Mr.KiranAbasahebShejul, Dr.ManishaPatil

DOI: https://doi.org/10.5281/zenodo.22725553

Amongtheyoungadultssocialmediahasbecomeanimportantpartofeverydaycommunication, information sharing, entertainment, and social interaction. At the same time to recommend content, personalize feeds, rank posts ,and influence what users see and interact with artificial intelligence(AI)is increasingly used by social media platforms. AI and machine- learning methods are also being used by researchers to understand and predict user engagement. However, the available research is spread across different areas. Some studies focus on psychological and behavioral factors among young adults, while others concentrate on prediction models, temporal patterns, recommendation algorithms, or explainable AI .This integrative review brings these research streams together .Analysis was performed on twenty-four studies from 2017 to 2026. The studies have been compared based on their population, platforms, data, engagement indicators, behavioral indicators,predictivevariables,methodology,machinelearningmodels, results, and limitations. The literature was organized into six themes: themeaningandmeasurementofsocialmediaengagement;behavioralandpsychological factorsamongyoungadults;AIandmachine-learningmethodsforengagementprediction; temporal, contextual, content, and multi modal features; theinfluenceofAIrecommendationsystems;andexplainability,privacy,andresponsibleAI.The review shows that engagement is not a single behavior. Passive viewing, liking, commenting, sharing, and content creation havedifferentcharacteristicsandmayrequiredifferentpredictionstrategies.Studiesalsoshowthatpersonality,boredom, information overload, emotion regulation, social influence, previous activity, content characteristics, posting time, and algorithmic recommendations can affect engagement. Machine learning algorithms such as Random Forest and Gradient Boosting are appropriate for structured data where as LSTMs and other deep learning algorithms are appropriate for sequential data. Recently, there has also been some attention paid to the usage of Transformers and other graph-based algorithms.Regardlessoftheseadvancements,notmanystudieshavebeencarriedoutwhichintegratespecificbehaviorsof young adults with multi dimensional engagement, temporal and contextual data, AI predictions, and explainability. Moreover,crossplatformvalidation,privacy,fairness,andexplainabilityofthemodelsisyettobetakenintoconsideration. Thus,thisreviewsuggestsaframeworkfortheuseofAIfromyoungadult-centricandcontext-awareperspective.

Artificial Intelligence;SocialMediaEngagement;YoungAdults;MachineLearning;EngagementPrediction; UserBehavior;ExplainableAI;RecommendationSystems;PredictiveAnalytics

This paper presents an AI-based framework for understanding and predicting social-media engagement among young adults. The framework treats engagement as a group of different behaviours. It combines personal, behavioural, emotional, content, context, relationship and algorithm-related information. Researchers can select models that match their data. Explainability and responsible-AI rules are central parts of the framework. The propositions, measurement table and evaluation plan make future testing possible. The framework connects human behaviour with AI prediction. It supports research that is accurate, clear, transparent and responsible.

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Mr.KiranAbasahebShejul, Dr.ManishaPatil (2026). AConceptualAI-BasedFrameworkforUnderstandingand PredictingSocialMediaEngagementamongYoungAdults. International Journal of Computer Techniques, 13(5), 352–358. ISSN: 2394-2231. DOI: https://doi.org/10.5281/zenodo.22725553