Predicting Users' Value Changes by the Friends' Influence from Social
Media Usage
- URL: http://arxiv.org/abs/2109.08021v1
- Date: Sun, 12 Sep 2021 09:17:03 GMT
- Title: Predicting Users' Value Changes by the Friends' Influence from Social
Media Usage
- Authors: Md. Saddam Hossain Mukta, Ahmed Shahriar Sakib, Md. Adnanul Islam,
Mohiuddin Ahmed, Mumshad Ahamed Rifat
- Abstract summary: Existing studies show that values of a person can be identified from their social network usage.
We propose a Bounded Confidence Model (BCM) based value dynamics model from 275 different ego networks in Facebook.
- Score: 0.6299766708197883
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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