725 research outputs found
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts
We introduce an adversarial method for producing high-recall explanations of
neural text classifier decisions. Building on an existing architecture for
extractive explanations via hard attention, we add an adversarial layer which
scans the residual of the attention for remaining predictive signal. Motivated
by the important domain of detecting personal attacks in social media comments,
we additionally demonstrate the importance of manually setting a semantically
appropriate `default' behavior for the model by explicitly manipulating its
bias term. We develop a validation set of human-annotated personal attacks to
evaluate the impact of these changes.Comment: Accepted to EMNLP 2018 Code and data available at
https://github.com/shcarton/rcn
The Internet and Democratic Debate
Presents findings from a survey conducted in June 2004. Looks at the role of the Internet in providing a wider awareness of political views during the 2004 campaign season
Lifetime Adherence to Physical Activity Recommendations and Fall Occurrence in Community-dwelling Older Adults: a Retrospective Cohort Study
Falling is a major health concern for community-dwelling older adults. Regular physical activity has been proposed to prevent falls. The aim of this study was to assess whether the achievement of the 2004 UK Department of Health physical activity recommendations over a lifetime had a protective effect against falling in older people. 313 community-dwelling older adults completed a questionnaire about lifetime physical activity and fall occurrence. There were significantly fewer falls in those who had led an active lifestyle compared to those who had not (Ï2Yates=4.568, p=0.033), with a lower relative risk of fall occurrence for the active respondents (RR=0.671) compared to the inactive (RR=1.210). Of those who were sufficiently active in their early adulthood, the decade where there was the biggest decrease in remaining active enough was in the 60s. It is concluded that an active lifestyle may have decreased the likelihood of having a fall in older ag
Zoning Speech on the Internet: A Legal and Technical Model
Speech, it is said, divides into three sorts - (1) speech that everyone has a right to (political speech, speech about public affairs); (2) speech that no one has a right to (obscene speech, child porn); and (3) speech that some have a right to but others do not (in the United States, Ginsberg speech, or speech that is harmful to minors, to which adults have a right but kids do not). Speech-protective regimes, on this view, are those where category (1) speech predominates; speech-repressive regimes are those where categories (2) and (3) prevail. This divide has meaning for speech and regulation within a single jurisdiction, but it makes less sense across jurisdictions. For when viewed across jurisdictions, most controversial speech falls into category (3) - speech that is permitted to some in some places, but not to others in other places. What constitutes political speech in the United States (Nazi speech) is banned in Germany; what constitutes obscene speech in Tennessee is permitted in Holland; what constitutes porn in Japan is child porn in the United States; what is harmful to minors in Bavaria is Disney in New York. Every jurisdiction controls access to some speech - what we call mandatory access controls - but what that speech is differs from jurisdiction to jurisdiction. This diversity creates a problem (for governments at least) when we consider speech within cyberspace. Within cyberspace, mandated access controls are extremely difficult. If access control requires knowing (a) the identities of the speaker and receiver, (b) the jurisdictions of the speaker and receiver, and (c) the content of the speech at issue, then as cyberspace was initially designed, none of these data are easily determined. As a result, real space laws do not readily translate into the context of cyberspace
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
How to Train Your YouTube Recommender to Avoid Unwanted Videos
YouTube provides features for users to indicate disinterest when presented
with unwanted recommendations, such as the "Not interested" and "Don't
recommend channel" buttons. These buttons are purported to allow the user to
correct "mistakes" made by the recommendation system. Yet, relatively little is
known about the empirical efficacy of these buttons. Neither is much known
about users' awareness of and confidence in them. To address these gaps, we
simulated YouTube users with sock puppet agents. Each agent first executed a
"stain phase", where it watched many videos of one assigned topic; it then
executed a "scrub phase", where it tried to remove recommendations of the
assigned topic. Each agent repeatedly applied a single scrubbing strategy,
either indicating disinterest in one of the videos visited in the stain phase
(disliking it or deleting it from the watch history), or indicating disinterest
in a video recommended on the homepage (clicking the "not interested" or "don't
recommend channel" button or opening the video and clicking the dislike
button). We found that the stain phase significantly increased the fraction of
the recommended videos dedicated to the assigned topic on the user's homepage.
For the scrub phase, using the "Not interested" button worked best,
significantly reducing such recommendations in all topics tested, on average
removing 88% of them. Neither the stain phase nor the scrub phase, however, had
much effect on videopage recommendations. We also ran a survey (N = 300) asking
adult YouTube users in the US whether they were aware of and used these buttons
before, as well as how effective they found these buttons to be. We found that
44% of participants were not aware that the "Not interested" button existed.
However, those who were aware of this button often used it to remove unwanted
recommendations (82.8%) and found it to be modestly effective (3.42 out of 5).Comment: Accepted into ICWSM 2024, the code is publicly available at
https://github.com/avliu-um/youtube-disinteres
When Do People Trust Their Social Groups?
Trust facilitates cooperation and supports positive outcomes in social
groups, including member satisfaction, information sharing, and task
performance. Extensive prior research has examined individuals' general
propensity to trust, as well as the factors that contribute to their trust in
specific groups. Here, we build on past work to present a comprehensive
framework for predicting trust in groups. By surveying 6,383 Facebook Groups
users about their trust attitudes and examining aggregated behavioral and
demographic data for these individuals, we show that (1) an individual's
propensity to trust is associated with how they trust their groups, (2)
smaller, closed, older, more exclusive, or more homogeneous groups are trusted
more, and (3) a group's overall friendship-network structure and an
individual's position within that structure can also predict trust. Last, we
demonstrate how group trust predicts outcomes at both individual and group
level such as the formation of new friendship ties.Comment: CHI 201
The Influence Limiter: Provably Manipulation-Resistant Recommender Systems (Appendix)
Appendix containing proofs omitted from
Resnick and Sami,"The Influence Limiter: Provably Manipulation-Resistant Recommender Systems", ACM Recommender Systems Conference 2007.http://deepblue.lib.umich.edu/bitstream/2027.42/55415/1/recsys-appendix.pd
Protocols for automated negotiations with buyer anonymity and seller reputations
In many Internet commerce applications buyers can easily achieve anonymity, limiting what a seller can learn about any buyer individually. However, because sellers need to keep a fixed web address, buyers can probe them repeatedly or pool their information about sellers with the information obtained by other buyers; hence, sellers' strategies become public knowledge. Under assumptions of buyer anonymity, publiclyâknown seller strategies, and no negotiation transaction costs for buyers, we find that takeâitâorâleaveâit offers will yield at least as much seller profit as any attempt at price discrimination could yield. As we relax those assumptions, however, we find that sellers, and in some cases buyers as well, may benefit from a more general bargaining protocol.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45434/1/11066_2004_Article_329139.pd
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