192 research outputs found
On the discovery of social roles in large scale social systems
The social role of a participant in a social system is a label
conceptualizing the circumstances under which she interacts within it. They may
be used as a theoretical tool that explains why and how users participate in an
online social system. Social role analysis also serves practical purposes, such
as reducing the structure of complex systems to rela- tionships among roles
rather than alters, and enabling a comparison of social systems that emerge in
similar contexts. This article presents a data-driven approach for the
discovery of social roles in large scale social systems. Motivated by an
analysis of the present art, the method discovers roles by the conditional
triad censuses of user ego-networks, which is a promising tool because they
capture the degree to which basic social forces push upon a user to interact
with others. Clusters of censuses, inferred from samples of large scale network
carefully chosen to preserve local structural prop- erties, define the social
roles. The promise of the method is demonstrated by discussing and discovering
the roles that emerge in both Facebook and Wikipedia. The article con- cludes
with a discussion of the challenges and future opportunities in the discovery
of social roles in large social systems
Seasonality in Dynamic Stochastic Block Models
Sociotechnological and geospatial processes exhibit time varying structure
that make insight discovery challenging. This paper proposes a new statistical
model for such systems, modeled as dynamic networks, to address this challenge.
It assumes that vertices fall into one of k types and that the probability of
edge formation at a particular time depends on the types of the incident nodes
and the current time. The time dependencies are driven by unique seasonal
processes, which many systems exhibit (e.g., predictable spikes in geospatial
or web traffic each day). The paper defines the model as a generative process
and an inference procedure to recover the seasonal processes from data when
they are unknown. Evaluation with synthetic dynamic networks show the recovery
of the latent seasonal processes that drive its formation.Comment: 4 page worksho
Realistic Traffic Generation for Web Robots
Critical to evaluating the capacity, scalability, and availability of web
systems are realistic web traffic generators. Web traffic generation is a
classic research problem, no generator accounts for the characteristics of web
robots or crawlers that are now the dominant source of traffic to a web server.
Administrators are thus unable to test, stress, and evaluate how their systems
perform in the face of ever increasing levels of web robot traffic. To resolve
this problem, this paper introduces a novel approach to generate synthetic web
robot traffic with high fidelity. It generates traffic that accounts for both
the temporal and behavioral qualities of robot traffic by statistical and
Bayesian models that are fitted to the properties of robot traffic seen in web
logs from North America and Europe. We evaluate our traffic generator by
comparing the characteristics of generated traffic to those of the original
data. We look at session arrival rates, inter-arrival times and session
lengths, comparing and contrasting them between generated and real traffic.
Finally, we show that our generated traffic affects cache performance similarly
to actual traffic, using the common LRU and LFU eviction policies.Comment: 8 page
Accurate Local Estimation of Geo-Coordinates for Social Media Posts
Associating geo-coordinates with the content of social media posts can
enhance many existing applications and services and enable a host of new ones.
Unfortunately, a majority of social media posts are not tagged with
geo-coordinates. Even when location data is available, it may be inaccurate,
very broad or sometimes fictitious. Contemporary location estimation approaches
based on analyzing the content of these posts can identify only broad areas
such as a city, which limits their usefulness. To address these shortcomings,
this paper proposes a methodology to narrowly estimate the geo-coordinates of
social media posts with high accuracy. The methodology relies solely on the
content of these posts and prior knowledge of the wide geographical region from
where the posts originate. An ensemble of language models, which are smoothed
over non-overlapping sub-regions of a wider region, lie at the heart of the
methodology. Experimental evaluation using a corpus of over half a million
tweets from New York City shows that the approach, on an average, estimates
locations of tweets to within just 2.15km of their actual positions.Comment: In Proceedings of the 26th International Conference on Software
Engineering and Knowledge Engineering, pp. 642 - 647, 201
Visual Entailment: A Novel Task for Fine-Grained Image Understanding
Existing visual reasoning datasets such as Visual Question Answering (VQA),
often suffer from biases conditioned on the question, image or answer
distributions. The recently proposed CLEVR dataset addresses these limitations
and requires fine-grained reasoning but the dataset is synthetic and consists
of similar objects and sentence structures across the dataset.
In this paper, we introduce a new inference task, Visual Entailment (VE) -
consisting of image-sentence pairs whereby a premise is defined by an image,
rather than a natural language sentence as in traditional Textual Entailment
tasks. The goal of a trained VE model is to predict whether the image
semantically entails the text. To realize this task, we build a dataset SNLI-VE
based on the Stanford Natural Language Inference corpus and Flickr30k dataset.
We evaluate various existing VQA baselines and build a model called Explainable
Visual Entailment (EVE) system to address the VE task. EVE achieves up to 71%
accuracy and outperforms several other state-of-the-art VQA based models.
Finally, we demonstrate the explainability of EVE through cross-modal attention
visualizations. The SNLI-VE dataset is publicly available at
https://github.com/ necla-ml/SNLI-VE
Finding Street Gang Members on Twitter
Most street gang members use Twitter to intimidate others, to present
outrageous images and statements to the world, and to share recent illegal
activities. Their tweets may thus be useful to law enforcement agencies to
discover clues about recent crimes or to anticipate ones that may occur.
Finding these posts, however, requires a method to discover gang member Twitter
profiles. This is a challenging task since gang members represent a very small
population of the 320 million Twitter users. This paper studies the problem of
automatically finding gang members on Twitter. It outlines a process to curate
one of the largest sets of verifiable gang member profiles that have ever been
studied. A review of these profiles establishes differences in the language,
images, YouTube links, and emojis gang members use compared to the rest of the
Twitter population. Features from this review are used to train a series of
supervised classifiers. Our classifier achieves a promising F1 score with a low
false positive rate.Comment: 8 pages, 9 figures, 2 tables, Published as a full paper at 2016
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining (ASONAM 2016
Visual Entailment Task for Visually-Grounded Language Learning
We introduce a new inference task - Visual Entailment (VE) - which differs
from traditional Textual Entailment (TE) tasks whereby a premise is defined by
an image, rather than a natural language sentence as in TE tasks. A novel
dataset SNLI-VE (publicly available at https://github.com/necla-ml/SNLI-VE) is
proposed for VE tasks based on the Stanford Natural Language Inference corpus
and Flickr30k. We introduce a differentiable architecture called the
Explainable Visual Entailment model (EVE) to tackle the VE problem. EVE and
several other state-of-the-art visual question answering (VQA) based models are
evaluated on the SNLI-VE dataset, facilitating grounded language understanding
and providing insights on how modern VQA based models perform.Comment: 4 pages, accepted by Visually Grounded Interaction and Language
(ViGIL) workshop in NeurIPS 201
A Broad Evaluation of the Tor English Content Ecosystem
Tor is among most well-known dark net in the world. It has noble uses,
including as a platform for free speech and information dissemination under the
guise of true anonymity, but may be culturally better known as a conduit for
criminal activity and as a platform to market illicit goods and data. Past
studies on the content of Tor support this notion, but were carried out by
targeting popular domains likely to contain illicit content. A survey of past
studies may thus not yield a complete evaluation of the content and use of Tor.
This work addresses this gap by presenting a broad evaluation of the content of
the English Tor ecosystem. We perform a comprehensive crawl of the Tor dark web
and, through topic and network analysis, characterize the types of information
and services hosted across a broad swath of Tor domains and their hyperlink
relational structure. We recover nine domain types defined by the information
or service they host and, among other findings, unveil how some types of
domains intentionally silo themselves from the rest of Tor. We also present
measurements that (regrettably) suggest how marketplaces of illegal drugs and
services do emerge as the dominant type of Tor domain. Our study is the product
of crawling over 1 million pages from 20,000 Tor seed addresses, yielding a
collection of over 150,000 Tor pages. We make a dataset of the intend to make
the domain structure publicly available as a dataset at
https://github.com/wsu-wacs/TorEnglishContent.Comment: 11 page
- …