Real or not? Identifying untrustworthy news websites using third-party partnerships

Untrustworthy content such as fake news and clickbait have become a pervasive problem on the Internet, causing significant socio-political problems around the world. Identifying untrustworthy content is a crucial step in countering them. The current best-practices for identification involve content analysis and arduous fact-checking of the content. To complement content analysis, we propose examining websites? third-parties to identify their trustworthiness. Websites utilize third-parties, also known as their digital supply chains, to create and present content and help the website function. Third-parties are an important indication of a website?s business model. Similar websites exhibit similarities in the third-parties they use. Using this perspective, we use machine learning and heuristic methods to discern similarities and dissimilarities in third-party usage, which we use to predict trustworthiness of websites. We demonstrate the effectiveness and robustness of our approach in predicting trustworthiness of websites from a database of News, Fake News, and Clickbait websites. Our approach can be easily and cost-effectively implemented to reinforce current identification methods

Our private data and the market for third-party providers of functionality to websites

Your personal information is out there. You did not give it out, so how did it get there? Internet websites provide visitors with different levels of interaction, ranging from delivering basic information to providing sophisticated features and tools such as profile management, interactive visual communication, and of course, advertising. Like many traditional businesses, websites turn to third-party outsourcing to offer these features and tools. Such services include functionality (password and account control, social media integration, video hosting, chat and forum services, payment services, etc.), performance (backup service, security and firewalls, responsiveness tools, etc.) and targeting/advertising (advertising, lead generation, analytics, etc.)

Dark clouds and silver linings : impact of COVID-19 on internet users’ privacy

Objectives: To examine the impact of COVID-19 pandemic on the extent of potential violations of Internet users’ privacy. Materials and Methos: We conducted a longitudinal study of the data sharing practices of the top 1,000 websites in the US between April 9th and August 27th, 2020. We fitted a conditional latent growth curve model on the data to examine the longitudinal trajectory of the third-party data sharing over the 21 weeks period of the study and examine how website characteristics affect this trajectory. Results: As the weekly number of COVID-19 deaths increased by 1,000, the average number of third parties increased by 0.26 [95%CI, 0.15 to 0.37] P<.001 units in the next week. This effect was more pronounced for websites with higher traffic as they increased their third parties by an additional 0.41 [95% CI, 0.18 to 0.64]; P<.001 units per week. However, privacy respecting websites that experienced a surge in traffic reduced their third parties by 1.01 [95% CI, -2.01 to 0]; P = 0.05 units per week in response to every 1,000 COVID-19 deaths in the preceding week. Discussion: While in general websites shared their users’ data with more third parties as COVID-19 progressed in the US, websites’ expected traffic and respect for users’ privacy significantly affect such trajectory. Conclusions: Attention should also be paid to the impact of the pandemic on elevating online privacy threats, and the variation in third-party tracking among different types of websites

How Much to Share with Third Parties? User Privacy Concerns and Website Dilemmas

Publishers websites are increasingly presenting content and services that are not created and managed by the website administrators themselves, but are provided by other third parties. While third party content and services provide value and utility to website users, this comes at the cost of user information being shared with the third party. Privacy concerns surrounding information leakage have been growing rapidly. With increasing concerns regarding online privacy and information disclosure, it is important to understand the factors that affect the level of sharing between publisher websites and third parties. In this study, we propose a two-sided economic model that captures the interaction between the users, publisher websites, and third parties. Specifically, we focus on the effect of privacy concerns on the sharing behavior of the publisher website and the impact of users’ privacy concerns on third party market concentration. We then analyze welfare aspects to provide insights on the impacts of industry regulations and policy on users, publisher websites, and third parties. We partially validate the model using an exploratory empirical analysis of publisher website third party sharing behavior and the structure of the industry. To the best of our knowledge, this study is among the first to analyze publisher website decision making in sharing user information with third parties

Economics of Data Protection Policies

The widespread abuse of user privacy on websites has prompted user advocacy groups to call on governments to intervene and protect consumer rights. In this paper, we present several data protection policies including no third-party sharing and GDPR-type consent-based that policy-makers and governments can utilize to improve user surplus and/or social welfare. We use a stylized analytical model to examine the impact of privacy concerns and competition on the decisions of various entities including websites, users, and third-parties under the policies. We find that consent-based policies may have the opposite and unintended effect of increasing the number of third-parties, and thus, the sharing of user information. Whereas in the absence of market entry and exit, policies may benefit social welfare, considering the impact of such policies on entry and exit of websites is shown to be an important factor. We also provide an empirical investigation of our findings about the impact of competition and consent-based policies on third-parties