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Modeling Polarization on Social Media Posts: A Heuristic Approach Using Media Bias

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Foundations of Intelligent Systems (ISMIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13515))

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Developing machine learning models to characterize political polarization on online social media is challenging due to the lack of annotated data, noise in social media datasets, and large volume of datasets. The common research practice is to analyze the biased structure of online user communities for a given topic or to qualitatively measure the effects of polarized topics on social media. However, there is a very limited work to analyze polarization at the ground-level like the social media posts itself which are heavily dependent on annotated data. Understanding the level of political leaning in social media posts is important to quantify the bias of online user communities. In this work, we show that current machine learning models can give better performance in predicting political leaning of social media posts. We also propose two heuristics based on news media bias and post content to collect the labeled data for supervised machine learning algorithms. We experiment the proposed heuristics and machine learning approaches to study political leaning on posts collected from two ideologically diverse social media forums: Gab and Twitter without the availability of human-annotated data.

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  1. 1. bias/ media-bias-rating-methods.


  1. Baly, R., Martino, G.D.S., Glass, J., Nakov, P.: We can detect your bias: Predicting the political ideology of news articles. arXiv preprint arXiv:2010.05338 (2020)

  2. Belcastro, L., Cantini, R., Marozzo, F., Talia, D., Trunfio, P.: Learning political polarization on social media using neural networks. IEEE Access 8, 47177–47187 (2020)

    Article  Google Scholar 

  3. Bernhardt, D., Krasa, S., Polborn, M.: Political polarization and the electoral effects of media bias. J. Public Econ. 92(5–6), 1092–1104 (2008)

    Article  Google Scholar 

  4. Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on twitter: a comprehensive data collection of news articles and social interactions. In: AAAI ICWSM, vol. 13, pp. 592–597 (2019)

    Google Scholar 

  5. Cruz, A.F., Rocha, G., Cardoso, H.L.: On document representations for detection of biased news articles. In: Proceedings of the 35th Annual ACM Symposium on Applied Computing, pp. 892–899 (2020)

    Google Scholar 

  6. Fair, G., Wesslen, R.: Shouting into the void: a database of the alternative social media platform gab. In: AAAI ICWSM, vol. 13, pp. 608–610 (2019)

    Google Scholar 

  7. Garimella, V., Smith, T., Weiss, R., West, R.: Political polarization in online news consumption. In: AAAI ICWSM, pp. 152–162 (2021)

    Google Scholar 

  8. Gerrish, S.M., Blei, D.M.: Predicting legislative roll calls from text. In: ICML, pp. 489–496 (2011)

    Google Scholar 

  9. Hosseinmardi, H., Ghasemian, A., Clauset, A., Mobius, M., Rothschild, D.M., Watts, D.J.: Examining the consumption of radical content on youtube. In: Proceedings of the National Academy of Sciences, vol. 118, no. 32 (2021)

    Google Scholar 

  10. Kulkarni, V., Ye, J., Skiena, S., Wang, W.Y.: Multi-view models for political ideology detection of news articles. In: EMNLP, pp. 3518–3527 (2018)

    Google Scholar 

  11. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Workshop Proceedings of ICLR (2013)

    Google Scholar 

  12. Nair, S., Ng, K.W., Iamnitchi, A., Skvoretz, J.: Diffusion of social conventions across polarized communities: an empirical study. Soc. Netw. Anal. Min. 11(1), 1–17 (2021).

    Article  Google Scholar 

  13. Sales, A., Balby, L., Veloso, A.: Media bias characterization in Brazilian presidential elections. In: ACM Conference on Hypertext and Social Media, pp. 231–240 (2019)

    Google Scholar 

  14. Vicario, M.D., Quattrociocchi, W., Scala, A., Zollo, F.: Polarization and fake news: early warning of potential misinformation targets. ACM TWEB 13(2), 1–22 (2019)

    Article  Google Scholar 

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Correspondence to Jade Gullic or Arunkumar Bagavathi .

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Kamal, S., Gullic, J., Bagavathi, A. (2022). Modeling Polarization on Social Media Posts: A Heuristic Approach Using Media Bias. In: Ceci, M., Flesca, S., Masciari, E., Manco, G., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2022. Lecture Notes in Computer Science(), vol 13515. Springer, Cham.

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  • Print ISBN: 978-3-031-16563-4

  • Online ISBN: 978-3-031-16564-1

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