相关论文: Understanding Political Polarisation using Languag…
Search engines play a crucial role in shaping public discourse by influencing how information is accessed and framed. While prior research has extensively examined various dimensions of search bias -- such as content prioritization,…
Search engines play an important role in the context of modern elections. By curating information in response to user queries, search engines influence how individuals are informed about election-related developments and perceive the media…
Large language models (LLMs) are increasingly deployed in politically sensitive settings, raising concerns about their potential to encode, amplify, or be steered toward specific ideologies. We investigate how adopting synthetic personas…
Political polarization emerges from a complex interplay of beliefs about policies, figures, and issues. However, most computational analyses reduce discourse to coarse partisan labels, overlooking how these beliefs interact. This is…
In recent years, the ease with which social media can be accessed has led to the unexpected problem of a shrinkage in information sources. This phenomenon is caused by a system that facilitates the connection of people with similar ideas…
An ability to infer the political leaning of social media users can help in gathering opinion polls thereby leading to a better understanding of public opinion. While there has been a body of research attempting to infer the political…
This paper studies an integrated system of political and economic systems from a systematic perspective to explore the complex interaction between them, and specially analyzes the case of the US presidential election forecasting. Based on…
In today's media landscape, where news outlets play a pivotal role in shaping public opinion, it is imperative to address the issue of sentiment manipulation within news text. News writers often inject their own biases and emotional…
The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a…
Research on the causes of political polarization points towards multiple drivers of the problem, from social and psychological to economic and technological. However, political institutions stand out, because -- while capable of…
Political biases encoded by LLMs might have detrimental effects on downstream applications. Existing bias analysis methods rely on small-size intermediate tasks (questionnaire answering or political content generation) and rely on the LLMs…
Has ideological polarization actually increased in the last decades, or have voters simply sorted themselves into parties matching their ideology more closely? We present a novel methodology to quantify multidimensional ideological…
We study individuals' willingness to engage with others who hold opposite views on polarizing policies. A representative sample of 2,507 Americans are given the opportunity to listen to recordings of fellow countrymen and women expressing…
The current research used the contexts of U.S. presidential debates and negotiations to examine whether matching the linguistic style of an opponent in a two-party exchange affects the reactions of third-party observers. Building off…
Search query suggestions affect users' interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to exposure to biased search results and can impact opinion…
By training deep neural networks on massive archives of digitized text, large language models (LLMs) learn the complex linguistic patterns that constitute historic and contemporary discourses. We argue that LLMs can serve as a valuable tool…
This paper presents a user modeling pipeline to analyze discussions and opinions shared on social media regarding polarized political events (e.g., public polls). The pipeline follows a four-step methodology. First, social media posts and…
This study seeks to identify and quantify biases in simulating political samples with Large Language Models, specifically focusing on vote choice and public opinion. Using the GPT-3.5-Turbo model, we leverage data from the American National…
Biased news reporting poses a significant threat to informed decision-making and the functioning of democracies. This study introduces a novel methodology for scalable, minimally biased analysis of media bias in political news. The proposed…
Politically divided societies are also often divided emotionally: people like and trust those with similar political views (in-group favoritism) while disliking and distrusting those with different views (out-group animosity). This…