Stop Tracking Me Bro! Differential Tracking Of User Demographics On
Hyper-partisan Websites
- URL: http://arxiv.org/abs/2002.00934v2
- Date: Mon, 30 Mar 2020 19:07:44 GMT
- Title: Stop Tracking Me Bro! Differential Tracking Of User Demographics On
Hyper-partisan Websites
- Authors: Pushkal Agarwal, Sagar Joglekar, Panagiotis Papadopoulos, Nishanth
Sastry, Nicolas Kourtellis
- Abstract summary: We take a first step to shed light and measure potential differences in tracking imposed on users when visiting specific party-line's websites.
This methodology allows us to create user personas with specific attributes like gender and age and automate their browsing behavior.
We test 9 personas on 556 hyper-partisan websites and find that right-leaning websites tend to track users more intensely than left-leaning.
- Score: 5.690539268996364
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Websites with hyper-partisan, left or right-leaning focus offer content that
is typically biased towards the expectations of their target audience. Such
content often polarizes users, who are repeatedly primed to specific (extreme)
content, usually reflecting hard party lines on political and socio-economic
topics. Though this polarization has been extensively studied with respect to
content, it is still unknown how it associates with the online tracking
experienced by browsing users, especially when they exhibit certain demographic
characteristics. For example, it is unclear how such websites enable the
ad-ecosystem to track users based on their gender or age. In this paper, we
take a first step to shed light and measure such potential differences in
tracking imposed on users when visiting specific party-line's websites. For
this, we design and deploy a methodology to systematically probe such websites
and measure differences in user tracking. This methodology allows us to create
user personas with specific attributes like gender and age and automate their
browsing behavior in a consistent and repeatable manner. Thus, we
systematically study how personas are being tracked by these websites and their
third parties, especially if they exhibit particular demographic properties.
Overall, we test 9 personas on 556 hyper-partisan websites and find that
right-leaning websites tend to track users more intensely than left-leaning,
depending on user demographics, using both cookies and cookie synchronization
methods and leading to more costly delivered ads.
Related papers
- How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users [50.699390248359265]
Browser fingerprinting can be used to identify and track users across the Web, even without cookies.
This technique and resulting privacy risks have been studied for over a decade.
We provide a first-of-its-kind dataset to enable further research.
arXiv Detail & Related papers (2024-10-09T14:51:58Z) - Filter bubbles and affective polarization in user-personalized large
language model outputs [0.15540058359482856]
Large language models (LLMs) have led to a push for increased personalization of model outputs to individual users.
We explore how prompting a leading large language model, ChatGPT-3.5, with a user's political affiliation prior to asking factual questions leads to differing results.
arXiv Detail & Related papers (2023-10-31T18:19:28Z) - User Attitudes to Content Moderation in Web Search [49.1574468325115]
We examine the levels of support for different moderation practices applied to potentially misleading and/or potentially offensive content in web search.
We find that the most supported practice is informing users about potentially misleading or offensive content, and the least supported one is the complete removal of search results.
More conservative users and users with lower levels of trust in web search results are more likely to be against content moderation in web search.
arXiv Detail & Related papers (2023-10-05T10:57:15Z) - Dynamics of Ideological Biases of Social Media Users [0.0]
We show that the evolution of online platform-wide opinion groups is driven by the desire to hold popular opinions.
We focus on two social media: Twitter and Parler, on which we tracked the political biases of their users.
arXiv Detail & Related papers (2023-09-27T19:39:07Z) - Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
We design an intelligent approach to online privacy protection that leverages supervised learning.
By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
arXiv Detail & Related papers (2023-04-06T05:20:16Z) - 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) - Estimating Topic Exposure for Under-Represented Users on Social Media [25.963970325207892]
This work focuses on highlighting the contributions of the engagers in the observed data.
The first step in behavioral analysis of these users is to find the topics they are exposed to but did not engage with.
We propose a novel framework that aids in identifying these users and estimates their topic exposure.
arXiv Detail & Related papers (2022-08-07T19:37:41Z) - 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) - Political Polarization in Online News Consumption [14.276551496332154]
Political polarization appears to be on the rise, as measured by voting behavior.
Research over the years has focused on the role of the Web as a driver of polarization.
We show that online news consumption follows a polarized pattern, where users' visits to news sources aligned with their own political leaning are substantially longer than their visits to other news sources.
arXiv Detail & Related papers (2021-04-09T22:35:46Z) - Differential Tracking Across Topical Webpages of Indian News Media [3.721918008485747]
We propose a novel method for automatic extraction and categorization of Indian news topical subpages based on the details in their URLs.
We find differential user tracking among subpages, and between subpages and homepages.
embedded third-parties tend to track specific subpages simultaneously, revealing possible user profiling in action.
arXiv Detail & Related papers (2021-03-07T20:20:47Z) - Right and left, partisanship predicts (asymmetric) vulnerability to
misinformation [71.46564239895892]
We analyze the relationship between partisanship, echo chambers, and vulnerability to online misinformation by studying news sharing behavior on Twitter.
We find that vulnerability to misinformation is most strongly influenced by partisanship for both left- and right-leaning users.
arXiv Detail & Related papers (2020-10-04T01:36:14Z)
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.