Can Celebrities Burst Your Bubble?
- URL: http://arxiv.org/abs/2003.06857v2
- Date: Tue, 17 Mar 2020 01:03:04 GMT
- Title: Can Celebrities Burst Your Bubble?
- Authors: Tu\u{g}rulcan Elmas, Kristina Hardi, Rebekah Overdorf, Karl Aberer
- Abstract summary: Using a state-of-the art model that quantifies the degree of polarization, this paper makes a first attempt to empirically answer the question: Can celebrities burst filter bubbles?
We use a case study to analyze how people react when celebrities are involved in a controversial topic and conclude with a list possible research directions.
- Score: 2.6919164079336992
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Polarization is a growing, global problem. As such, many social media based
solutions have been proposed in order to reduce it. In this study, we propose a
new solution that recommends topics to celebrities to encourage them to join a
polarized debate and increase exposure to contrarian content - bursting the
filter bubble. Using a state-of-the art model that quantifies the degree of
polarization, this paper makes a first attempt to empirically answer the
question: Can celebrities burst filter bubbles? We use a case study to analyze
how people react when celebrities are involved in a controversial topic and
conclude with a list possible research directions.
Related papers
- A Picture Is Worth a Graph: A Blueprint Debate Paradigm for Multimodal Reasoning [53.35861580821777]
The study addresses two key challenges: the trivialization of opinions resulting from excessive summarization and the diversion of focus caused by distractor concepts introduced from images.
To address the issue, we propose a deductive (top-down) debating approach called Blueprint Debate on Graphs (BDoG)
In BDoG, debates are confined to a blueprint graph to prevent opinion trivialization through world-level summarization. Moreover, by storing evidence in branches within the graph, BDoG mitigates distractions caused by frequent but irrelevant concepts.
arXiv Detail & Related papers (2024-03-22T06:03:07Z) - Uncovering the Deep Filter Bubble: Narrow Exposure in Short-Video
Recommendation [30.395376392259497]
Filter bubbles have been studied extensively within the context of online content platforms.
With the rise of short-video platforms, the filter bubble has been given extra attention.
arXiv Detail & Related papers (2024-03-07T14:14:40Z) - Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models [61.45529177682614]
We challenge the prevailing constrained evaluation paradigm for values and opinions in large language models.
We show that models give substantively different answers when not forced.
We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.
arXiv Detail & Related papers (2024-02-26T18:00:49Z) - "Here's Your Evidence": False Consensus in Public Twitter Discussions of COVID-19 Science [50.08057052734799]
We estimate scientific consensus based on samples of abstracts from preprint servers.
We find that anti-consensus posts and users, though overall less numerous than pro-consensus ones, are vastly over-represented on Twitter.
arXiv Detail & Related papers (2024-01-24T06:16:57Z) - Filter Bubbles in Recommender Systems: Fact or Fallacy -- A Systematic
Review [7.121051191777698]
A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials.
We conduct a systematic literature review on the topic of filter bubbles in recommender systems.
We propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue.
arXiv Detail & Related papers (2023-07-02T13:41:42Z) - AIM 2022 Challenge on Instagram Filter Removal: Methods and Results [66.98814754338841]
This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal.
The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content.
There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge.
arXiv Detail & Related papers (2022-10-17T12:21:59Z) - NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias [54.89737992911079]
We propose a new task, a neutral summary generation from multiple news headlines of the varying political spectrum.
One of the most interesting observations is that generation models can hallucinate not only factually inaccurate or unverifiable content, but also politically biased content.
arXiv Detail & Related papers (2022-04-11T07:06:01Z) - An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting
and Recent Behavior Changes [0.6094711396431726]
We present a study in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles.
Our key finding is that bursting of a filter bubble is possible, albeit it manifests differently from topic to topic.
Sadly, we did not find much improvements in misinformation occurrences, despite recent pledges by YouTube.
arXiv Detail & Related papers (2022-03-25T16:49:57Z) - 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) - Detecting Polarized Topics in COVID-19 News Using Partisanship-aware
Contextualized Topic Embeddings [3.9761027576939405]
Growing polarization of the news media has been blamed for fanning disagreement, controversy and even violence.
We propose Partisanship-aware Contextualized Topic Embeddings (PaCTE), a method to automatically detect polarized topics from partisan news sources.
arXiv Detail & Related papers (2021-04-15T23:05:52Z)
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.