489 research outputs found
Hoodsquare: Modeling and Recommending Neighborhoods in Location-based Social Networks
Information garnered from activity on location-based social networks can be
harnessed to characterize urban spaces and organize them into neighborhoods. In
this work, we adopt a data-driven approach to the identification and modeling
of urban neighborhoods using location-based social networks. We represent
geographic points in the city using spatio-temporal information about
Foursquare user check-ins and semantic information about places, with the goal
of developing features to input into a novel neighborhood detection algorithm.
The algorithm first employs a similarity metric that assesses the homogeneity
of a geographic area, and then with a simple mechanism of geographic
navigation, it detects the boundaries of a city's neighborhoods. The models and
algorithms devised are subsequently integrated into a publicly available,
map-based tool named Hoodsquare that allows users to explore activities and
neighborhoods in cities around the world.
Finally, we evaluate Hoodsquare in the context of a recommendation
application where user profiles are matched to urban neighborhoods. By
comparing with a number of baselines, we demonstrate how Hoodsquare can be used
to accurately predict the home neighborhood of Twitter users. We also show that
we are able to suggest neighborhoods geographically constrained in size, a
desirable property in mobile recommendation scenarios for which geographical
precision is key.Comment: ASE/IEEE SocialCom 201
Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement
When groups of people are tasked with making a judgment, the issue of
uncertainty often arises. Existing methods to reduce uncertainty typically
focus on iteratively improving specificity in the overall task instruction.
However, uncertainty can arise from multiple sources, such as ambiguity of the
item being judged due to limited context, or disagreements among the
participants due to different perspectives and an under-specified task. A
one-size-fits-all intervention may be ineffective if it is not targeted to the
right source of uncertainty. In this paper we introduce a new workflow,
Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a
targeted manner. By utilizing measurements that separate different sources of
uncertainty during an initial round of judgment elicitation, we can then select
a targeted intervention adding context or deliberation to most effectively
reduce uncertainty on each item being judged. We test our approach on two
tasks: rating word pair similarity and toxicity of online comments, showing
that targeted interventions reduced uncertainty for the most uncertain cases.
In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and
a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We
also found through a simulation that our targeted approach reduced the average
uncertainty scores for both sources of uncertainty as opposed to uniform
approaches where reductions in average uncertainty from one source came with an
increase for the other
Making Online Communities 'Better': A Taxonomy of Community Values on Reddit
Many researchers studying online social communities seek to make such
communities better. However, understanding what 'better' means is challenging,
due to the divergent opinions of community members, and the multitude of
possible community values which often conflict with one another. Community
members' own values for their communities are not well understood, and how
these values align with one another is an open question. Previous research has
mostly focused on specific and comparatively well-defined harms within online
communities, such as harassment, rule-breaking, and misinformation. In this
work, we ask 39 community members on reddit to describe their values for their
communities. We gather 301 responses in members' own words, spanning 125 unique
communities, and use iterative categorization to produce a taxonomy of 29
different community values across 9 major categories. We find that members
value a broad range of topics ranging from technical features to the diversity
of the community, and most frequently prioritize content quality. We identify
important understudied topics such as content quality and community size,
highlight where values conflict with one another, and call for research into
governance methods for communities that protect vulnerable members.Comment: 26 pages, 3 figure
Pika: Empowering Non-Programmers to Author Executable Governance Policies in Online Communities
Internet users have formed a wide array of online communities with nuanced
and diverse community goals and norms. However, most online platforms only
offer a limited set of governance models in their software infrastructure and
leave little room for customization. Consequently, technical proficiency
becomes a prerequisite for online communities to build governance policies in
code, excluding non-programmers from participation in designing community
governance. In this paper, we present Pika, a system that empowers
non-programmers to author a wide range of executable governance policies. At
its core, Pika incorporates a declarative language that decomposes governance
policies into modular components, thereby facilitating expressive policy
authoring through a user-friendly, form-based web interface. Our user studies
with 17 participants show that Pika can empower non-programmers to author
governance policies approximately 2.5 times faster than programmers who author
in code. We also provide insights about Pika's expressivity in supporting
diverse policies that online communities want.Comment: Under revie
Voluntary Disclosure and Information Asymmetry: Evidence from the 2005 Securities Offering Reform
In 2005, the Securities and Exchange Commission enacted the Securities Offering Reform (Reform), which relaxes âgunâjumpingâ restrictions, thereby allowing firms to more freely disclose information before equity offerings. We examine the effect of the Reform on voluntary disclosure behavior before equity offerings and the associated economic consequences. We find that firms provide significantly more preoffering disclosures after the Reform. Further, we find that these preoffering disclosures are associated with a decrease in information asymmetry and a reduction in the cost of raising equity capital. Our findings not only inform the debate on the market effect of the Reform, but also speak to the literature on the relation between voluntary disclosure and information asymmetry by examining the effect of quasiâexogenous changes in voluntary disclosure on information asymmetry, and thus a firm's cost of capital.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/100337/1/joar12022.pd
- âŠ