32 research outputs found
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On Birthing Dancing Stars: The Need for Bounded Chaos in Information Interaction
While computers causing chaos is acommon social trope, nearly the entirety of the history of computing is dedicated to generating order. Typical interactive information retrieval tasks ask computers to support the traversal and exploration of large, complex information spaces. The implicit assumption is that they are to support users in simplifying the complexity (i.e. in creating order from chaos). But for some types of task, particularly those that involve the creative application or synthesis of knowledge or the creation of new knowledge, this assumption may be incorrect. It is increasingly evident that perfect order—and the systems we create with it—support highly-structured information tasks well, but provide poor support for less-structured tasks.We need digital information environments that help create a little more chaos from order to spark creative thinking and knowledge creation. This paper argues for the need for information systems that offerwhat we term ‘bounded chaos’, and offers research directions that may support the creation of such interface
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We are the Change that we Seek: Information Interactions During a Change of Viewpoint
There has been considerable hype about filter bubbles and echo chambers influencing the views of information consumers. The fear is that these technologies are undermining democracy by swaying opinion and creating an uninformed, polarised populace. The literature in this space is mostly techno-centric, addressing the impact of technology. In contrast, our work is the first research in the information interaction field to examine changing viewpoints from a human-centric perspective. It provides a new understanding of view change and how we might support informed, autonomous view change behaviour. We interviewed 18 participants about a self-identified change of view, and the information touchpoints they engaged with along the way. In this paper we present the information types and sources that informed changes of viewpoint, and the ways in which our participants interacted with that information. We describe our findings in the context of the techno-centric literature and suggest principles for designing digital information environments that support user autonomy and reflection in viewpoint formation
Algorithms that "Don't See Color": Comparing Biases in Lookalike and Special Ad Audiences
Today, algorithmic models are shaping important decisions in domains such as
credit, employment, or criminal justice. At the same time, these algorithms
have been shown to have discriminatory effects. Some organizations have tried
to mitigate these effects by removing demographic features from an algorithm's
inputs. If an algorithm is not provided with a feature, one might think, then
its outputs should not discriminate with respect to that feature. This may not
be true, however, when there are other correlated features. In this paper, we
explore the limits of this approach using a unique opportunity created by a
lawsuit settlement concerning discrimination on Facebook's advertising
platform. Facebook agreed to modify its Lookalike Audiences tool - which
creates target sets of users for ads by identifying users who share "common
qualities" with users in a source audience provided by an advertiser - by
removing certain demographic features as inputs to its algorithm. The modified
tool, Special Ad Audiences, is intended to reduce the potential for
discrimination in target audiences. We create a series of Lookalike and Special
Ad audiences based on biased source audiences - i.e., source audiences that
have known skew along the lines of gender, age, race, and political leanings.
We show that the resulting Lookalike and Special Ad audiences both reflect
these biases, despite the fact that Special Ad Audiences algorithm is not
provided with the features along which our source audiences are skewed. More
broadly, we provide experimental proof that removing demographic features from
a real-world algorithmic system's inputs can fail to prevent biased outputs.
Organizations using algorithms to mediate access to life opportunities should
consider other approaches to mitigating discriminatory effects
"Foreign beauties want to meet you": The sexualization of women in Google's organic and sponsored text search results
Search engines serve as information gatekeepers on a multitude of topics dealing with different aspects of society. However, the ways search engines filter and rank information are prone to biases related to gender, ethnicity, and race. In this article, we conduct a systematic algorithm audit to examine how one specific form of bias, namely, sexualization, is manifested in Google’s text search results about different national and gender groups. We find evidence of the sexualization of women, particularly those from the Global South and East, in search outputs in both organic and sponsored search results. Our findings contribute to research on the sexualization of people in different forms of media, bias in web search, and algorithm auditing as well as have important implications for the ongoing debates about the responsibility of transnational tech companies for preventing systems they design from amplifying discrimination
Novelty in news search: a longitudinal study of the 2020 US elections
The 2020 US elections news coverage was extensive, with new pieces of
information generated rapidly. This evolving scenario presented an opportunity
to study the performance of search engines in a context in which they had to
quickly process information as it was published. We analyze novelty, a
measurement of new items that emerge in the top news search results, to compare
the coverage and visibility of different topics. We conduct a longitudinal
study of news results of five search engines collected in short-bursts (every
21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on
election day and lasting until one day after the announcement of Biden as the
winner. We find more new items emerging for election related queries ("joe
biden", "donald trump" and "us elections") compared to topical (e.g.,
"coronavirus") or stable (e.g., "holocaust") queries. We demonstrate
differences across search engines and regions over time, and we highlight
imbalances between candidate queries. When it comes to news search, search
engines are responsible for such imbalances, either due to their algorithms or
the set of news sources they rely on. We argue that such imbalances affect the
visibility of political candidates in news searches during electoral periods
Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives
We present the first large-scale measurement study of cross-partisan
discussions between liberals and conservatives on YouTube, based on a dataset
of 274,241 political videos from 973 channels of US partisan media and 134M
comments from 9.3M users over eight months in 2020. Contrary to a simple
narrative of echo chambers, we find a surprising amount of cross-talk: most
users with at least 10 comments posted at least once on both left-leaning and
right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based
on the user leaning predicted by a hierarchical attention model, we find that
conservatives were much more likely to comment on left-leaning videos than
liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm
made cross-partisan comments modestly less visible; for example, comments from
conservatives made up 26.3% of all comments on left-leaning videos but just
over 20% of the comments were in the top 20 positions. Lastly, using
Perspective API's toxicity score as a measure of quality, we find that
conservatives were not significantly more toxic than liberals when users
directly commented on the content of videos. However, when users replied to
comments from other users, we find that cross-partisan replies were more toxic
than co-partisan replies on both left-leaning and right-leaning videos, with
cross-partisan replies being especially toxic on the replier's home turf.Comment: Accepted into ICWSM 2021, the code and datasets are publicly
available at https://github.com/avalanchesiqi/youtube-crosstal