algorithms for social media networks
INTRODUCTION
Content ranking algorithms for social media networks utilize elements, logic, and analytics to deliver useful content. A vital part of this process is the input of a team of dedicated content strategists, data scientists, and software engineers that determines the scope and criteria for classifying search results and personal feeds. While Facebook, Instagram, and LinkedIn algorithms work differently, a compression of their difference is essential in understanding their functionality, the collaboration of their implementation, and how to develop innovative social media algorithms.
COMPARISON OF SOLUTIONS
Facebook’s algorithm ranks posts based on the number of comments and shares instead of clicks and likes. As such, posts it considers interesting, valuable, and relevant appear at the top of a user’s News Feed. It uses a historical snapshot of users’ engagement with friends’ posts and brand pages to predict the content they would like to see. Besides, the Facebook algorithm prioritizes content on its platform using the following four ranking signals, including signals of what a post is about, an inventory of all posts available to display, prediction of user reactions, and a final score based on these three factors.
Following a revamp of its feed algorithm in March 2016, Instagram prioritizes content that users care about first. However, Instagram returned to a time-based algorithm in March 2018 to provide users with fresh content through timely content delivery. Although Instagram users appreciate personalized feeds based on their interaction history, clients care more about the latest content.
To sum up the contrasts between the two updates:
Significance Algorithm: Launched in March 2016, it reordered content based on the client’s relationship (a.k.a. engagement history) with the individual posts and the post’s practicality [1].
Recency Algorithm: rolled out in March 2018, this algorithm ranks user content based on a post’s timeliness. It places content in users’ feeds based on a post’s timeliness. However, Instagram’s present time-sensitive algorithm doesn’t imply that post engagement is a significant ranking factor. In any case, it implies that post times are more critical to guarantee posted content shows up in users’ feeds at the ideal time. In contrast, Instagram’s search algorithm analyses user activity to rank posts. It changes search results to reflect changing user activity.
Similarly, Linkedin’s algorithms present users with content based on individuals’ relevance. Its primary goal is to promote engagement and prioritize relevant content. LinkedIn’s algorithm groups thousands of signals to present content using three main categories, including personal identity, content engagement, and individual behavior. Its algorithm’s overall goal is to help users become more productive and thriving through the analysis of users’ connections, hashtags, page, and people followed, and the probability that users will share, comment, or react to posts topics [2].
SOCIAL MEDIA NETWORKS DATA STRUCTURES
When designing data structures for social media networks like Facebook, LinkedIn, and Instagram, the best approach would be to use an undirected graph stored as an adjacency matrix. Finding the shortest path between two users entails using a bidirectional search approach since the edges’ cost is uniform [3]. Fundamentally, this entails going around in circles centered around each user, with each circle representing a node with data connected to other nodes. Example nodes include users, events, photos, friends, follows, likes, groups, comment stories, video albums, and page ir stories. Concentric circles revolve around every user, where each circle is simply a user to locate their friends, friends of friends, and so on regression. Following this path would undoubtedly help the algorithm identify connections between platform users extending from the central circle to outer circles using the shortest path [2]. Also, one could use a depth-first search approach to overcome the limitationS of keeping track of explored nodes.
REFERENCES
[1] M. Marabelli, S. Newell, and R.D., Galliers, “Social media Affordances and constraints: Design, use and implications for enterprises,” SSRN Electronic Journal, (2018). doi.org/10.2139/ssrn.3143304
[2]Aslam, W., Butt, W., and Anwar, M., 2018. A Systematic Review on Social Network Analysis. Proceedings of the 2018 International Conference on Computing and Big Data – ICCBD ’18.
[3] Peterson-Salahuddin, C., and Diakopoulos, N., 2020. Negotiated Autonomy: The Role of Social Media Algorithms in Editorial Decision Making. Media and Communication, 8(3), pp.27-38.