26,576 research outputs found
Crowdsourced real-world sensing: sentiment analysis and the real-time web
The advent of the real-time web is proving both challeng-
ing and at the same time disruptive for a number of areas of research,
notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis
and discusses the motivations and challenges behind such a direction
Inferring short-term volatility indicators from Bitcoin blockchain
In this paper, we study the possibility of inferring early warning indicators
(EWIs) for periods of extreme bitcoin price volatility using features obtained
from Bitcoin daily transaction graphs. We infer the low-dimensional
representations of transaction graphs in the time period from 2012 to 2017
using Bitcoin blockchain, and demonstrate how these representations can be used
to predict extreme price volatility events. Our EWI, which is obtained with a
non-negative decomposition, contains more predictive information than those
obtained with singular value decomposition or scalar value of the total Bitcoin
transaction volume
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
Using Markov Models and Statistics to Learn, Extract, Fuse, and Detect Patterns in Raw Data
Many systems are partially stochastic in nature. We have derived data driven
approaches for extracting stochastic state machines (Markov models) directly
from observed data. This chapter provides an overview of our approach with
numerous practical applications. We have used this approach for inferring
shipping patterns, exploiting computer system side-channel information, and
detecting botnet activities. For contrast, we include a related data-driven
statistical inferencing approach that detects and localizes radiation sources.Comment: Accepted by 2017 International Symposium on Sensor Networks, Systems
and Securit
Inferring Mechanisms for Global Constitutional Progress
Constitutions help define domestic political orders, but are known to be
influenced by two international mechanisms: one that reflects global temporal
trends in legal development, and another that reflects international network
dynamics such as shared colonial history. We introduce the provision space; the
growing set of all legal provisions existing in the world's constitutions over
time. Through this we uncover a third mechanism influencing constitutional
change: hierarchical dependencies between legal provisions, under which the
adoption of essential, fundamental provisions precedes more advanced
provisions. This third mechanism appears to play an especially important role
in the emergence of new political rights, and may therefore provide a useful
roadmap for advocates of those rights. We further characterise each legal
provision in terms of the strength of these mechanisms
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