6,551 research outputs found
The Social World of Content Abusers in Community Question Answering
Community-based question answering platforms can be rich sources of
information on a variety of specialized topics, from finance to cooking. The
usefulness of such platforms depends heavily on user contributions (questions
and answers), but also on respecting the community rules. As a crowd-sourced
service, such platforms rely on their users for monitoring and flagging content
that violates community rules.
Common wisdom is to eliminate the users who receive many flags. Our analysis
of a year of traces from a mature Q&A site shows that the number of flags does
not tell the full story: on one hand, users with many flags may still
contribute positively to the community. On the other hand, users who never get
flagged are found to violate community rules and get their accounts suspended.
This analysis, however, also shows that abusive users are betrayed by their
network properties: we find strong evidence of homophilous behavior and use
this finding to detect abusive users who go under the community radar. Based on
our empirical observations, we build a classifier that is able to detect
abusive users with an accuracy as high as 83%.Comment: Published in the proceedings of the 24th International World Wide Web
Conference (WWW 2015
Preference-Informed Fairness
We study notions of fairness in decision-making systems when individuals have
diverse preferences over the possible outcomes of the decisions. Our starting
point is the seminal work of Dwork et al. which introduced a notion of
individual fairness (IF): given a task-specific similarity metric, every pair
of individuals who are similarly qualified according to the metric should
receive similar outcomes. We show that when individuals have diverse
preferences over outcomes, requiring IF may unintentionally lead to
less-preferred outcomes for the very individuals that IF aims to protect. A
natural alternative to IF is the classic notion of fair division, envy-freeness
(EF): no individual should prefer another individual's outcome over their own.
Although EF allows for solutions where all individuals receive a
highly-preferred outcome, EF may also be overly-restrictive. For instance, if
many individuals agree on the best outcome, then if any individual receives
this outcome, they all must receive it, regardless of each individual's
underlying qualifications for the outcome.
We introduce and study a new notion of preference-informed individual
fairness (PIIF) that is a relaxation of both individual fairness and
envy-freeness. At a high-level, PIIF requires that outcomes satisfy IF-style
constraints, but allows for deviations provided they are in line with
individuals' preferences. We show that PIIF can permit outcomes that are more
favorable to individuals than any IF solution, while providing considerably
more flexibility to the decision-maker than EF. In addition, we show how to
efficiently optimize any convex objective over the outcomes subject to PIIF for
a rich class of individual preferences. Finally, we demonstrate the broad
applicability of the PIIF framework by extending our definitions and algorithms
to the multiple-task targeted advertising setting introduced by Dwork and
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