413 research outputs found
Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities
The proliferation of online communities has created exciting opportunities to
study the mechanisms that explain group success. While a growing body of
research investigates community success through a single measure -- typically,
the number of members -- we argue that there are multiple ways of measuring
success. Here, we present a systematic study to understand the relations
between these success definitions and test how well they can be predicted based
on community properties and behaviors from the earliest period of a community's
lifetime. We identify four success measures that are desirable for most
communities: (i) growth in the number of members; (ii) retention of members;
(iii) long term survival of the community; and (iv) volume of activities within
the community. Surprisingly, we find that our measures do not exhibit very high
correlations, suggesting that they capture different types of success.
Additionally, we find that different success measures are predicted by
different attributes of online communities, suggesting that success can be
achieved through different behaviors. Our work sheds light on the basic
understanding of what success represents in online communities and what
predicts it. Our results suggest that success is multi-faceted and cannot be
measured nor predicted by a single measurement. This insight has practical
implications for the creation of new online communities and the design of
platforms that facilitate such communities.Comment: To appear at The Web Conference 201
The Law and Economics of Contracts
This paper, which will appear as a chapter in the forthcoming Handbook of Law and Economics (A.M. Polinsky & S. Shavell, eds.), surveys major issues arising in the economic analysis of contract law. It begins with an introductory discussion of scope and methodology, and then addresses four topic areas that correspond to the major doctrinal divisions of the law of contracts. These areas include freedom of contract (i.e., the scope of private power to create binding obligations), formation of contracts (both the procedural mechanics of exchange, and rules that govern pre-contractual behavior), contract interpretation (what consequences follow when agreements are ambiguous or incomplete), and enforcement of contractual obligations. For each of these sections, we address the economic analysis of particular legal rules and institutions, and, where relevant, connections between legal arrangements and associated topics in microeconomic theory, including welfare economics and the theory of contracts
Unsupervised, Efficient and Semantic Expertise Retrieval
We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised statistical vector space and probabilistic
generative approaches. Our proposed log-linear model achieves the retrieval
performance levels of state-of-the-art document-centric methods with the low
inference cost of so-called profile-centric approaches. It yields a
statistically significant improved ranking over vector space and generative
models in most cases, matching the performance of supervised methods on various
benchmarks. That is, by using solely text we can do as well as methods that
work with external evidence and/or relevance feedback. A contrastive analysis
of rankings produced by discriminative and generative approaches shows that
they have complementary strengths due to the ability of the unsupervised
discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World
Wide Web. 201
Study of Relevance and Effort across Devices
Relevance judgements are essential for designing information
retrieval systems. Traditionally, judgements have been judgements have been gathered via desktop interfaces. However, with the rise in popularity of smaller devices for information access, it has become imperative to investigate whether desktop based judgements are different from judgements gathered using mobiles. Recently, user effort and document usefulness have also emerged as important dimensions to optimize and evaluate information retrieval systems. Since existing work is limited to desktops, it remains to be seen how these judgements are affected by user’s search device.
In this paper, we address these shortcomings by collecting
and analyzing relevance, usefulness and effort judgements on
mobiles and desktops. Analysis of these judgements indicates
that high agreement rate between desktop and mobile judges
for relevance, followed by usefulness and findability. We also found that desktop judges are likely to spend more time and examine documents in greater depth on non-relevant/notuseful/difficult documents compared to mobile judges. Based on our findings, we suggest that relevance judgements should be gathered via desktops and effort judgements should be collected on each device independently
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