 95433,
"stream_url": "https://dms.licdn.com/playlist/vid/D5610AQHMR0NwMGT9OA/mp4-720p-30fp-crf28/0/1698258671760?e=1709766000&v=beta&t=z4CSbcKuBnHRKB90z8Gg03pdqBbog1FRP4N_5P34f-E"
},
"images": [],
"posted": "2023-10-25 18:31:19",
"post_url": "https://www.linkedin.com/feed/update/urn:li:activity:7123013173649031169/",
"reshared": false,
"num_likes": 6,
"num_praises": 1,
"num_reposts": 0,
"num_comments": 0,
"poster_linkedin_url": "https://www.linkedin.com/in/ryan-du"
},
// ... more posts
],
"paging": {
"count": 50,
"start": 0,
"pagination_token": null
},
"message": "ok"
}
Field Descriptions:
urn
(string): A unique identifier for the LinkedIn post.text
(string): The text content of the LinkedIn post.time
(string): The relative time since the post was made (e.g., "11mo" for 11 months ago).images
(array): An array of image objects associated with the post. Each image object contains aurl
field.posted
(string): The timestamp of when the post was made, in the format "YYYY-MM-DD HH:mm:ss".document
(object): An object containing information about the document associated with the post, if applicable. It includes theurl
,title
, andpage_count
fields.post_url
(string): The URL of the LinkedIn post.reshared
(boolean): Indicates whether the post is a reshared post.num_likes
(integer): The number of likes the post has received.num_empathy
(integer): The number of empathy reactions the post has received.num_praises
(integer): The number of praises (reactions) the post has received.num_reposts
(integer): The number of times the post has been reposted (shared).num_comments
(integer): The number of comments on the post.resharer_comment
(string): The comment added by the user who reshared the post, if applicable.article_title
(string): The title of the article associated with the post, if applicable.num_interests
(integer): The number of interests (reactions) the post has received.article_subtitle
(string): The subtitle or domain of the article associated with the post, if applicable.poster_linkedin_url
(string): The LinkedIn URL of the user who made the post.video
(object): An object containing information about the video associated with the post, if applicable. It includes theduration
(integer) andstream_url
(string) fields.paging
(object): An object containing pagination information, including thecount
(integer) of posts,start
(integer) index, andpagination_token
(string or null).message
(string): A message indicating the status of the API response (e.g., "ok").
Use Cases
The LinkedIn Post insight is valuable for various use cases, such as:
- Analyzing an individual's professional interests and engagement on LinkedIn
- Identifying thought leaders and influencers within a specific industry or domain
- Monitoring competitor activity and their content strategies
- Gathering market intelligence and tracking industry trends
- Personalizing outreach and communication based on an individual's LinkedIn activity
- Enhancing lead generation and prospecting efforts by leveraging LinkedIn insights
By leveraging the LinkedIn Post insight, you can gain a deeper understanding of an individual's professional presence and engagement on LinkedIn, enabling you to tailor your strategies and interactions accordingly.
Updated about 2 months ago