123,991 research outputs found
User Participation in Social Media: Digg Study
The social news aggregator Digg allows users to submit and moderate stories
by voting on (digging) them. As is true of most social sites, user
participation on Digg is non-uniformly distributed, with few users contributing
a disproportionate fraction of content. We studied user participation on Digg,
to see whether it is motivated by competition, fueled by user ranking, or
social factors, such as community acceptance.
For our study we collected activity data of the top users weekly over the
course of a year. We computed the number of stories users submitted, dugg or
commented on weekly. We report a spike in user activity in September 2006,
followed by a gradual decline, which seems unaffected by the elimination of
user ranking. The spike can be explained by a controversy that broke out at the
beginning of September 2006. We believe that the lasting acrimony that this
incident has created led to a decline of top user participation on Digg.Comment: Workshops of 2007 IEEE/WIC/ACM International Conference on Web
Intelligence and Intelligent Agent Technology (WI-IAT 07
A simple person's approach to understanding the contagion condition for spreading processes on generalized random networks
We present derivations of the contagion condition for a range of spreading
mechanisms on families of generalized random networks and bipartite random
networks. We show how the contagion condition can be broken into three
elements, two structural in nature, and the third a meshing of the contagion
process and the network. The contagion conditions we obtain reflect the
spreading dynamics in a clear, interpretable way. For threshold contagion, we
discuss results for all-to-all and random network versions of the model, and
draw connections between them.Comment: 10 pages, 9 figures; chapter to appear in "Spreading Dynamics in
Social Systems"; Eds. Sune Lehmann and Yong-Yeol Ahn, Springer Natur
Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
To help their users to discover important items at a particular time, major
websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K
recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most
Viewed News Stories), which rely on crowdsourced popularity signals to select
the items. However, different sections of a crowd may have different
preferences, and there is a large silent majority who do not explicitly express
their opinion. Also, the crowd often consists of actors like bots, spammers, or
people running orchestrated campaigns. Recommendation algorithms today largely
do not consider such nuances, hence are vulnerable to strategic manipulation by
small but hyper-active user groups.
To fairly aggregate the preferences of all users while recommending top-K
items, we borrow ideas from prior research on social choice theory, and
identify a voting mechanism called Single Transferable Vote (STV) as having
many of the fairness properties we desire in top-K item (s)elections. We
develop an innovative mechanism to attribute preferences of silent majority
which also make STV completely operational. We show the generalizability of our
approach by implementing it on two different real-world datasets. Through
extensive experimentation and comparison with state-of-the-art techniques, we
show that our proposed approach provides maximum user satisfaction, and cuts
down drastically on items disliked by most but hyper-actively promoted by a few
users.Comment: In the proceedings of the Conference on Fairness, Accountability, and
Transparency (FAT* '19). Please cite the conference versio
Re-examining the contributions of money and banking shocks to the U.S. Great Depression
This paper quantitatively evaluates the hypothesis that deflation can account for much of the Great Depression (1929–33). We examine two popular explanations of the Depression: (1) The “high wage” story, according to which deflation, combined with imperfectly flexible wages, raised real wages and reduced employment and output. (2) The “bank failure” story, according to which deflationary money shocks contributed to bank failures and to a reduction in the efficiency of financial intermediation, which in turn reduced lending and output. We evaluate these stories using general equilibrium business cycle models, and find that wage shocks and banking shocks account for a small fraction of the Great Depression. We also find that some other predictions of the theories are at variance with the data.Monetary policy ; Depressions ; Deflation (Finance) ; Banks and banking
Social Dynamics of Digg
Online social media provide multiple ways to find interesting content. One
important method is highlighting content recommended by user's friends. We
examine this process on one such site, the news aggregator Digg. With a
stochastic model of user behavior, we distinguish the effects of the content
visibility and interestingness to users. We find a wide range of interest and
distinguish stories primarily of interest to a users' friends from those of
interest to the entire user community. We show how this model predicts a
story's eventual popularity from users' early reactions to it, and estimate the
prediction reliability. This modeling framework can help evaluate alternative
design choices for displaying content on the site.Comment: arXiv admin note: text overlap with arXiv:1010.023
Analyzing and Modeling Special Offer Campaigns in Location-based Social Networks
The proliferation of mobile handheld devices in combination with the
technological advancements in mobile computing has led to a number of
innovative services that make use of the location information available on such
devices. Traditional yellow pages websites have now moved to mobile platforms,
giving the opportunity to local businesses and potential, near-by, customers to
connect. These platforms can offer an affordable advertisement channel to local
businesses. One of the mechanisms offered by location-based social networks
(LBSNs) allows businesses to provide special offers to their customers that
connect through the platform. We collect a large time-series dataset from
approximately 14 million venues on Foursquare and analyze the performance of
such campaigns using randomization techniques and (non-parametric) hypothesis
testing with statistical bootstrapping. Our main finding indicates that this
type of promotions are not as effective as anecdote success stories might
suggest. Finally, we design classifiers by extracting three different types of
features that are able to provide an educated decision on whether a special
offer campaign for a local business will succeed or not both in short and long
term.Comment: in The 9th International AAAI Conference on Web and Social Media
(ICWSM 2015
- …