Yash Shukla,
Sameer Awasthi,
Yogesh Shukla,
Samraddhi Saurabh Pathak,
Karan Verma,
- Student, Department of Computer Science and Engineering – Artificial Intelligence Machine Learning (CSE-AIML), Bansal Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India
- Head, Department of Computer Science and Engineering – Artificial Intelligence Machine Learning (CSE-AIML), Bansal Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India
- Student, Department of Computer Science and Engineering – Artificial Intelligence Machine Learning (CSE-AIML), Bansal Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India
- Student, Department of Computer Science and Engineering – Artificial Intelligence Machine Learning (CSE-AIML), Bansal Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India
- Student, Department of Computer Science and Engineering – Artificial Intelligence Machine Learning (CSE-AIML), Bansal Institute of Engineering and Technology, Lucknow, Uttar Pradesh, India
Abstract
With the rapid growth of Instagram as a dominant social media platform, there is an increasing need for tools that allow users to extract, analyze, and monitor profile data efficiently. Instagram has become one of the leading platforms for personal branding, influencer marketing, business promotion, and public communication. As businesses and individuals seek to better understand their presence and influence on this platform, the need for reliable data extraction tools has never been greater. The Instaloader package, a Python-based tool, provides robust functionalities for scraping publicly available data from Instagram, making it particularly useful for researchers, digital marketers, social media managers, and developers who require access to structured data for analysis and decision-making. This research paper explores the design, development, and implementation of an Instagram Profile Checker Tool using Instaloader. The tool is designed to enable users to retrieve essential profile details, including username, biography (bio), follower count, following count, total posts, and key engagement metrics such as average likes, comments, and post frequency. By compiling this data, the tool assists in the formulation of digital marketing strategies, detailed user behavior analysis, influencer profiling, and social listening activities. It provides an easy-to-use interface for obtaining critical information, making it accessible for both technical and non-technical users who need insights into Instagram profiles. This information can help businesses refine content strategies, monitor competitors, and select appropriate influencers for collaborations based on genuine engagement metrics rather than superficial follower counts. Furthermore, the research discusses key limitations of the tool, including restrictions related to private accounts, data accessibility limitations, rate-limiting by Instagram, and the potential impact of changes in Instagram’s policies or API (application programming interface) structures that could affect future tool performance. Finally, the paper discusses potential future enhancements, such as integrating artificial intelligence and machine learning techniques for advanced sentiment analysis, automated content classification, and predictive analytics to further strengthen the tool’s capabilities and provide even deeper insights into user behavior and content performance.
Keywords: Instagram, Instaloader, authenticating Instagram profile, web scraping, Python, social media analytics
[This article belongs to Journal of Computer Technology & Applications ]
Yash Shukla, Sameer Awasthi, Yogesh Shukla, Samraddhi Saurabh Pathak, Karan Verma. A Comprehensive Tool for Authenticating Instagram Profiles Using Instaloader. Journal of Computer Technology & Applications. 2025; 16(02):93-98.
Yash Shukla, Sameer Awasthi, Yogesh Shukla, Samraddhi Saurabh Pathak, Karan Verma. A Comprehensive Tool for Authenticating Instagram Profiles Using Instaloader. Journal of Computer Technology & Applications. 2025; 16(02):93-98. Available from: https://journals.stmjournals.com/jocta/article=2025/view=209319
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Journal of Computer Technology & Applications
| Volume | 16 |
| Issue | 02 |
| Received | 28/02/2025 |
| Accepted | 14/04/2025 |
| Published | 24/04/2025 |
| Publication Time | 55 Days |
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