Feedback dynamics in Politics: The interplay between sentiment and engagement

• URL: http://arxiv.org/abs/2511.02663v1
• Date: Tue, 04 Nov 2025 15:41:04 GMT
• Title: Feedback dynamics in Politics: The interplay between sentiment and engagement
• Authors: Simone Formentin,
• Abstract summary: We identify sentiment dynamics through a simple yet interpretable linear model.<n>The analysis reveals a closed-loop behavior: engagement with positive and negative messages influences the sentiment of subsequent posts.
• Score: 0.38580784887142777
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate feedback mechanisms in political communication by testing whether politicians adapt the sentiment of their messages in response to public engagement. Using over 1.5 million tweets from Members of Parliament in the United Kingdom, Spain, and Greece during 2021, we identify sentiment dynamics through a simple yet interpretable linear model. The analysis reveals a closed-loop behavior: engagement with positive and negative messages influences the sentiment of subsequent posts. Moreover, the learned coefficients highlight systematic differences across political roles: opposition members are more reactive to negative engagement, whereas government officials respond more to positive signals. These results provide a quantitative, control-oriented view of behavioral adaptation in online politics, showing how feedback principles can explain the self-reinforcing dynamics that emerge in social media discourse.

Related papers

• Prioritize Economy or Climate Action? Investigating ChatGPT Response Differences Based on Inferred Political Orientation [0.0]
  This study explores how inferred political views impact the responses of ChatGPT globally, regardless of the chat session.<n>We develop three personas (two politically oriented and one neutral) with four statements reflecting their viewpoints on DEI programs, abortion, gun rights, and vaccination.<n>We convey the personas' remarks to ChatGPT using memory and custom instructions, allowing it to infer their political perspectives without directly stating them.
  arXiv  Detail & Related papers  (2025-11-04T21:07:01Z)
• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• Unveiling Political Influence Through Social Media: Network and Causal Dynamics in the 2022 French Presidential Election [0.0]
  During the 2022 French presidential election, we collected daily Twitter messages on key topics posted by political candidates and their close networks.<n>Using a data-driven approach, we analyze interactions among political parties, identifying central topics that shape the landscape of political debate.<n>Our findings demonstrate how specific issues, such as health and foreign policy, act as catalysts for cross-party influence, particularly during critical election phases.
  arXiv  Detail & Related papers  (2025-06-19T16:35:02Z)
• Toxic behavior silences online political conversations [0.0]
  We investigate the hypothesis that individuals may refrain from expressing minority opinions publicly due to being exposed to toxic behavior.<n>Using hidden Markov models, we identify a latent state consistent with toxicity-driven silence.<n>Our findings offer insights into the intricacies of online political deliberation and emphasize the importance of considering self-censorship dynamics.
  arXiv  Detail & Related papers  (2024-12-07T20:39:20Z)
• Sentiment Analysis of Spanish Political Party Tweets Using Pre-trained Language Models [2.754736377001085]
  This study investigates sentiment patterns within Spanish political party communications on Twitter by leveraging BETO and RoBERTuito. Using a dataset of tweets from major Spanish political parties, this research analyzes sentiment distributions and explores the relationship between sentiment expression and party ideology.
  arXiv  Detail & Related papers  (2024-11-07T16:53:09Z)
• Aligning AI with Public Values: Deliberation and Decision-Making for Governing Multimodal LLMs in Political Video Analysis [48.14390493099495]
  How AI models should deal with political topics has been discussed, but it remains challenging and requires better governance.<n>This paper examines the governance of large language models through individual and collective deliberation, focusing on politically sensitive videos.
  arXiv  Detail & Related papers  (2024-09-15T03:17:38Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• Measuring the Effect of Influential Messages on Varying Personas [67.1149173905004]
  We present a new task, Response Forecasting on Personas for News Media, to estimate the response a persona might have upon seeing a news message. The proposed task not only introduces personalization in the modeling but also predicts the sentiment polarity and intensity of each response. This enables more accurate and comprehensive inference on the mental state of the persona.
  arXiv  Detail & Related papers  (2023-05-25T21:01:00Z)
• The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning [50.24983453990065]
  We use a deep-learning approach to process a sample of 220 YouTube videos of political leaders from 15 different countries.<n>We observe statistically significant differences in the average score of negative emotions between groups of leaders with varying degrees of populist rhetoric.
  arXiv  Detail & Related papers  (2023-04-19T18:32:49Z)
• Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political Communication [7.136205674624813]
  This paper attempts to analyse tweets of politicians from three European countries. By utilising state-of-the-art pre-trained language models, we performed sentiment analysis on hundreds of thousands of tweets. Our analysis indicates that politicians' negatively charged tweets spread more widely, especially in more recent times.
  arXiv  Detail & Related papers  (2022-02-01T13:25:19Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)

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.