Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion

• URL: http://arxiv.org/abs/2212.09056v1
• Date: Sun, 18 Dec 2022 10:18:15 GMT
• Title: Beyond Digital "Echo Chambers": The Role of Viewpoint Diversity in Political Discussion
• Authors: Rishav Hada, Amir Ebrahimi Fard, Sarah Shugars, Federico Bianchi, Patricia Rossini, Dirk Hovy, Rebekah Tromble, Nava Tintarev
• Abstract summary: We operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. We find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers.
• Score: 24.49418802276767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Increasingly taking place in online spaces, modern political conversations are typically perceived to be unproductively affirming -- siloed in so called ``echo chambers'' of exclusively like-minded discussants. Yet, to date we lack sufficient means to measure viewpoint diversity in conversations. To this end, in this paper, we operationalize two viewpoint metrics proposed for recommender systems and adapt them to the context of social media conversations. This is the first study to apply these two metrics (Representation and Fragmentation) to real world data and to consider the implications for online conversations specifically. We apply these measures to two topics -- daylight savings time (DST), which serves as a control, and the more politically polarized topic of immigration. We find that the diversity scores for both Fragmentation and Representation are lower for immigration than for DST. Further, we find that while pro-immigrant views receive consistent pushback on the platform, anti-immigrant views largely operate within echo chambers. We observe less severe yet similar patterns for DST. Taken together, Representation and Fragmentation paint a meaningful and important new picture of viewpoint diversity.

Related papers

• When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models [3.6329973651062297]
  We develop a computational approach to measure metaphorical language. We identify seven concepts evoked in immigration discourse. We then study the relationship between metaphor, political ideology, and user engagement in 400K US tweets about immigration.
  arXiv  Detail & Related papers  (2025-02-18T19:19:01Z)
• PanoSent: A Panoptic Sextuple Extraction Benchmark for Multimodal Conversational Aspect-based Sentiment Analysis [74.41260927676747]
  This paper bridges the gaps by introducing a multimodal conversational Sentiment Analysis (ABSA) To benchmark the tasks, we construct PanoSent, a dataset annotated both manually and automatically, featuring high quality, large scale, multimodality, multilingualism, multi-scenarios, and covering both implicit and explicit sentiment elements. To effectively address the tasks, we devise a novel Chain-of-Sentiment reasoning framework, together with a novel multimodal large language model (namely Sentica) and a paraphrase-based verification mechanism.
  arXiv  Detail & Related papers  (2024-08-18T13:51:01Z)
• Overview of PerpectiveArg2024: The First Shared Task on Perspective Argument Retrieval [56.66761232081188]
  We present a novel dataset covering demographic and socio-cultural (socio) variables, such as age, gender, and political attitude, representing minority and majority groups in society. We find substantial challenges in incorporating perspectivism, especially when aiming for personalization based solely on the text of arguments without explicitly providing socio profiles. While we bootstrap perspective argument retrieval, further research is essential to optimize retrieval systems to facilitate personalization and reduce polarization.
  arXiv  Detail & Related papers  (2024-07-29T03:14:57Z)
• The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented Intervention [61.80236015147771]
  We quantify the trade-off between using diversity interventions and preserving demographic factuality in T2I models. Experiments on DoFaiR reveal that diversity-oriented instructions increase the number of different gender and racial groups. We propose Fact-Augmented Intervention (FAI) to reflect on verbalized or retrieved factual information about gender and racial compositions of generation subjects in history.
  arXiv  Detail & Related papers  (2024-06-29T09:09:42Z)
• 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)
• A critical appraisal of equity in conversational AI: Evidence from auditing GPT-3's dialogues with different publics on climate change and Black Lives Matter [17.549208519206605]
  This paper proposes an analytical framework for unpacking the meaning of equity in human-AI dialogues. Our corpus consists of over 20,000 rounds of dialogues between GPT-3 and 3290 individuals. We found a substantively worse user experience with GPT-3 among the opinion and the education minority subpopulations.
  arXiv  Detail & Related papers  (2022-09-27T18:44:41Z)
• Unraveling Social Perceptions & Behaviors towards Migrants on Twitter [1.6904475483445451]
  We identify two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users towards migrants. Our proposed transformer-based model, i.e., BERT + CNN, has reported an F1-score of 0.76 and outper-formed other models.
  arXiv  Detail & Related papers  (2021-12-04T20:45:26Z)
• Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions [10.112779201155005]
  We present the new Us vs. Them dataset, consisting of 6861 Reddit comments annotated for populist attitudes. We investigate the relationship between populist mindsets and social groups, as well as a range of emotions typically associated with these. We present a set of multi-task learning models that leverage and demonstrate the importance of emotion and group identification as auxiliary tasks.
  arXiv  Detail & Related papers  (2021-01-28T12:18:19Z)
• M2P2: Multimodal Persuasion Prediction using Adaptive Fusion [65.04045695380333]
  This paper solves two problems: the Debate Outcome Prediction (DOP) problem predicts who wins a debate and the Intensity of Persuasion Prediction (IPP) problem predicts the change in the number of votes before and after a speaker speaks. Our M2P2 framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem.
  arXiv  Detail & Related papers  (2020-06-03T18:47:24Z)
• Out of the Echo Chamber: Detecting Countering Debate Speeches [18.321466611103684]
  We study the problem in the context of debate speeches. We aim to identify, from among a set of speeches on the same topic and with an opposing stance, the ones that directly counter it. We explore several algorithms addressing this task, and while some are successful, all fall short of expert human performance.
  arXiv  Detail & Related papers  (2020-05-03T18:02:10Z)
• Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
  We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
  arXiv  Detail & Related papers  (2020-04-20T20:00:27Z)

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.