Modeling Political Orientation of Social Media Posts: An Extended Analysis

• URL: http://arxiv.org/abs/2311.12323v1
• Date: Tue, 21 Nov 2023 03:34:20 GMT
• Title: Modeling Political Orientation of Social Media Posts: An Extended Analysis
• Authors: Sadia Kamal, Brenner Little, Jade Gullic, Trevor Harms, Kristin Olofsson, Arunkumar Bagavathi
• Abstract summary: Developing machine learning models to characterize political polarization on online social media presents significant challenges. These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data. We introduce two methods that leverage on news media bias and post content to label social media posts. We demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing machine learning models to characterize political polarization on online social media presents significant challenges. These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data. The common research practice typically examines the biased structure of online user communities for a given topic or qualitatively measuring the impacts of polarized topics on social media. However, there is limited work focusing on analyzing polarization at the ground-level, specifically in the social media posts themselves. Such existing analysis heavily relies on annotated data, which often requires laborious human labeling, offers labels only to specific problems, and lacks the ability to determine the near-future bias state of a social media conversations. Understanding the degree of political orientation conveyed in social media posts is crucial for quantifying the bias of online user communities and investigating the spread of polarized content. In this work, we first introduce two heuristic methods that leverage on news media bias and post content to label social media posts. Next, we compare the efficacy and quality of heuristically labeled dataset with a randomly sampled human-annotated dataset. Additionally, we demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts, employing both traditional supervised learning and few-shot learning setups. We conduct experiments using the proposed heuristic methods and machine learning approaches to predict the political orientation of posts collected from two social media forums with diverse political ideologies: Gab and Twitter.

Related papers

• 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)
• Intertwined Biases Across Social Media Spheres: Unpacking Correlations in Media Bias Dimensions [12.588239777597847]
  Media bias significantly shapes public perception by reinforcing stereotypes and exacerbating societal divisions. We introduce a novel dataset collected from YouTube and Reddit over the past five years. Our dataset includes automated annotations for YouTube content across a broad spectrum of bias dimensions.
  arXiv  Detail & Related papers  (2024-08-27T21:03:42Z)
• 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)
• Quantitative Analysis of Forecasting Models:In the Aspect of Online Political Bias [0.0]
  We propose a approach to classify social media posts into five distinct political leaning categories. Our approach involves utilizing existing time series forecasting models on two social media datasets with different political ideologies.
  arXiv  Detail & Related papers  (2023-09-11T16:17:24Z)
• Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
  We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs) We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing. We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
  arXiv  Detail & Related papers  (2023-09-02T00:32:55Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• Self-supervised Hypergraph Representation Learning for Sociological Analysis [52.514283292498405]
  We propose a fundamental methodology to support the further fusion of data mining techniques and sociological behavioral criteria. First, we propose an effective hypergraph awareness and a fast line graph construction framework. Second, we propose a novel hypergraph-based neural network to learn social influence flowing from users to users.
  arXiv  Detail & Related papers  (2022-12-22T01:20:29Z)
• Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction [12.472629584751509]
  It is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations. We introduce a self-attentive semi-supervised framework for political inclination detection to further that objective. We found that the model is highly efficient even in resource-constrained settings.
  arXiv  Detail & Related papers  (2022-09-21T12:07:16Z)
• A Machine Learning Pipeline to Examine Political Bias with Congressional Speeches [0.3062386594262859]
  We give machine learning approaches to study political bias in two ideologically diverse social media forums: Gab and Twitter. Our proposed methods exploit the use of transcripts collected from political speeches in US congress to label the data. We also present a machine learning approach that combines features from cascades and text to forecast cascade's political bias with an accuracy of about 85%.
  arXiv  Detail & Related papers  (2021-09-18T21:15:21Z)
• Two-Faced Humans on Twitter and Facebook: Harvesting Social Multimedia for Human Personality Profiling [74.83957286553924]
  We infer the Myers-Briggs Personality Type indicators by applying a novel multi-view fusion framework, called "PERS" Our experimental results demonstrate the PERS's ability to learn from multi-view data for personality profiling by efficiently leveraging on the significantly different data arriving from diverse social multimedia sources.
  arXiv  Detail & Related papers  (2021-06-20T10:48:49Z)
• I Know Where You Are Coming From: On the Impact of Social Media Sources on AI Model Performance [79.05613148641018]
  We will study the performance of different machine learning models when being learned on multi-modal data from different social networks. Our initial experimental results reveal that social network choice impacts the performance.
  arXiv  Detail & Related papers  (2020-02-05T11:10:44Z)

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.