Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation

@article{Juneja2021AuditingEP,
  title={Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation},
  author={Prerna Juneja and Tanushree Mitra},
  journal={Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems},
  year={2021}
}
  • Prerna JunejaTanushree Mitra
  • Published 21 January 2021
  • Computer Science
  • Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
There is a growing concern that e-commerce platforms are amplifying vaccine-misinformation. To investigate, we conduct two-sets of algorithmic audits for vaccine misinformation on the search and recommendation algorithms of Amazon—world’s leading e-retailer. First, we systematically audit search-results belonging to vaccine-related search-queries without logging into the platform—unpersonalized audits. We find 10.47% of search-results promote misinformative health products. We also observe… 

Auditing Google's Search Headlines as a Potential Gateway to Misleading Content

Novel data related to the content of political headlines during the 2020 US election period is presented and it is revealed that videos (as compared to stories, search results, and advertisements) are the most problematic in terms of exposing users to delegitimizing headlines.

COVID-19 VACCINES: CHARACTERIZING MISINFORMATION CAMPAIGNS

Vaccine hesitancy and misinformation on social media has increased concerns about COVID-19 vaccine uptake required to achieve herd immunity and overcome the pandemic. However anti-science and

COVID-19 Vaccines: Characterizing Misinformation Campaigns and Vaccine Hesitancy on Twitter

For COVID-19 vaccines, misinformation and conspiracy campaigns and their characteristic behaviours are investigated, and whether coordinated efforts are used to promote misinformation in vaccine related discussions are identified, and accounts coordinately promoting a ‘Great Reset’ conspiracy group are found.

COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives

COVID-19 vaccine hesitancy has increased concerns about vaccine uptake required to overcome the pandemic and protect public health. A critical factor associated with anti-vaccine attitudes is the

Auditing Algorithms: Understanding Algorithmic Systems from the Outside In

The algorithm audit is a method of repeatedly querying an algorithm and observing its output in order to draw conclusions about the algorithm's opaque output, and one strategy that has proven effective is the algorithm audit.

FaCov: COVID-19 Viral News and Rumors Fact-Check Articles Dataset

A COVID-19 related dataset – FaCov – a compilation of fact-checking articles that examine and evaluate some of the most widely circulated rumors and claims concerning the coronavirus is presented.

Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles

An auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter bubble, but also what it takes to “burst the bubble”, i.e., revert the bubble enclosure is presented.

Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic

This work utilizes the pre-trained Transformer-based XLNet model to classify tweets as Misleading or Non-Misleading and validate against a random subset of results manually, to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones.

The Plebeian Algorithm: A Democratic Approach to Censorship and Moderation.

The novel Plebeian Algorithm is proposed, which utilizes sentiment analysis and post popularity as metrics to flag a post as misinformation and which guarantees a more democratic way of using social media that is beneficial for repairing social trust and encouraging the public's evidence-informed decision making.

An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes

The key finding is that bursting of a filter bubble is possible, albeit it manifests differently from topic to topic, and it is observed that filter bubbles do not truly appear in some situations.

References

SHOWING 1-10 OF 66 REFERENCES

Auditing Partisan Audience Bias within Google Search

A mixed-methods algorithm audit of partisan audience bias and personalization within Google Search found that results positioned toward the bottom of Google SERPs were more left-leaning than results positioned towards the top, and that the direction and magnitude of overall lean varied by search query, component type, and other factors.

Online Information of Vaccines: Information Quality, Not Only Privacy, Is an Ethical Responsibility of Search Engines

This study suggests that designing a search engine that is privacy savvy and avoids issues with filter bubbles that can result from user-tracking is necessary but insufficient; instead, mechanisms should be developed to test search engines from the perspective of information quality before they can be deemed trustworthy providers of public health information.

Auditing the Partisanship of Google Search Snippets

It is found that Google Search's snippets generally amplify partisanship, and that this effect is robust across different types of webpages, query topics, and partisan (left- and right-leaning) queries.

Fake Cures: User-centric Modeling of Health Misinformation in Social Media

This work examines the individuals on social media that are posting questionable health-related information, and in particular promoting cancer treatments which have been shown to be ineffective, providing a potential tool for public health officials to identify such individuals for preventive intervention.

Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube

YouTube still has a long way to go to mitigate misinformation on its platform and a filter bubble effect, both in the Top 5 and Up-Next recommendations for all topics, except vaccine controversies; for these topics, watching videos that promote misinformation leads to more misinformative video recommendations.

An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace

This study develops a methodology for detecting algorithmic pricing, and uses it empirically to analyze their prevalence and behavior on Amazon Marketplace, and uncovers the algorithmic Pricing strategies adopted by over 500 sellers.

Algorithms and Health Misinformation: A Case Study of Vaccine Books on Amazon

This study examines how vaccine-related books appear on Amazon, focusing on search and recommendation algorithms, and found that books sharing similar views of vaccines were recommended together such that when a user views a vaccine-hesitant book, many other vaccine- Hesitant books are further recommended for the user.

Algorithmic Detection and Analysis of Vaccine-Denialist Sentiment Clusters in Social Networks

A deep neural network is used to predict tweet vaccine sentiments, surpassing state-of-the-art performance and finding that the network of repeated mutual interactions of actors in the vaccine discourse is highly stratified, with an assortativity coefficient of .813.

How often people google for vaccination: Qualitative and quantitative insights from a systematic search of the web-based activities using Google Trends

How often people search the Internet for vaccination-related information is investigated, if this search is spontaneous or induced by media, and which kind of information is in particular searched, to monitor the interest for preventable infections and related vaccines.

Measuring Price Discrimination and Steering on E-commerce Web Sites

This paper develops a methodology for accurately measuring when price steering and discrimination occur and implements it for a variety of e-commerce web sites, and investigates the effect of user behaviors on personalization.
...