  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.
