Toeing the Party Line: Election Manifestos as a Key to Understand Political Discourse on Twitter

• URL: http://arxiv.org/abs/2410.15743v1
• Date: Mon, 21 Oct 2024 08:01:46 GMT
• Title: Toeing the Party Line: Election Manifestos as a Key to Understand Political Discourse on Twitter
• Authors: Maximilian Maurer, Tanise Ceron, Sebastian Padó, Gabriella Lapesa,
• Abstract summary: We use hashtags as a signal to fine-tune text representations without the need for manual annotation. We find that our method yields stable positioning reflective of manifesto positioning, both in scenarios with all tweets of candidates. This indicates that it is possible to reliably analyze the relative positioning of actors forgoing manual annotation.
• Score: 15.698347233120993
• License:
• Abstract: Political discourse on Twitter is a moving target: politicians continuously make statements about their positions. It is therefore crucial to track their discourse on social media to understand their ideological positions and goals. However, Twitter data is also challenging to work with since it is ambiguous and often dependent on social context, and consequently, recent work on political positioning has tended to focus strongly on manifestos (parties' electoral programs) rather than social media. In this paper, we extend recently proposed methods to predict pairwise positional similarities between parties from the manifesto case to the Twitter case, using hashtags as a signal to fine-tune text representations, without the need for manual annotation. We verify the efficacy of fine-tuning and conduct a series of experiments that assess the robustness of our method for low-resource scenarios. We find that our method yields stable positioning reflective of manifesto positioning, both in scenarios with all tweets of candidates across years available and when only smaller subsets from shorter time periods are available. This indicates that it is possible to reliably analyze the relative positioning of actors forgoing manual annotation, even in the noisier context of social media.

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)
• Tracking the Newsworthiness of Public Documents [107.12303391111014]
  This work focuses on news coverage of local public policy in the San Francisco Bay Area by the San Francisco Chronicle. First, we gather news articles, public policy documents and meeting recordings and link them using probabilistic relational modeling. Second, we define a new task: newsworthiness prediction, to predict if a policy item will get covered.
  arXiv  Detail & Related papers  (2023-11-16T10:05:26Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach [9.350629400940493]
  We focus on the 2020 US presidential election and create a large-scale dataset from Twitter. To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors. We introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors.
  arXiv  Detail & Related papers  (2023-04-22T10:38:35Z)
• 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)
• 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)
• Tweets2Stance: Users stance detection exploiting Zero-Shot Learning Algorithms on Tweets [0.06372261626436675]
  The aim of the study is to predict the stance of a Party p in regard to each statement s exploiting what the Twitter Party account wrote on Twitter. Results obtained from multiple experiments show that Tweets2Stance can correctly predict the stance with a general minimum MAE of 1.13, which is a great achievement considering the task complexity.
  arXiv  Detail & Related papers  (2022-04-22T14:00:11Z)
• Identification of Twitter Bots based on an Explainable ML Framework: the US 2020 Elections Case Study [72.61531092316092]
  This paper focuses on the design of a novel system for identifying Twitter bots based on labeled Twitter data. Supervised machine learning (ML) framework is adopted using an Extreme Gradient Boosting (XGBoost) algorithm. Our study also deploys Shapley Additive Explanations (SHAP) for explaining the ML model predictions.
  arXiv  Detail & Related papers  (2021-12-08T14:12:24Z)
• Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls [56.8172499765118]
  We discuss potential sources of bias in nowcasting and forecasting. Concepts are presented to attenuate the issue of falsely perceived accuracy. One key idea is the use of Probabilities of Events instead of party shares.
  arXiv  Detail & Related papers  (2021-04-28T07:02:24Z)
• 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)
• Text-Based Ideal Points [26.981303055207267]
  We introduce the text-based ideal point model (TBIP), an unsupervised probabilistic topic model that analyzes texts to quantify the political positions of its authors. The TBIP separates lawmakers by party, learns interpretable politicized topics, and infers ideal points close to the classical vote-based ideal points. It can estimate ideal points of anyone who authors political texts, including non-voting actors.
  arXiv  Detail & Related papers  (2020-05-08T21:16:42Z)

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.