9,074 research outputs found
Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis
Notwithstanding recent work which has demonstrated the potential of using
Twitter messages for content-specific data mining and analysis, the depth of
such analysis is inherently limited by the scarcity of data imposed by the 140
character tweet limit. In this paper we describe a novel approach for targeted
knowledge exploration which uses tweet content analysis as a preliminary step.
This step is used to bootstrap more sophisticated data collection from directly
related but much richer content sources. In particular we demonstrate that
valuable information can be collected by following URLs included in tweets. We
automatically extract content from the corresponding web pages and treating
each web page as a document linked to the original tweet show how a temporal
topic model based on a hierarchical Dirichlet process can be used to track the
evolution of a complex topic structure of a Twitter community. Using
autism-related tweets we demonstrate that our method is capable of capturing a
much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining, 201
The Neurocognitive Process of Digital Radicalization: A Theoretical Model and Analytical Framework
Recent studies suggest that empathy induced by narrative messages can effectively facilitate persuasion and reduce psychological reactance. Although limited, emerging research on the etiology of radical political behavior has begun to explore the role of narratives in shaping an individual’s beliefs, attitudes, and intentions that culminate in radicalization. The existing studies focus exclusively on the influence of narrative persuasion on an individual, but they overlook the necessity of empathy and that in the absence of empathy, persuasion is not salient. We argue that terrorist organizations are strategic in cultivating empathetic-persuasive messages using audiovisual materials, and disseminating their message within the digital medium. Therefore, in this paper we propose a theoretical model and analytical framework capable of helping us better understand the neurocognitive process of digital radicalization
AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments
This report considers the application of Articial Intelligence (AI) techniques to
the problem of misuse detection and misuse localisation within telecommunications
environments. A broad survey of techniques is provided, that covers inter alia
rule based systems, model-based systems, case based reasoning, pattern matching,
clustering and feature extraction, articial neural networks, genetic algorithms, arti
cial immune systems, agent based systems, data mining and a variety of hybrid
approaches. The report then considers the central issue of event correlation, that
is at the heart of many misuse detection and localisation systems. The notion of
being able to infer misuse by the correlation of individual temporally distributed
events within a multiple data stream environment is explored, and a range of techniques,
covering model based approaches, `programmed' AI and machine learning
paradigms. It is found that, in general, correlation is best achieved via rule based approaches,
but that these suffer from a number of drawbacks, such as the difculty of
developing and maintaining an appropriate knowledge base, and the lack of ability
to generalise from known misuses to new unseen misuses. Two distinct approaches
are evident. One attempts to encode knowledge of known misuses, typically within
rules, and use this to screen events. This approach cannot generally detect misuses
for which it has not been programmed, i.e. it is prone to issuing false negatives.
The other attempts to `learn' the features of event patterns that constitute normal
behaviour, and, by observing patterns that do not match expected behaviour, detect
when a misuse has occurred. This approach is prone to issuing false positives,
i.e. inferring misuse from innocent patterns of behaviour that the system was not
trained to recognise. Contemporary approaches are seen to favour hybridisation,
often combining detection or localisation mechanisms for both abnormal and normal
behaviour, the former to capture known cases of misuse, the latter to capture
unknown cases. In some systems, these mechanisms even work together to update
each other to increase detection rates and lower false positive rates. It is concluded
that hybridisation offers the most promising future direction, but that a rule or state
based component is likely to remain, being the most natural approach to the correlation
of complex events. The challenge, then, is to mitigate the weaknesses of
canonical programmed systems such that learning, generalisation and adaptation
are more readily facilitated
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter
Introduction Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Objectives Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts. Methods We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall®, oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time. Results Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall®: 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time. Conclusion Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks
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Identifying Important Life Events from Twitter Using Semantic and Syntactic Patterns
Identifying global events from social media has been the focus of much research in recent years. However, the identification of personal life events poses new requirements and challenges that have received relatively little research attention. In this paper we explore a new approach for life event identification, where we expand social media posts into both semantic, and syntactic networks of content. Frequent graph patterns are mined from these networks and used as features to enrich life-event classifiers. Results show that our approach significantly outperforms the best performing baseline in accuracy (by 4.48% points) and F-measure (by 4.54% points) when used to identify five major life events identified from the psychology literature: Getting Married, Having Children, Death of a Parent, Starting School, and Falling in Love. In addition, our results show that, while semantic graphs are effective at discriminating the theme of the post (e.g. the topic of marriage), syntactic graphs help identify whether the post describes a personal event (e.g. someone getting married)
Discovering lesser known molecular players and mechanistic patterns in Alzheimer's disease using an integrative disease modelling approach
Convergence of exponentially advancing technologies is driving medical research with life changing discoveries. On the contrary, repeated failures of high-profile drugs to battle Alzheimer's disease (AD) has made it one of the least successful therapeutic area. This failure pattern has provoked researchers to grapple with their beliefs about Alzheimer's aetiology. Thus, growing realisation that Amyloid-β and tau are not 'the' but rather 'one of the' factors necessitates the reassessment of pre-existing data to add new perspectives. To enable a holistic view of the disease, integrative modelling approaches are emerging as a powerful technique. Combining data at different scales and modes could considerably increase the predictive power of the integrative model by filling biological knowledge gaps. However, the reliability of the derived hypotheses largely depends on the completeness, quality, consistency, and context-specificity of the data. Thus, there is a need for agile methods and approaches that efficiently interrogate and utilise existing public data. This thesis presents the development of novel approaches and methods that address intrinsic issues of data integration and analysis in AD research. It aims to prioritise lesser-known AD candidates using highly curated and precise knowledge derived from integrated data. Here much of the emphasis is put on quality, reliability, and context-specificity. This thesis work showcases the benefit of integrating well-curated and disease-specific heterogeneous data in a semantic web-based framework for mining actionable knowledge. Furthermore, it introduces to the challenges encountered while harvesting information from literature and transcriptomic resources. State-of-the-art text-mining methodology is developed to extract miRNAs and its regulatory role in diseases and genes from the biomedical literature. To enable meta-analysis of biologically related transcriptomic data, a highly-curated metadata database has been developed, which explicates annotations specific to human and animal models. Finally, to corroborate common mechanistic patterns — embedded with novel candidates — across large-scale AD transcriptomic data, a new approach to generate gene regulatory networks has been developed. The work presented here has demonstrated its capability in identifying testable mechanistic hypotheses containing previously unknown or emerging knowledge from public data in two major publicly funded projects for Alzheimer's, Parkinson's and Epilepsy diseases
Mining social media data for biomedical signals and health-related behavior
Social media data has been increasingly used to study biomedical and
health-related phenomena. From cohort level discussions of a condition to
planetary level analyses of sentiment, social media has provided scientists
with unprecedented amounts of data to study human behavior and response
associated with a variety of health conditions and medical treatments. Here we
review recent work in mining social media for biomedical, epidemiological, and
social phenomena information relevant to the multilevel complexity of human
health. We pay particular attention to topics where social media data analysis
has shown the most progress, including pharmacovigilance, sentiment analysis
especially for mental health, and other areas. We also discuss a variety of
innovative uses of social media data for health-related applications and
important limitations in social media data access and use.Comment: To appear in the Annual Review of Biomedical Data Scienc
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
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