Reliability of Content and Echo Chambers on YouTube during the COVID-19
Debate
- URL: http://arxiv.org/abs/2106.08684v2
- Date: Tue, 29 Nov 2022 21:10:12 GMT
- Title: Reliability of Content and Echo Chambers on YouTube during the COVID-19
Debate
- Authors: Niccol\`o Di Marco, Matteo Cinelli, Walter Quattrociocchi
- Abstract summary: This paper aims to investigate information diffusion during the COVID-19 pandemic by evaluating news consumption on YouTube.
We analyse more than 2 million users' engagement with 13,000 videos released by 68 YouTube channels labelled with a political bias and fact-checking index.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The spread of inaccurate and misleading information may alter behaviours and
complicate crisis management, especially during an emergency like the COVID-19
pandemic. This paper aims to investigate information diffusion during the
COVID-19 pandemic by evaluating news consumption on YouTube. First, we analyse
more than 2 million users' engagement with 13,000 videos released by 68 YouTube
channels, labelled with a political bias and fact-checking index. Then, we
study the relationship between each user\~Os political preference and their
consumption of questionable (i.e., poorly fact-checked) and reliable
information. Our results, quantified using measures from information theory,
provide evidence for the existence of echo chambers across two dimensions
represented by political bias and the trustworthiness of information channels.
We observe that the echo chamber structure cannot be reproduced after properly
randomising the users' interaction patterns. Moreover, we observe a relation
between the political bias of users and their tendency to consume highly
questionable news.
Related papers
- MisinfoEval: Generative AI in the Era of "Alternative Facts" [50.069577397751175]
We introduce a framework for generating and evaluating large language model (LLM) based misinformation interventions.
We present (1) an experiment with a simulated social media environment to measure effectiveness of misinformation interventions, and (2) a second experiment with personalized explanations tailored to the demographics and beliefs of users.
Our findings confirm that LLM-based interventions are highly effective at correcting user behavior.
arXiv Detail & Related papers (2024-10-13T18:16:50Z) - Measuring COVID-19 Related Media Consumption on Twitter [2.746705315038595]
Social media platforms have provided essential updates regarding the pandemic.
Online communications with media outlets remain unexplored on an international scale.
This thesis presents the first-of-its-kind study on media consumption on COVID-19 across countries.
arXiv Detail & Related papers (2023-09-16T04:01:45Z) - 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) - The Morbid Realities of Social Media: An Investigation into the
Misinformation Shared by the Deceased Victims of COVID-19 [0.4995343972237368]
We study a unique dataset of Facebook posts by users who shared and believed in Covid-19 misinformation before succumbing to Covid-19 often resulting in death.
Our analysis reveals the overwhelming politicization of Covid-19 through the prevalence of anti-government themes.
Results from this study bring insights into the responsibility of political elites in shaping public discourse and the platform's role in dampening the reach of harmful misinformation.
arXiv Detail & Related papers (2022-09-20T19:47:27Z) - The Role of Bias in News Recommendation in the Perception of the
Covid-19 Pandemic [1.0618008515822484]
News recommender systems (NRs) have been shown to shape public discourse and to enforce behaviors that have a detrimental effect on democracies.
We performed sequence prediction by using the BERT4Rec algorithm to investigate the interplay of news of coverage and user behavior.
arXiv Detail & Related papers (2022-09-15T21:10:11Z) - "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the
Viral Spread of COVID-19 Misinformation [60.268682953952506]
We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models.
We provide a strategy to combat social media posts that are likely to cause widespread harm.
arXiv Detail & Related papers (2022-06-12T19:41:01Z) - Characterizing User Susceptibility to COVID-19 Misinformation on Twitter [40.0762273487125]
This study attempts to answer it who constitutes the population vulnerable to the online misinformation in the pandemic.
We distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation.
We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation.
arXiv Detail & Related papers (2021-09-20T13:31:15Z) - 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) - 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) - 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.