Unpacking the Black Box: An Exploration of the Algorithms Driving Social Media Networks-A Review


Authors : Amey Thorat; Amulya Kura; Ansika Jaiswal; Sapana Survase; Amit Aylani

Volume/Issue : Volume 8 - 2023, Issue 5 - May

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://rb.gy/sdzbh

DOI : https://doi.org/10.5281/zenodo.7960559

Social media algorithms are used for finding detailed information in large unstructured data by relevant keywords used by users. There are different algorithms used for social media from a searching point of view. One of the algorithms is the "Probability of Node's Degree" algorithm, which is based on the concept of breadth-first search, random walk, and the highest degree seeking algorithm. The algorithm involves selecting a source node and a target node, and then traversing the nodes in the network to find the target node. The algorithm checks if the target node is a neighbor of the current node and, if not, transmits a query message to other nodes based on their probability of being relevant to the search. Nodes with higher degrees are more likely to be searched, making the algorithm beneficial to nodes with higher degrees. In addition to this, there are other algorithms such as FP-FOREST, DSTree, UPTree algorithm, and KC-LA, which are used for finding frequent patterns, maintaining and mining frequent item sets, and finding K-Clique in complex social networks. These algorithms are useful in datadriven decision-making and in gaining insights into social media analytics.

Keywords : Social Media Algorithm, Social Media Analytics, Complex Social Network, Social Media, K-Clique, Learning Automation, Betweenness Centrality, Random Walk.

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