How Inclusive Are Wikipedia's Hyperlinks in Articles Covering Polarizing Topics?

• URL: http://arxiv.org/abs/2007.08197v5
• Date: Fri, 1 Apr 2022 01:15:44 GMT
• Title: How Inclusive Are Wikipedia's Hyperlinks in Articles Covering Polarizing Topics?
• Authors: Cristina Menghini and Aris Anagnostopoulos and Eli Upfal
• Abstract summary: We focus on the influence of the interconnect topology between articles describing complementary aspects of polarizing topics. We introduce a novel measure of exposure to diverse information to quantify users' exposure to different aspects of a topic. We identify cases in which the network topology significantly limits the exposure of users to diverse information on the topic, encouraging users to remain in a knowledge bubble.
• Score: 8.035521056416242
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wikipedia relies on an extensive review process to verify that the content of each individual page is unbiased and presents a neutral point of view. Less attention has been paid to possible biases in the hyperlink structure of Wikipedia, which has a significant influence on the user's exploration process when visiting more than one page. The evaluation of hyperlink bias is challenging because it depends on the global view rather than the text of individual pages. In this paper, we focus on the influence of the interconnect topology between articles describing complementary aspects of polarizing topics. We introduce a novel measure of exposure to diverse information to quantify users' exposure to different aspects of a topic throughout an entire surfing session, rather than just one click ahead. We apply this measure to six polarizing topics (e.g., gun control and gun right), and we identify cases in which the network topology significantly limits the exposure of users to diverse information on the topic, encouraging users to remain in a knowledge bubble. Our findings demonstrate the importance of evaluating Wikipedia's network structure in addition to the extensive review of individual articles.

Related papers

• Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models [11.597314728459573]
  We study how to apply large language models to write grounded and organized long-form articles from scratch, with comparable breadth and depth to Wikipedia pages. We propose STORM, a writing system for the Synthesis of Topic Outlines through Retrieval and Multi-perspective Question Asking.
  arXiv  Detail & Related papers  (2024-02-22T01:20:17Z)
• Orphan Articles: The Dark Matter of Wikipedia [13.290424502717734]
  We conduct the first systematic study of orphan articles, which are articles without any incoming links from other Wikipedia articles. We find that a surprisingly large extent of content, roughly 15% (8.8M) of all articles, is de facto invisible to readers navigating Wikipedia. We also provide causal evidence through a quasi-experiment that adding new incoming links to orphans (de-orphanization) leads to a statistically significant increase of their visibility.
  arXiv  Detail & Related papers  (2023-06-06T18:04:33Z)
• Curious Rhythms: Temporal Regularities of Wikipedia Consumption [15.686850035802667]
  We show that even after removing the global pattern of day-night alternation, the consumption habits of individual articles maintain strong diurnal regularities. We investigate topical and contextual correlates of Wikipedia articles' access rhythms, finding that article topic, reader country, and access device (mobile vs. desktop) are all important predictors of daily attention patterns.
  arXiv  Detail & Related papers  (2023-05-16T14:48:08Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• 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)
• 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)
• Predicting Links on Wikipedia with Anchor Text Information [0.571097144710995]
  We study the transductive and the inductive tasks of link prediction on several subsets of the English Wikipedia. We propose an appropriate evaluation sampling methodology and compare several algorithms.
  arXiv  Detail & Related papers  (2021-05-25T07:57:57Z)
• WikiAsp: A Dataset for Multi-domain Aspect-based Summarization [69.13865812754058]
  We propose WikiAsp, a large-scale dataset for multi-domain aspect-based summarization. Specifically, we build the dataset using Wikipedia articles from 20 different domains, using the section titles and boundaries of each article as a proxy for aspect annotation. Results highlight key challenges that existing summarization models face in this setting, such as proper pronoun handling of quoted sources and consistent explanation of time-sensitive events.
  arXiv  Detail & Related papers  (2020-11-16T10:02:52Z)
• Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages [60.00219873112454]
  We investigate the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.
  arXiv  Detail & Related papers  (2020-08-05T11:11:55Z)
• Information Consumption and Social Response in a Segregated Environment: the Case of Gab [74.5095691235917]
  This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic. We find that there are no strong statistical differences in the social response to questionable and reliable content. Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
  arXiv  Detail & Related papers  (2020-06-03T11:34:25Z)
• Quantifying Engagement with Citations on Wikipedia [13.703047949952852]
  One in 300 page views results in a reference click. Clicks occur more frequently on shorter pages and on pages of lower quality. Recent content, open access sources and references about life events are particularly popular.
  arXiv  Detail & Related papers  (2020-01-23T15:52:36Z)

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.