Instagram Influencer Data Extraction

@kamran2012

Hi there, I will deliver the .xlsx workbook with all three sheets (Summary, Posts_Virtual, Posts_Human) covering 100 Instagram creators, pulling every post from 1 Jan 2016 to scrape date. Each row will include date, caption excerpt, content type, like count, comment count, and share data where exposed (with clear "N/A" flags for hidden metrics). My approach: a Python pipeline using Instaloader paired with the Meta Graph API for public data validation. You will get a short update at the end of each day so you can track progress across the 100 accounts. Questions: 1) Do you already have the list of 50 virtual and 50 human influencers selected, or do you need me to curate that list? 2) For accounts with 5,000+ posts since 2016, is a full pull still required, or would you cap at a certain number? Looking forward to discussing further. Best regards, Kamran

@nareshk44

I would be incredibly privileged to tackle this intriguing project. With vast experience in data extraction, web scraping and a high level of expertise in Excel, I can swiftly and accurately compile all the required information about the 100 influencers into one well-structured and comprehensive workbook. I have a profound understanding of the various data fields you need for each influencer's account and will ensure to include any Instagram exposed repost numbers alongside a clear N/A where there are any gaps. Timeliness and accuracy are absolutely non-negotiable, and rest assured that these qualities define my work ethic. Just to give you an idea on runtime, based on my previous scrape projects I estimate being able to comfortably deliver this within 7-10 days without compromise on accuracy. Drawing from my skills in Python, Instaloader, Meta Graph API and Selenium, I will utilize the most compliant scraper ensuring no compromise of your login credentials. My deep understanding of these tools will also be handy during validation of the data by spot-checking. To maintain accuracy throughout the project as well as ensure compliance with your specifications, different formats like posts with videos or photo carousels will be clearly indicated. In conclusion, my commitment towards top-quality service, fast & accurate delivery as well as vast skillset makes me the ideal fit for your project. Let's get started!

@MartinDudumi

Hi, I can deliver the complete dataset in the exact 3-sheet Excel structure requested. I would use a Python-based extraction workflow with incremental saving/checkpointing, followed by data validation and cleanup before generating the final workbook. Each post will include its live URL for easy verification, and metrics that Instagram does not publicly expose will be clearly marked as N/A rather than estimated. Since you're prioritizing accuracy, I would allow approximately 5–6 days for extraction, handling rate limits, validation and final Excel cleanup. Before starting, I'd just like to confirm whether you will provide the 100 influencer handles or whether selecting the 50 virtual and 50 human creators is also part of the project. I can start immediately.

@PINESCRIPTERR

Hi, I can deliver a structured, quality-checked Excel workbook covering 50 virtual and 50 human Instagram creators, with separate summary and post-level sheets exactly as specified. I would use a compliant collection pipeline built around Instagram’s official API where access permits, supplemented only by publicly accessible data collection that respects platform limits. The process will normalize timestamps, captions, media types, follower totals, likes, comments, and available share/repost metrics. Hidden metrics will be recorded consistently as `N/A`, never inferred. Each post row will also include the influencer handle and live post URL to support your spot checks. I will remove duplicates using post IDs, flag unavailable or deleted content, preserve Unicode captions, and validate totals between the post sheets and Summary sheet. The final `.xlsx` will include filters, frozen headers, consistent data types, and a brief methodology/data-limitations note. Because Instagram may restrict historical depth and metrics depending on account type, API permissions, login state, and regional visibility, I will first verify coverage on a small account sample before processing the complete list. Question 1: Will you provide the 100 approved handles, or should they also be researched and classified? Question 2: Do you have authorized Meta Graph API access for any of these accounts? Regards, Houssame

@sagarwal3005

Your project requires extracting a substantial amount of data from Instagram for both virtual and human influencers, ensuring compliance is key. I propose using Python with a combination of Instaloader for scraping public data and a structured approach to store it in a well-structured Excel workbook. I can segregate the data into three sheets as specified and ensure that missing metrics are clearly labeled "N/A". With expertise in Python and web scraping, I can deliver you an accurate and well-organized solution. My profile reflects a 4.9-star rating across 200 client reviews and 220 projects completed, demonstrating my commitment to quality. Could you clarify the expected timeline for your project?

@opencollar

Hi, I understand you need detailed data on virtual and human influencers for analysis. Sometimes, people want to compare engagement across different influencer types naturally. I'll get the complete posting history from Instagram for both groups, extracting data like follower count, post date, captions, likes, comments, and share counts. Do you prefer live URLs to verify accuracy or a certain data format to streamline your review? Let’s chat about how we can plan this out and create a solid solution together. Regards, Nick.

@mobasherreza10

As a part of Aesthetic Logic, I specialize in web scraping and data analysis—ideal for your Instagram Influencer data extraction task. My expertise in Python and Selenium will enable me to develop a robust and effective scraper that adheres to Instagram's terms of service while efficiently gathering the necessary information. With experience working with tools like Instaloader, Meta Graph API, as well as custom scrapers, I can ensure proper extraction and organization of the required details from each profile's posting history. Accuracy is at the forefront of my work, and I understand the importance of validating the data. In this case, I will go a step further than spot-checking by meticulously verifying a random sample of the data against live URLs to guarantee its integrity. You mentioned runtime: taking the nature of web scraping into account, I can reasonably estimate completion within an efficient timeframe without sacrificing quality.

@alizain721

Hello, I will deliver the requested .xlsx with three sheets named Summary, Posts_Virtual and Posts_Human containing complete post histories from 1 January 2016 through the scrape date, with follower totals and per-post rows that include date, caption first 100 characters, content type, like count, comment count and share or repost numbers or N/A where hidden. I extracted full Instagram archives for 120 accounts into validated Excel workbooks for an influencer agency last year. Method: I will use Instaloader and Python for bulk export, pandas and openpyxl to shape the workbook, and Selenium only for posts where share metrics are dynamically hidden; each post row will include the live post URL for your spot checks. Estimated runtime: 24 to 48 hours after you provide the handle list. Do you already have the 50 virtual and 50 human handles or should I compile candidate lists for your approval? Happy to jump on a quick chat. Ali Zain

@C0RETECHIES

Hi there, I understand you need a structured historical dataset comparing the post performance of 50 virtual and 50 human Instagram influencers since 2016. The operational goal is to run a data extraction pipeline that iterates through each of the 100 accounts, pulls their complete post history, and organizes the specified metrics (likes, comments, content type, etc.) into a clean, three-sheet Excel workbook for your analysis and validation. Technical approach: I'll use a Python-based solution, likely leveraging Instaloader or a similar library that interfaces with Instagram's data endpoints. This is more stable than browser automation. The process will include robust error handling, respect for rate limits using proxies if necessary, and data processing with Pandas to structure the output. The estimated runtime for the full scrape of 100 accounts over an 8-year period is approximately 24-48 hours. Core modules: - Account Scraper: Collects current follower counts and initiates the historical scan. - Post History Extractor: Paginates through each profile's feed within the specified date range, capturing all required post-level data. - Data Processor: Cleans and sanitizes the raw data, handling missing fields (like shares) and formatting it for export. - Excel Report Generator: Constructs the final .xlsx file with the 'Summary', 'Posts_Virtual', and 'Posts_Human' sheets as specified. Implementation strategy: I will start by scraping 2-3 accounts from each category to produce a sample file for your review. This ensures the data format and accuracy meet your requirements before launching the full extraction. After your approval, I will run the complete scrape, perform a final quality assurance check, and then deliver the final workbook. Regards, Rohit

@SkillCrafts

I can efficiently extract and structure the Instagram influencer data you require, similar to a recent project where I built an automated scraper for competitor analysis, delivering actionable insights from large datasets. My solution will ensure a clean, well-organized Excel workbook meeting all your specified field requirements for both virtual and human influencers. My approach involves leveraging Python with libraries like `requests` and `BeautifulSoup` for web scraping, and `selenium` to handle dynamic content and potential authentication. I'll develop custom scripts to navigate Instagram profiles, parse post data (date, caption snippet, content type, likes, comments, shares/reposts), and handle cases where share/repost data is unavailable with a clear "N/A" marker. The data will be systematically collected and then exported into your specified Excel format, ensuring accuracy and completeness for all 100 influencers from January 1, 2016, to the completion date. To best tailor the extraction process, are there any specific Instagram API limitations or account access considerations we should be aware of? I'm confident in delivering this project accurately and efficiently. Let me know when you're available for a brief chat to discuss the next steps.

@johnallenvillan1

Hi there, You need a clean Excel workbook with 50 virtual and 50 human Instagram creators, plus full post history from 1 Jan 2016 to scrape date. I’ve spent the last 4 years solving exactly this type of data extraction problem, including Instagram profile history builds and structured Excel delivery. The real risk here is missing posts, inconsistent fields, or inaccurate share/repost data where Instagram does not expose it. I’ll avoid that by using a compliant collection workflow, validating each handle against live URLs, and normalizing every record into a consistent schema before export. I’ll build the scrape in Python with a layered approach: profile discovery, post harvesting, timestamp normalization, caption truncation, and metric capture. I’ll separate virtual and human accounts into the required sheets, then generate a Summary tab with follower totals and post counts. Where repost/share counts are unavailable, I’ll mark them clearly as N/A. I’ll keep the process isolated and documented so the workbook is easy to audit and rerun if needed. Best regards, John allen

@naeem1114

Instagram often hides share counts behind its mobile UI, so a pure API pull will miss those values for many posts. I'll use Selenium to load each post page in a headless browser, capture the share/repost field when present, and fall back to “N/A” otherwise. Instaloader will pull the remaining metadata, and I’ll write everything into one .xlsx with Summary, PostsVirtual and PostsHuman sheets. A common pitfall is hitting Instagram's rate limits when requesting thousands of posts, which can cause intermittent failures. I'll add automatic back‑off and retry logic so the scraper pauses and resumes without losing data. Ready to start immediately and deliver a clean workbook that matches your spec.