Analysis of Persian News Agencies on Instagram, A Words Co-occurrence Graph-based Approach

Document Type : Original Article


1 School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

2 Assistant Professor, School of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran


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.


Main Subjects

[1]    A. Rejeb, K. Rejeb, A. Abdollahi, and H. Treiblmaier, “The big picture on Instagram research: Insights from a bibliometric analysis,” Telemat. Informatics, vol. 73, no. December 2021, p. 101876, 2022,
[2]    C. Wartena, R. Brussee, and W. Slakhorst, “Keyword extraction using word co-occurrence,” Proc. - 21st Int. Work. Database Expert Syst. Appl. DEXA 2010, IEEE, 2010, pp. 54–58,
[3]    K. S. Hasan and V. Ng, “Automatic keyphrase extraction: A survey of the state of the art,” 52nd Annu. Meet. Assoc. Comput. Linguist. ACL 2014 - Proc. Conf., vol. 1, pp. 1262–1273, 2014,
[4]    F. Liu, D. Pennell, F. Liu, and Y. Liu, “Unsupervised approaches for automatic keyword extraction using meeting transcripts,” NAACL HLT 2009 - Hum. Lang. Technol. 2009 Annu. Conf. North Am. Chapter Assoc. Comput. Linguist. Proc. Conf., IEEE, January 2009, pp. 620–628,
[5]    S. Mirasdar and M. Bedekar, “Graph of Words Model for Natural Language Processing,” in Graph Learning and Network Science for Natural Language Processing, CRC Press, 2023, pp. 1–20.
[6]    M. Garg, A survey on different dimensions for graphical keyword extraction techniques: Issues and Challenges, vol. 54, no. 6, pp. 4731–4770, 2021.
[7]    M. I. Fudolig, T. Alshaabi, M. V. Arnold, C. M. Danforth, and P. S. Dodds, “Sentiment and structure in word co-occurrence networks on Twitter,” Appl. Netw. Sci., vol. 7, no. 1, pp. 1–18, 2022,
[8]    R. Futrell, “Estimating word co-occurrence probabilities from pretrained static embeddings using a log-bilinear model,” C. 2022 - Work. Cogn. Model. Comput. Linguist. Proc. Work., pp. 54–60, 2022,
[9]    J. Gabín, M. E. Ares, and J. Parapar, “Keyword Embeddings for Query Suggestion,” In European Conference on Information Retrieval, Cham: Springer Nature Switzerland, 2023, pp. 346–360,
[10]   D. A. Vega-Oliveros, P. S. Gomes, E. E. Milios, and L. Berton, “A multi-centrality index for graph-based keyword extraction,” Inf. Process. Manag., vol. 56, no. 6, p. 102063, 2019,
[11]   S. K. Biswas, M. Bordoloi, and J. Shreya, “A graph based keyword extraction model using collective node weight,” Expert Syst. Appl., vol. 97, pp. 51–59, 2018,
[12]   W. Li, J. Xue, X. Zhang, H. Chen, and Z. Chen, “Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk sampling,” arXiv preprint arXiv:2301.04312, pp. 1–16.
[13]   H. Hettiarachchi, M. Adedoyin-olowe, J. Bhogal, and M. M. Gaber, “WhatsUp : An event resolution approach for co-occurring events in social media,” Inf. Sci. (Ny)., vol. 625, pp. 553–577, 2023,
[14]   R. Nazar and D. Lindemann, “Terminology extraction using co-occurrence patterns as predictors of semantic relevance,” In Proceedings of the Workshop on Terminology in the 21st century: many faces, many places, 2022, pp. 26–29.
[15]   F. Valentini, D. F. Slezak, and E. Altszyler, “The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings,” arXiv preprint arXiv:2301.00792, 2023.
[16]   R. Mihalcea and P. Tarau, “TextRank: Bringing order into texts,” Proc. 2004 Conf. Empir. Methods Nat. Lang. Process. EMNLP 2004 - A Meet. SIGDAT, a Spec. Interes. Gr. ACL held conjunction with ACL 2004, vol. 85, 2004, pp. 404–411.
[17]   A. Xiong, D. Liu, H. Tian, Z. Liu, P. Yu, and M. Kadoch, “News keyword extraction algorithm based on semantic clustering and word graph model,” Tsinghua Sci. Technol., vol. 26, no. 6, pp. 886–893, 2021,
[18]   J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv Prepr. arXiv1810.04805, 2018.
[19]   L. Chi and L. Hu, “ISKE: An unsupervised automatic keyphrase extraction approach using the iterated sentences based on graph method,” Knowledge-Based Syst., vol. 223, p. 107014, 2021,
[20]   L. Gan, Z. Teng, Y. Zhang, L. Zhu, F. Wu, and Y. Yang, “SemGloVe: Semantic Co-Occurrences for GloVe From BERT,” IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 30, pp. 2696–2704, 2022,
[21]   S. Anjali, N. M. Meera, and M. G. Thushara, “A Graph based Approach for Keyword Extraction from Documents,” in 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 2019, pp. 1–4,
[22]   L. Zhao, Z. Miao, C. Wang, and W. Kong, “An Unsupervised Keyword Extraction Method based on Text Semantic Graph,” in 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC ), 2022, pp. 1431–1436,
[23]   D. Brock, A. Khan, T. Doan, A. Lin, Y. Guo, and P. Tarau, “Textstar: a Fast and Lightweight Graph-Based Algorithm for Extractive Summarization and Keyphrase Extraction,” Proc. 20th Annu. Work. Australas. Lang. Technol. Assoc., no. 3, pp. 161–169, 2022, [Online]. Available:
[24]   Y. Ying, T. Qingping, X. Qinzheng, Z. Ping, and L. Panpan, “A Graph-based Approach of Automatic Keyphrase Extraction,” Procedia Comput. Sci., vol. 107, no. Icict, pp. 248–255, 2017,