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Comparative analysis on Facebook post interaction using DNN, ELM and LSTM.

Sabih Ahmad Khan1, Hsien-Tsung Chang1,2

  • 1Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan.

Plos One
|November 13, 2019
PubMed
Summary
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This study predicts social media post interactions using machine learning and advanced word embeddings like word2vec and doc2vec. It compares deep neural networks, Extreme Learning Machine, and Long Short-Term Memory for optimal prediction accuracy.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Science

Background:

  • Predicting user engagement on social media is crucial for content strategy.
  • Machine learning offers powerful tools for analyzing complex user behavior patterns.
  • Effective representation of text data, like social media posts, is key to accurate predictions.

Purpose of the Study:

  • To develop and evaluate a novel machine learning approach for predicting user interactions on social media posts.
  • To compare the efficacy of different word embedding techniques (word2vec, doc2vec) in conjunction with various machine learning models.
  • To identify the optimal combination of text representation and predictive modeling for social media interaction forecasting.

Main Methods:

  • Social media posts were converted into vector representations using word2vec and doc2vec models.

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  • Word embeddings were generated using Continuous Bag-of-Words (CBOW), skip-gram, Distributed Memory (PV-DM), and Distributed Bag of Words (PV-DBOW) models, including pre-trained Google vectors.
  • Machine learning algorithms including Deep Neural Network (DNN), Extreme Learning Machine (ELM), and Long Short-Term Memory (LSTM) were trained using combined word embeddings and post attributes (time, type, interactions) to predict total interactions.

    • Main Results:

    • The study systematically compared the performance of word2vec and doc2vec for generating word embeddings.
    • Different machine learning models (DNN, ELM, LSTM) demonstrated varying degrees of success in predicting post interactions.
    • The analysis identified specific word embedding strategies and model combinations that yielded superior prediction results.

    Conclusions:

    • The integration of sophisticated word embeddings with advanced machine learning models significantly enhances the prediction of social media user interactions.
    • The choice of word embedding technique (word2vec vs. doc2vec) and the specific model architecture (DNN, ELM, LSTM) critically impact prediction accuracy.
    • This research provides a robust framework for understanding and predicting engagement dynamics on social media platforms.