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Timothy Havens

dblp:119/8009 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
personalized search
0.412019
Measuring Political Personalization of Google News Search · WWW 2019
Information retrieval › web search
search personalization
0.412019
Measuring Political Personalization of Google News Search · WWW 2019

Methods — techniques the papers use, named apart from their topics

sock-puppet auditing · 0.4
YearPublicationVenuePosition
2019 Measuring Political Personalization of Google News Search
abstract
There is a growing concern about the extent to which algorithmic personalization limits people's exposure to diverse viewpoints, thereby creating “filter bubbles” or “echo chambers.” Prior research on web search personalization has mainly reported location-based personalization of search results. In this paper, we investigate whether web search results are personalized based on a user's browsing history, which can be inferred by search engines via third-party tracking. Specifically, we develop a “sock puppet” auditing system in which a pair of fresh browser profiles, first, visits web pages that reflect divergent political discourses and, second, executes identical politically oriented Google News searches. Comparing the search results returned by Google News for distinctly trained browser profiles, we observe statistically significant personalization that tends to reinforce the presumed partisanship.
Huyen T. Le, Raven Maragh, Brian Ekdale, Andrew High, Timothy Havens, Zubair Shafiq
WWW5