32,111 research outputs found
Quantifying Biases in Online Information Exposure
Our consumption of online information is mediated by filtering, ranking, and
recommendation algorithms that introduce unintentional biases as they attempt
to deliver relevant and engaging content. It has been suggested that our
reliance on online technologies such as search engines and social media may
limit exposure to diverse points of view and make us vulnerable to manipulation
by disinformation. In this paper, we mine a massive dataset of Web traffic to
quantify two kinds of bias: (i) homogeneity bias, which is the tendency to
consume content from a narrow set of information sources, and (ii) popularity
bias, which is the selective exposure to content from top sites. Our analysis
reveals different bias levels across several widely used Web platforms. Search
exposes users to a diverse set of sources, while social media traffic tends to
exhibit high popularity and homogeneity bias. When we focus our analysis on
traffic to news sites, we find higher levels of popularity bias, with smaller
differences across applications. Overall, our results quantify the extent to
which our choices of online systems confine us inside "social bubbles."Comment: 25 pages, 10 figures, to appear in the Journal of the Association for
Information Science and Technology (JASIST
Measuring Online Social Bubbles
Social media have quickly become a prevalent channel to access information,
spread ideas, and influence opinions. However, it has been suggested that
social and algorithmic filtering may cause exposure to less diverse points of
view, and even foster polarization and misinformation. Here we explore and
validate this hypothesis quantitatively for the first time, at the collective
and individual levels, by mining three massive datasets of web traffic, search
logs, and Twitter posts. Our analysis shows that collectively, people access
information from a significantly narrower spectrum of sources through social
media and email, compared to search. The significance of this finding for
individual exposure is revealed by investigating the relationship between the
diversity of information sources experienced by users at the collective and
individual level. There is a strong correlation between collective and
individual diversity, supporting the notion that when we use social media we
find ourselves inside "social bubbles". Our results could lead to a deeper
understanding of how technology biases our exposure to new information
VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting
Information spread in social media depends on a number of factors, including
how the site displays information, how users navigate it to find items of
interest, users' tastes, and the `virality' of information, i.e., its
propensity to be adopted, or retweeted, upon exposure. Probabilistic models can
learn users' tastes from the history of their item adoptions and recommend new
items to users. However, current models ignore cognitive biases that are known
to affect behavior. Specifically, people pay more attention to items at the top
of a list than those in lower positions. As a consequence, items near the top
of a user's social media stream have higher visibility, and are more likely to
be seen and adopted, than those appearing below. Another bias is due to the
item's fitness: some items have a high propensity to spread upon exposure
regardless of the interests of adopting users. We propose a probabilistic model
that incorporates human cognitive biases and personal relevance in the
generative model of information spread. We use the model to predict how
messages containing URLs spread on Twitter. Our work shows that models of user
behavior that account for cognitive factors can better describe and predict
user behavior in social media.Comment: SBP 201
Search Bias Quantification: Investigating Political Bias in Social Media and Web Search
Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe
On Measuring Bias in Online Information
Bias in online information has recently become a pressing issue, with search
engines, social networks and recommendation services being accused of
exhibiting some form of bias. In this vision paper, we make the case for a
systematic approach towards measuring bias. To this end, we discuss formal
measures for quantifying the various types of bias, we outline the system
components necessary for realizing them, and we highlight the related research
challenges and open problems.Comment: 6 pages, 1 figur
Cognitive coherence in the evluation of a novel single item
Article published in Judgement and Decision-Makin
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