Quantifying Polarization: A Comparative Study of Measures and Methods

• URL: http://arxiv.org/abs/2501.07473v1
• Date: Mon, 13 Jan 2025 16:43:23 GMT
• Title: Quantifying Polarization: A Comparative Study of Measures and Methods
• Authors: Edoardo Di Martino, Matteo Cinelli, Roy Cerqueti, Walter Quattrociocchi,
• Abstract summary: Political polarization, a key driver of social fragmentation, has drawn increasing attention for its role in shaping online and offline discourse. This study evaluates five widely used polarization measures, testing their strengths and weaknesses with synthetic datasets. We present a novel adaptation of Kleinberg's burst detection algorithm to improve mode detection in polarized distributions.
• Score: 2.0249250133493195
• License:
• Abstract: Political polarization, a key driver of social fragmentation, has drawn increasing attention for its role in shaping online and offline discourse. Despite significant efforts, accurately measuring polarization within ideological distributions remains a challenge. This study evaluates five widely used polarization measures, testing their strengths and weaknesses with synthetic datasets and a real-world case study on YouTube discussions during the 2020 U.S. Presidential Election. Building on these findings, we present a novel adaptation of Kleinberg's burst detection algorithm to improve mode detection in polarized distributions. By offering both a critical review and an innovative methodological tool, this work advances the analysis of ideological patterns in social media discourse.

Related papers

• In-Group Love, Out-Group Hate: A Framework to Measure Affective Polarization via Contentious Online Discussions [2.8963943201523796]
  We introduce a discrete choice model that captures decision-making within affectively polarized social networks. We propose a statistical inference method estimate key parameters -- in-group love and out-group hate -- from social media data.
  arXiv  Detail & Related papers  (2024-12-18T23:58:13Z)
• A Survey of Stance Detection on Social Media: New Directions and Perspectives [50.27382951812502]
  stance detection has emerged as a crucial subfield within affective computing. Recent years have seen a surge of research interest in developing effective stance detection methods. This paper provides a comprehensive survey of stance detection techniques on social media.
  arXiv  Detail & Related papers  (2024-09-24T03:06:25Z)
• Sampled Datasets Risk Substantial Bias in the Identification of Political Polarization on Social Media [34.192291430580454]
  We study the structural polarization of the Polish political debate on Twitter over a 24-hour period. Large samples can be representative of the whole political discussion on a platform, but small samples consistently fail to accurately reflect the true structure of polarization online.
  arXiv  Detail & Related papers  (2024-06-28T12:13:29Z)
• Ethos and Pathos in Online Group Discussions: Corpora for Polarisation Issues in Social Media [6.530320465510631]
  Growing polarisation in society caught the attention of the scientific community as well as news media. We propose to approach the problem by investigating rhetorical strategies employed by individuals in polarising discussions online. We develop multi-topic and multi-platform corpora with manual annotation of appeals to ethos and pathos, two modes of persuasion in Aristotelian rhetoric.
  arXiv  Detail & Related papers  (2024-04-07T09:10:47Z)
• Polarity Calibration for Opinion Summarization [46.83053173308394]
  Polarity calibration aims to align the polarity of output summary with that of input text. We evaluate our model on two types of opinions summarization tasks: summarizing product reviews and political opinions articles.
  arXiv  Detail & Related papers  (2024-04-02T07:43:12Z)
• Mitigating Framing Bias with Polarity Minimization Loss [56.24404488440295]
  Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. We propose a new loss function that encourages the model to minimize the polarity difference between the polarized input articles to reduce framing bias.
  arXiv  Detail & Related papers  (2023-11-03T09:50:23Z)
• Off-Policy Evaluation for Large Action Spaces via Policy Convolution [60.6953713877886]
  Policy Convolution family of estimators uses latent structure within actions to strategically convolve the logging and target policies. Experiments on synthetic and benchmark datasets demonstrate remarkable mean squared error (MSE) improvements when using PC.
  arXiv  Detail & Related papers  (2023-10-24T01:00:01Z)
• PARTNER: Level up the Polar Representation for LiDAR 3D Object Detection [81.16859686137435]
  We present PARTNER, a novel 3D object detector in the polar coordinate. Our method outperforms the previous polar-based works with remarkable margins of 3.68% and 9.15% on and ONCE validation set.
  arXiv  Detail & Related papers  (2023-08-08T01:59:20Z)
• Network polarization, filter bubbles, and echo chambers: An annotated review of measures and reduction methods [0.0]
  Polarization arises when the underlying network becomes characterized by highly connected groups with weak inter-group connectivity. This work presents an annotated review of network polarization measures and models used to handle the polarization.
  arXiv  Detail & Related papers  (2022-07-27T21:23:27Z)
• Designing Recommender Systems to Depolarize [0.32634122554913997]
  Polarization is implicated in the erosion of democracy and the progression to violence. While algorithm-driven social media does not seem to be a primary driver of polarization at the country level, it could be a useful intervention point in polarized societies. This paper examines algorithmic depolarization interventions with the goal of conflict transformation.
  arXiv  Detail & Related papers  (2021-07-11T03:23:42Z)
• Sentiment Analysis Based on Deep Learning: A Comparative Study [69.09570726777817]
  The study of public opinion can provide us with valuable information. The efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems.
  arXiv  Detail & Related papers  (2020-06-05T16:28:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.