Engagement, Content Quality and Ideology over Time on the Facebook URL Dataset
- URL: http://arxiv.org/abs/2409.13461v1
- Date: Fri, 20 Sep 2024 12:50:17 GMT
- Title: Engagement, Content Quality and Ideology over Time on the Facebook URL Dataset
- Authors: Emma Fraxanet, Fabrizio Germano, Andreas Kaltenbrunner, Vicenç Gómez,
- Abstract summary: This study examines user engagement metrics related to news URLs in the U.S. from January 2017 to December 2020.
By incorporating the ideological alignment and quality of news sources, along with users' political preferences, we construct weighted averages of ideology and quality of news consumption for liberal, conservative, and moderate audiences.
We identify two significant shifts in trends for both metrics, each coinciding with changes in user engagement.
- Score: 3.443622476405787
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Unpacking the relationship between the ideology of social media users and their online news consumption offers critical insight into the feedback loop between users' engagement behavior and the recommender systems' content provision. However, disentangling inherent user behavior from platform-induced influences poses significant challenges, particularly when working with datasets covering limited time periods. In this study, we conduct both aggregate and longitudinal analyses using the Facebook Privacy-Protected Full URLs Dataset, examining user engagement metrics related to news URLs in the U.S. from January 2017 to December 2020. By incorporating the ideological alignment and quality of news sources, along with users' political preferences, we construct weighted averages of ideology and quality of news consumption for liberal, conservative, and moderate audiences. This allows us to track the evolution of (i) the ideological gap between liberals and conservatives and (ii) the average quality of each group's news consumption. These metrics are linked to broader phenomena such as polarization and misinformation. We identify two significant shifts in trends for both metrics, each coinciding with changes in user engagement. Interestingly, during both inflection points, the ideological gap widens and news quality declines; however, engagement increases after the first one and decreases after the second. Finally, we contextualize these changes by discussing their potential relation to two major updates to Facebook's News Feed algorithm.
Related papers
- Incentivizing News Consumption on Social Media Platforms Using Large Language Models and Realistic Bot Accounts [4.06613683722116]
This project examines how to enhance users' exposure to and engagement with verified and ideologically balanced news on Twitter.
We created 28 bots that replied to users tweeting about sports, entertainment, or lifestyle with a contextual reply.
To test differential effects by gender of the bots, treated users were randomly assigned to receive responses by bots presented as female or male.
We find that the treated users followed more news accounts and the users in the female bot treatment were more likely to like news content than the control.
arXiv Detail & Related papers (2024-03-20T07:44:06Z) - Modeling Political Orientation of Social Media Posts: An Extended
Analysis [0.0]
Developing machine learning models to characterize political polarization on online social media presents significant challenges.
These challenges mainly stem from various factors such as the lack of annotated data, presence of noise in social media datasets, and the sheer volume of data.
We introduce two methods that leverage on news media bias and post content to label social media posts.
We demonstrate that current machine learning models can exhibit improved performance in predicting political orientation of social media posts.
arXiv Detail & Related papers (2023-11-21T03:34:20Z) - Detecting Political Opinions in Tweets through Bipartite Graph Analysis:
A Skip Aggregation Graph Convolution Approach [9.350629400940493]
We focus on the 2020 US presidential election and create a large-scale dataset from Twitter.
To detect political opinions in tweets, we build a user-tweet bipartite graph based on users' posting and retweeting behaviors.
We introduce a novel skip aggregation mechanism that makes tweet nodes aggregate information from second-order neighbors.
arXiv Detail & Related papers (2023-04-22T10:38:35Z) - 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) - Tweets2Stance: Users stance detection exploiting Zero-Shot Learning
Algorithms on Tweets [0.06372261626436675]
The aim of the study is to predict the stance of a Party p in regard to each statement s exploiting what the Twitter Party account wrote on Twitter.
Results obtained from multiple experiments show that Tweets2Stance can correctly predict the stance with a general minimum MAE of 1.13, which is a great achievement considering the task complexity.
arXiv Detail & Related papers (2022-04-22T14:00:11Z) - 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) - Content-based Analysis of the Cultural Differences between TikTok and
Douyin [95.32409577885645]
Short-form video social media shifts away from the traditional media paradigm by telling the audience a dynamic story to attract their attention.
In particular, different combinations of everyday objects can be employed to represent a unique scene that is both interesting and understandable.
Offered by the same company, TikTok and Douyin are popular examples of such new media that has become popular in recent years.
The hypothesis that they express cultural differences together with media fashion and social idiosyncrasy is the primary target of our research.
arXiv Detail & Related papers (2020-11-03T01:47:49Z) - Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
We argue that it is critical to understand what user attributes potentially cause users to share fake news.
In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities.
We propose a principled approach to alleviating selection bias in fake news dissemination.
arXiv Detail & Related papers (2020-10-20T19:37:04Z) - Political audience diversity and news reliability in algorithmic ranking [54.23273310155137]
We propose using the political diversity of a website's audience as a quality signal.
Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 U.S. citizens, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards.
arXiv Detail & Related papers (2020-07-16T02:13:55Z) - 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.