Forecasting Social Polarization in Digital Public Spheres: A Computational Social Science Approach Using Network Analysis and Machine Learning

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@article{1705669,
      author = {Dr. Y I Chawan},
      title = {Forecasting Social Polarization in Digital Public Spheres: A Computational Social Science Approach Using Network Analysis and Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {9},
      pages = {576-585},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705669.pdf},
      abstract = {This paper addresses the challenge of predicting social polarization in digital public spheres by mobilising a computational social science framework that combines large-scale social media data (limited to the period prior to March 2024) with advanced analytic techniques, explaining how the acceleration of ideological fragmentation across platforms, from Twitter/X, Facebook, and Reddit to TikTok ?has made Polarization a pressing sociological issue, with implications for democratic governance and civic trust, which in turn employed network analysis to detect and map echo chambers by tracing clusters, measuring centrality, and assessing the density of cross-ideological connections, while simultaneously deploying natural language processing to perform sentiment analysis and topic modelling across millions of public posts to capture discursive change and the emergence of antagonistic repertoires, which fed into machine learning models ?specifically time-series forecasting and supervised classification ?to test whether computational approaches could out-perform traditional tools in anticipating spikes of polarization around key issues (including electoral campaigns, pandemic governance, and climate policy debates), with results suggesting that machine learning classifiers can obtain significant accuracy in predicting early warning signals of polarization when trained on historical interaction data, especially when using features as retweet cascades, hashtag co-occurrence networks, and lexical divergence jointly, but also highlighting ongoing methodological and ethical challenges?most notably, sampling bias resulting from platform-specific affordances, the opacity of algorithmic curation systems, and the risk of overfitting models to volatile discursive events, while from a sociological perspective, findings confirm theoretical arguments that polarization cannot be reduced to a simple aggregation of individual preferences, but emerges from dynamic interaction between technological infrastructures, communicative practices, and broader structural inequalities, thus making a strong case for cross-disciplinary approaches that engage computational precision with sociological theory, and finally concluding that while computational forecasting cannot fully eliminate the uncertainty surrounding rapidly shifting digital publics, it nonetheless provides a measure of useful rigor to scholars and policymakers attempting to understand, anticipate, and ideally mitigate the social consequences of polarization in an era of algorithmically mediated communication.},
      keywords = {Computational Social Science, Social Polarization, Digital Public Spheres, Network Analysis, Machine Learning, Natural Language Processing},
      month = {March},
  }