Changes in Policy Preferences in German Tweets during the COVID Pandemic

• URL: http://arxiv.org/abs/2308.04444v1
• Date: Mon, 31 Jul 2023 16:07:28 GMT
• Title: Changes in Policy Preferences in German Tweets during the COVID Pandemic
• Authors: Felix Biessmann
• Abstract summary: We present a novel data set of tweets with fine grained political preference annotations. A text classification model trained on this data is used to extract political opinions. Results indicate that in response to the COVID pandemic, expression of political opinions increased.
• Score: 4.663960015139793
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Online social media have become an important forum for exchanging political opinions. In response to COVID measures citizens expressed their policy preferences directly on these platforms. Quantifying political preferences in online social media remains challenging: The vast amount of content requires scalable automated extraction of political preferences -- however fine grained political preference extraction is difficult with current machine learning (ML) technology, due to the lack of data sets. Here we present a novel data set of tweets with fine grained political preference annotations. A text classification model trained on this data is used to extract policy preferences in a German Twitter corpus ranging from 2019 to 2022. Our results indicate that in response to the COVID pandemic, expression of political opinions increased. Using a well established taxonomy of policy preferences we analyse fine grained political views and highlight changes in distinct political categories. These analyses suggest that the increase in policy preference expression is dominated by the categories pro-welfare, pro-education and pro-governmental administration efficiency. All training data and code used in this study are made publicly available to encourage other researchers to further improve automated policy preference extraction methods. We hope that our findings contribute to a better understanding of political statements in online social media and to a better assessment of how COVID measures impact political preferences.

Related papers

• On the Use of Proxies in Political Ad Targeting [49.61009579554272]
  We show that major political advertisers circumvented mitigations by targeting proxy attributes. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
  arXiv  Detail & Related papers  (2024-10-18T17:15:13Z)
• Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
  This study seeks to identify and quantify biases in simulating political samples with Large Language Models. Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
  arXiv  Detail & Related papers  (2024-07-16T05:52:26Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• 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)
• Diverse Perspectives Can Mitigate Political Bias in Crowdsourced Content Moderation [5.470971742987594]
  Social media companies have grappled with defining and enforcing content moderation policies surrounding political content on their platforms. It is unclear how well human labelers perform at this task, or whether biases affect this process. We experimentally evaluate the feasibility and practicality of using crowd workers to identify political content.
  arXiv  Detail & Related papers  (2023-05-23T20:10:43Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• Predicting Political Ideology from Digital Footprints [0.0]
  This paper proposes a new method to predict individual political ideology from digital footprints on one of the world's largest online discussion forum. We compiled a unique data set from the online discussion forum reddit that contains information on the political ideology of around 91,000 users.
  arXiv  Detail & Related papers  (2022-06-01T11:03:15Z)
• Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning [0.9137554315375922]
  We propose a novel unsupervised technique for learning fine-grained political leaning from social media posts. Our results pave the way for the development of new and better unsupervised approaches for the detection of fine-grained political leaning.
  arXiv  Detail & Related papers  (2022-02-23T09:18:13Z)
• Millions of Co-purchases and Reviews Reveal the Spread of Polarization and Lifestyle Politics across Online Markets [68.8204255655161]
  We study the pervasiveness of polarization and lifestyle politics over different product segments in a diverse market. We sample 234.6 million relations among 21.8 million market entities to find product categories that are politically relevant, aligned, and polarized. Cultural products are 4 times more polarized than any other segment.
  arXiv  Detail & Related papers  (2022-01-17T18:16:37Z)
• Reaching the bubble may not be enough: news media role in online political polarization [58.720142291102135]
  A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations. This study investigates whether this holds in the context of nationwide elections in Brazil and Canada.
  arXiv  Detail & Related papers  (2021-09-18T11:34:04Z)
• Inferring Political Preferences from Twitter [0.0]
  Political Sentiment Analysis of social media helps the political strategists to scrutinize the performance of a party or candidate. During the time of elections, the social networks get flooded with blogs, chats, debates and discussions about the prospects of political parties and politicians. In this work, we chose to identify the inclination of political opinions present in Tweets by modelling it as a text classification problem using classical machine learning.
  arXiv  Detail & Related papers  (2020-07-21T05:20:43Z)

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.