Dynamics of Cross-Platform Attention to Retracted Papers
- URL: http://arxiv.org/abs/2110.07798v2
- Date: Wed, 15 Jun 2022 22:02:57 GMT
- Title: Dynamics of Cross-Platform Attention to Retracted Papers
- Authors: Hao Peng, Daniel M. Romero, Em\H{o}ke-\'Agnes Horv\'at
- Abstract summary: Retracted papers circulate widely on social media, digital news and other websites before their official retraction.
We quantify the amount and type of attention 3,851 retracted papers received over time in different online platforms.
- Score: 25.179837269945015
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Retracted papers often circulate widely on social media, digital news and
other websites before their official retraction. The spread of potentially
inaccurate or misleading results from retracted papers can harm the scientific
community and the public. Here we quantify the amount and type of attention
3,851 retracted papers received over time in different online platforms.
Comparing to a set of non-retracted control papers from the same journals, with
similar publication year, number of co-authors and author impact, we show that
retracted papers receive more attention after publication not only on social
media, but also on heavily curated platforms, such as news outlets and
knowledge repositories, amplifying the negative impact on the public. At the
same time, we find that posts on Twitter tend to express more criticism about
retracted than about control papers, suggesting that criticism-expressing
tweets could contain factual information about problematic papers. Most
importantly, around the time they are retracted, papers generate discussions
that are primarily about the retraction incident rather than about research
findings, showing that by this point papers have exhausted attention to their
results and highlighting the limited effect of retractions. Our findings reveal
the extent to which retracted papers are discussed on different online
platforms and identify at scale audience criticism towards them. In this
context, we show that retraction is not an effective tool to reduce online
attention to problematic papers.
Related papers
- Who Checks the Checkers? Exploring Source Credibility in Twitter's Community Notes [0.03511246202322249]
The proliferation of misinformation on social media platforms has become a significant concern.
This study focuses on the specific feature of Twitter Community Notes, despite its potential role in crowd-sourced fact-checking.
We find that the majority of cited sources are news outlets that are left-leaning and are of high factuality, pointing to a potential bias in the platform's community fact-checking.
arXiv Detail & Related papers (2024-06-18T09:47:58Z) - Position: AI/ML Influencers Have a Place in the Academic Process [82.2069685579588]
We investigate the role of social media influencers in enhancing the visibility of machine learning research.
We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023.
Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers.
arXiv Detail & Related papers (2024-01-24T20:05:49Z) - CausalCite: A Causal Formulation of Paper Citations [80.82622421055734]
CausalCite is a new way to measure the significance of a paper by assessing the causal impact of the paper on its follow-up papers.
It is based on a novel causal inference method, TextMatch, which adapts the traditional matching framework to high-dimensional text embeddings.
We demonstrate the effectiveness of CausalCite on various criteria, such as high correlation with paper impact as reported by scientific experts.
arXiv Detail & Related papers (2023-11-05T23:09:39Z) - Estimating the Causal Effect of Early ArXiving on Paper Acceptance [56.538813945721685]
We estimate the effect of arXiving a paper before the reviewing period (early arXiving) on its acceptance to the conference.
Our results suggest that early arXiving may have a small effect on a paper's chances of acceptance.
arXiv Detail & Related papers (2023-06-24T07:45:38Z) - Characterizing the effect of retractions on scientific careers [1.6758573326215693]
Retracting academic papers is a fundamental tool of quality control when the validity of papers or the integrity of authors is questioned.
Previous studies have highlighted the adverse effects of retractions on citation counts and coauthors' citations.
Our investigation focuses on the likelihood of authors exiting scientific publishing following a retraction, and the evolution of collaboration networks.
arXiv Detail & Related papers (2023-06-11T15:52:39Z) - Forgotten Knowledge: Examining the Citational Amnesia in NLP [63.13508571014673]
We show how far back in time do we tend to go to cite papers? How has that changed over time, and what factors correlate with this citational attention/amnesia?
We show that around 62% of cited papers are from the immediate five years prior to publication, whereas only about 17% are more than ten years old.
We show that the median age and age diversity of cited papers were steadily increasing from 1990 to 2014, but since then, the trend has reversed, and current NLP papers have an all-time low temporal citation diversity.
arXiv Detail & Related papers (2023-05-29T18:30:34Z) - 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) - Information Retention in the Multi-platform Sharing of Science [1.4626565477022566]
We examine information retention in the over 4 million online posts referencing 9,765 of the most-mentioned scientific articles.
We find a strong tendency towards low levels of information retention, following a distinct trajectory of loss.
sequences involving more platforms tend to be associated with higher information retention.
arXiv Detail & Related papers (2022-07-27T22:28:48Z) - Newsalyze: Effective Communication of Person-Targeting Biases in News
Articles [8.586057042714698]
We present a system for bias identification, which combines state-of-the-art methods from natural language understanding.
Second, we devise bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers.
Third, our main contribution is a large-scale user study that measures bias-awareness in a setting that approximates daily news consumption.
arXiv Detail & Related papers (2021-10-18T10:23:19Z) - Some Ethical Issues in the Review Process of Machine Learning
Conferences [0.38073142980733]
Recent successes in the Machine Learning community have led to a steep increase in the number of papers submitted to conferences.
This increase made more prominent some of the issues that affect the current review process used by these conferences.
We study the problem of reviewers' recruitment, infringements of the double-blind process, fraudulent behaviors, biases in numerical ratings, and the appendix phenomenon.
arXiv Detail & Related papers (2021-06-01T21:22:41Z) - Misinfo Belief Frames: A Case Study on Covid & Climate News [49.979419711713795]
We propose a formalism for understanding how readers perceive the reliability of news and the impact of misinformation.
We introduce the Misinfo Belief Frames (MBF) corpus, a dataset of 66k inferences over 23.5k headlines.
Our results using large-scale language modeling to predict misinformation frames show that machine-generated inferences can influence readers' trust in news headlines.
arXiv Detail & Related papers (2021-04-18T09:50:11Z)
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.