737 research outputs found
Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach
The ever-growing number of people using Twitter makes it a valuable source of
timely information. However, detecting events in Twitter is a difficult task,
because tweets that report interesting events are overwhelmed by a large volume
of tweets on unrelated topics. Existing methods focus on the textual content of
tweets and ignore the social aspect of Twitter. In this paper we propose MABED
(i.e. mention-anomaly-based event detection), a novel statistical method that
relies solely on tweets and leverages the creation frequency of dynamic links
(i.e. mentions) that users insert in tweets to detect significant events and
estimate the magnitude of their impact over the crowd. MABED also differs from
the literature in that it dynamically estimates the period of time during which
each event is discussed, rather than assuming a predefined fixed duration for
all events. The experiments we conducted on both English and French Twitter
data show that the mention-anomaly-based approach leads to more accurate event
detection and improved robustness in presence of noisy Twitter content.
Qualitatively speaking, we find that MABED helps with the interpretation of
detected events by providing clear textual descriptions and precise temporal
descriptions. We also show how MABED can help understanding users' interest.
Furthermore, we describe three visualizations designed to favor an efficient
exploration of the detected events.Comment: 17 page
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Extracting Personal Behavioral Patterns from Geo-Referenced Tweets
This paper presents an exploratory study of the potential of geo-referenced Twitter data for extracting knowledge about significant personal places, behaviors and potential interests of people. The study was done analysing two months’ worth of tweets from residents of the greater Seattle area
POISED: Spotting Twitter Spam Off the Beaten Paths
Cybercriminals have found in online social networks a propitious medium to
spread spam and malicious content. Existing techniques for detecting spam
include predicting the trustworthiness of accounts and analyzing the content of
these messages. However, advanced attackers can still successfully evade these
defenses.
Online social networks bring people who have personal connections or share
common interests to form communities. In this paper, we first show that users
within a networked community share some topics of interest. Moreover, content
shared on these social network tend to propagate according to the interests of
people. Dissemination paths may emerge where some communities post similar
messages, based on the interests of those communities. Spam and other malicious
content, on the other hand, follow different spreading patterns.
In this paper, we follow this insight and present POISED, a system that
leverages the differences in propagation between benign and malicious messages
on social networks to identify spam and other unwanted content. We test our
system on a dataset of 1.3M tweets collected from 64K users, and we show that
our approach is effective in detecting malicious messages, reaching 91%
precision and 93% recall. We also show that POISED's detection is more
comprehensive than previous systems, by comparing it to three state-of-the-art
spam detection systems that have been proposed by the research community in the
past. POISED significantly outperforms each of these systems. Moreover, through
simulations, we show how POISED is effective in the early detection of spam
messages and how it is resilient against two well-known adversarial machine
learning attacks
A Survey on Visual Analytics of Social Media Data
The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..
Mining urban events from the tweet stream through a probabilistic mixture model
The geographical identification of content in Social Networks have enabled to bridge the gap between online social platforms and the physical world. Although vast amounts of data in such networks are due to breaking news or global occurrences, local events witnessed by users in situ are also present in these streams and of great importance for many city entities. Nowadays, unsupervised machine learning techniques, such as Tweet-SCAN, are able to retrospectively detect these local events from tweets. However, these approaches have limited abilities to reason about unseen observations in a principled way due to the lack of a proper probabilistic foundation. Probabilistic models have also been proposed for the task, but their event identification capabilities are far from those of Tweet-SCAN. In this paper, we identify two key factors which, when combined, boost the accuracy of such models. As a first key factor, we notice that the large amount of meaningless social data requires explicitly modeling non-event observations.Therefore, we propose to incorporate a background model that captures spatio-temporal fluctuations of non-event tweets. As a second key factor, we observe that the shortness of tweets hampers the application of traditional topic models. Thus, we integrate event detection and topic modeling, assigning topic proportions to events instead of assigning them to individual tweets. As a result, we propose Warble, a new probabilistic model and learning scheme for retrospective event detection that incorporates these two key factors. We evaluate Warble in a data set of tweets located in Barcelona during its festivities. The empirical results show that the model outperforms other state-of-the-art techniques in detecting various types of events while relying on a principled probabilistic framework that enables to reason under uncertainty.This work is partially supported by Obra Social “la Caixa”, by the Spanish Ministry of Science and Innovation under contract (TIN2015-65316), by the Severo Ochoa Program (SEV2015-0493), by SGR programs of the Catalan Government (2014-SGR-1051, 2014-SGR-118), Collectiveware
(TIN2015-66863-C2-1-R) and BSC/UPC NVIDIA GPU Center of Excellence.We would also like to thank the reviewers for their constructive feedback.Peer ReviewedPostprint (author's final draft
Report on the Information Retrieval Festival (IRFest2017)
The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017
The social sciences and the web : From ‘Lurking’ to interdisciplinary ‘Big Data’ research
Acknowledgements This research is supported by the award made by the RCUK Digital Economy theme to the dot.rural Digital Economy Hub (award reference: EP/G066051/1) and the UK Economic & Social Research Council (ESRC) (award reference: ES/M001628/1).Peer reviewedPublisher PD
Reliable and Interpretable Drift Detection in Streams of Short Texts
Data drift is the change in model input data that is one of the key factors
leading to machine learning models performance degradation over time.
Monitoring drift helps detecting these issues and preventing their harmful
consequences. Meaningful drift interpretation is a fundamental step towards
effective re-training of the model. In this study we propose an end-to-end
framework for reliable model-agnostic change-point detection and interpretation
in large task-oriented dialog systems, proven effective in multiple customer
deployments. We evaluate our approach and demonstrate its benefits with a novel
variant of intent classification training dataset, simulating customer requests
to a dialog system. We make the data publicly available.Comment: ACL2023 industry track (9 pages
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