Predicting Users' Value Changes by the Friends' Influence from Social Media Usage

TL;DR

This paper introduces a model to predict how social media influence from friends can change users' personal values over time, utilizing a novel BCM-based approach and machine learning techniques for improved accuracy.

Contribution

It proposes a new BCM-based value dynamics model for social influence and applies hyperparameter tuning and machine learning to accurately predict value changes.

Findings

SVR outperforms other regressors in prediction accuracy

Optimized hyperparameters yield low MSE of 0.00347

Social influence significantly affects value change predictions

Abstract

Basic human values represent a set of values such as security, independence, success, kindness, and pleasure, which we deem important to our lives. Each of us holds different values with different degrees of significance. Existing studies show that values of a person can be identified from their social network usage. However, the value priority of a person may change over time due to different factors such as life experiences, influence, social structure and technology. Existing studies do not conduct any analysis regarding the change of users' value from the social influence, i.e., group persuasion, form the social media usage. In our research, first, we predict users' value score by the influence of friends from their social media usage. We propose a Bounded Confidence Model (BCM) based value dynamics model from 275 different ego networks in Facebook that predicts how social influence may persuade a person to change their value over time. Then, to predict better, we use particle swarm optimization based hyperparameter tuning technique. We observe that these optimized hyperparameters produce accurate future value score. We also run our approach with different machine learning based methods and find support vector regression (SVR) outperforms other regressor models. By using SVR with the best hyperparameters of BCM model, we find the lowest Mean Squared Error (MSE) score 0.00347.