The rise of the Internet and the exponential increase in data have made manual data summarization and analysis a challenging task. Instagram social network is a prominent social network widely utilized in Iran for information sharing and communication across various age groups. The inherent structure of Instagram, characterized by its text-rich content and graph-like data representation, enables the utilization of text and graph processing techniques for data analysis purposes. The degree distributions of these networks exhibit scale-free characteristics, indicating non-random growth patterns. Recently, word co-occurrence has gained attention from researchers across multiple disciplines due to its simplicity and practicality. Keyword extraction is a crucial task in natural language processing. In this study, we demonstrated that high-precision extraction of keywords from Instagram posts in the Persian language can be achieved using unsupervised word co-occurrence methods without resorting to conventional techniques such as clustering or pre-trained models. After graph visualization and community detection, it was observed that the top topics covered by news agencies are represented by these graphs. This approach is generalizable to new and diverse datasets and can provide acceptable outputs for new data. To the author's knowledge, this method has not been employed in the Persian language before on Instagram social network. The new crawled data has been publicly released on GitHub for exploration by other researchers. By employing this method, it is possible to use other graph-based algorithms, such as community detections. The results help us to identify the key role of different news agencies in information diffusion among the public, identify hidden communities, and discover latent patterns among a massive amount of data.

翻译：互联网的兴起与数据的指数级增长使得手动数据摘要与分析成为一项挑战性任务。Instagram社交网络是伊朗各年龄段群体广泛用于信息共享与交流的重要社交平台。其以文本丰富内容与图结构数据表征为特色的内在架构，使得文本与图处理技术能够应用于数据分析。这些网络的度分布呈现无标度特性，表明其增长模式非随机。近年来，词共现因其简洁性与实用性而受到多学科研究者的关注。关键词提取是自然语言处理中的关键任务。本研究表明，采用无监督词共现方法（无需依赖聚类或预训练模型等传统技术）即可实现对Instagram波斯语帖子的高精度关键词提取。经过图可视化和社区检测后发现，新闻机构报道的核心主题可通过这些图结构呈现。该方法可推广至新颖多样的数据集，并能对新数据提供可接受的输出结果。据作者所知，该方法此前尚未被应用于Instagram社交网络的波斯语场景。我们新爬取的数据集已在GitHub上公开，供其他研究者探索。通过此方法，可进一步应用社区检测等基于图的算法。研究结果有助于识别不同新闻机构在公众信息传播中的关键作用、发现隐藏社区，并在海量数据中揭示潜在模式。