Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Integration of opinion into customer analysis model

Nowadays, Opinion Mining is getting more important than before especially in doing analysis and forecasting about customers’ behavior for businesses purpose. The right decision in producing new products or services based on data about customers’ characteristics means profit for organization/company. This paper proposes a new architecture for Opinion Mining, which uses a multidimensional model to integrate customers’ characteristics and their comments about products (or services). The key step to achieve this objective is to transfer comments (opinions) to a fact table that includes several dimensions, such as, customers, products, time and locations. This research presents a comprehensive way to calculate customers’ orientation for all possible products’ attributes. A use case study is also presented in this paper to show the advantages of using OLAP and data cubes to analyze costumers’ opinions.

Representing USDL for humans and tools

This chapter deals with technical aspects of how USDL service descriptions can be read from and written to different representations for use by humans and tools. A combination of techniques for representing and exchanging USDL have been drawn from Model-Driven Engineering and Semantic Web technologies. The USDL language's structural definition is specified as a MOF meta-model, but some modules were originally defined using the OWL language from the Semantic Web community and translated to the meta-model format. We begin with the important topic of serializing USDL descriptions into XML, so that they can be exchanged beween editors, repositories, and other tools. The following topic is how USDL can be made available through the Semantic Web as a network of linked data, connected via URIs. Finally, consideration is given to human-readable representations of USDL descriptions, and how they can be generated, in large part, from the contents of a stored USDL model.

Self-identified experts lost on the interwebs

Security cues found in web browsers are meant to alert users to potential online threats, yet many studies demonstrate that security indicators are largely ineffective in this regard. Those studies have depended upon self-reporting of subjects' use or aggregate experimentation that correlate responses to sites with and without indicators. We report on a laboratory experiment using eye-tracking to follow the behavior of self-identified computer experts as they share information across popular social media websites. The use of eye-tracking equipment allows us to explore possible behavioral differences in the way experts perceive web browser security cues, as opposed to non-experts. Unfortunately, due to the use of self-identified experts, technological issues with the setup, and demographic anomalies, our results are inconclusive. We describe our initial experimental design, lessons learned in our experimentation, and provide a set of steps for others to follow in implementing experiments using unfamiliar technologies, eye-tracking specifically, subjects with different experience with the laboratory tasks, as well as individuals with varying security expertise. We also discuss recruitment and how our design will address the inherent uncertainties in recruitment, as opposed to design for an ideal population. Some of these modifications are generalizable, together they will allow us to run a larger 2x2 study, rather than a study of only experts using two different single sign-on systems.

On the security of TLS renegotiation

The Transport Layer Security (TLS) protocol is the most widely used security protocol on the Internet. It supports negotiation of a wide variety of cryptographic primitives through different cipher suites, various modes of client authentication, and additional features such as renegotiation. Despite its widespread use, only recently has the full TLS protocol been proven secure, and only the core cryptographic protocol with no additional features. These additional features have been the cause of several practical attacks on TLS. In 2009, Ray and Dispensa demonstrated how TLS renegotiation allows an attacker to splice together its own session with that of a victim, resulting in a man-in-the-middle attack on TLS-reliant applications such as HTTP. TLS was subsequently patched with two defence mechanisms for protection against this attack. We present the first formal treatment of renegotiation in secure channel establishment protocols. We add optional renegotiation to the authenticated and confidential channel establishment model of Jager et al., an adaptation of the Bellare--Rogaway authenticated key exchange model. We describe the attack of Ray and Dispensa on TLS within our model. We show generically that the proposed fixes for TLS offer good protection against renegotiation attacks, and give a simple new countermeasure that provides renegotiation security for TLS even in the face of stronger adversaries.

A topic based document relevance ranking model

Topic modelling has been widely used in the fields of information retrieval, text mining, machine learning, etc. In this paper, we propose a novel model, Pattern Enhanced Topic Model (PETM), which makes improvements to topic modelling by semantically representing topics with discriminative patterns, and also makes innovative contributions to information filtering by utilising the proposed PETM to determine document relevance based on topics distribution and maximum matched patterns proposed in this paper. Extensive experiments are conducted to evaluate the effectiveness of PETM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models.

Toward understanding social engineering

There is no doubt that social engineering plays a vital role in compromising most security defenses, and in attacks on people, organizations, companies, or even governments. It is the art of deceiving and tricking people to reveal critical information or to perform an action that benefits the attacker in some way. Fraudulent and deceptive people have been using social engineering traps and tactics using information technology such as e-mails, social networks, web sites, and applications to trick victims into obeying them, accepting threats, and falling victim to various crimes and attacks such as phishing, sexual abuse, financial abuse, identity theft, impersonation, physical crime, and many other forms of attack. Although organizations, researchers, practitioners, and lawyers recognize the severe risk of social engineering-based threats, there is a severe lack of understanding and controlling of such threats. One side of the problem is perhaps the unclear concept of social engineering as well as the complexity of understand human behaviors in behaving toward, approaching, accepting, and failing to recognize threats or the deception behind them. The aim of this paper is to explain the definition of social engineering based on the related theories of the many related disciplines such as psychology, sociology, information technology, marketing, and behaviourism. We hope, by this work, to help researchers, practitioners, lawyers, and other decision makers to get a fuller picture of social engineering and, therefore, to open new directions of collaboration toward detecting and controlling it.

eHealth-as-a-Service (eHaaS) : the industrialisation of health informatics, a practical approach

With the introduction of the Personally Controlled Health Record (PCEHR), the Australian public is being asked to accept greater responsibility for their healthcare. Although well designed, constructed and intentioned, policy and privacy concerns have resulted in an eHealth model that may impact future health information sharing requirements. Thus an opportunity to transform the beleaguered Australian PCEHR into a sustainable on-demand technology consumption model for patient safety must be explored further. Moreover, the current clerical focus of healthcare practitioners must be renegotiated to establish a shared knowledge creation landscape of action for safer patient interventions. To achieve this potential however requires a platform that will facilitate efficient and trusted unification of all health information available in real-time across the continuum of care. As a conceptual paper, the goal of the authors is to deliver insights into the antecedents of usage influencing superior patient outcomes within an eHealth-as-a-Service framework. To achieve this, the paper attempts to distil key concepts and identify common themes drawn from a preliminary literature review of eHealth and cloud computing concepts, specifically cloud service orchestration to establish a conceptual framework and a research agenda. Initial findings support the authors’ view that an eHealth-as-a-Service (eHaaS) construct will serve as a disruptive paradigm shift in the aggregation and transformation of health information for use as real-world knowledge in patient care scenarios. Moreover, the strategic value of extending the community Health Record Bank (HRB) model lies in the ability to automatically draw on a multitude of relevant data repositories and sources to create a single source of practice based evidence and to engage market forces to create financial sustainability.

You are now entering the Twittersphere! Where EE meets social media

“Humans did not weave web 2.0, they are merely a strand in it. Whatever they do to the interweb, they do to themselves.” Is social media just a diversionary gimmick? A passing phase? The latest craze? Or can social media be socially ‘transformative’ media with an integral place in EE pedagogies? In this paper I will examine the affordances of Web 2.0 (and 3.0) technologies, such as social media, in the light of differences in perceptions of what technology represents underpinned by comparative theories of technology. An instrumental theory (or neutralist approach) of technology is one which views technology as a ‘tool’ without any inherent value whereas a critical theory of technology views technology as a site of struggle, of power relationships and of social transformation. From a critical perspective Web 2.0 (and 3.0) technologies have the capacity to democratise the knowledge economy by turning knowledge consumers into “prod-users” (Bruns, 2008); to promote ubiquitous learning; and to facilitate collaboration through online Communities of Practice. However, applying a critical technology perspective means that we also need to consider that web technologies are not a panacea: misappropriation and indiscriminate use of technology; substitution for valid EE experiences; environmental impacts; and exacerbating the digital divide are the flip side of the coin (W. J. Rohwedder, 1999).

Accelerating entomology with Web3D insects

Capturing high-quality 3D models of insects is challenging - they are usually too small for laser or depth camera based systems, and techniques such as CT scanning do not record color. We have developed a prototype system that generates unprecedentedly high-quality natural-color 3D models of various insects from 3mm to 30 mm in length. Through the use of 3D web standards we are able to use these models to develop novel applications for entomologists and ensure wide accessibility. © 2014 Authors.

Interpreting discovered patterns in terms of ontology concepts

Semantic Web offers many possibilities for future Web technologies. Therefore, it is a need to search for ways that can bring the huge amount of unstructured documents from current Web to Semantic Web automatically. One big challenge in searching for such ways is how to understand patterns by both humans and machine. To address this issue, we present an innovative model which interprets patterns to high level concepts. These concepts can explain the patterns' meanings in a human understandable way while improving the information filtering performance. The model is evaluated by comparing it against one state-of-the-art benchmark model using standard Reuters dataset. The results show that the proposed model is successful. The significance of this model is three fold. It gives a way to interpret text mining output, provides a technique to find concepts relevant to the whole set of patterns which is an essential feature to understand the topic, and to some extent overcomes information mismatch and overload problems of existing models. This model will be very useful for knowledge based applications.

Chronicling the patient journey: Co-creating value with digital health ecosystems

With the introduction of the PCEHR (Personally Controlled Electronic Health Record), the Australian public is being asked to accept greater responsibility for the management of their health information. However, the implementation of the PCEHR has occasioned poor adoption rates underscored by criticism from stakeholders with concerns about transparency, accountability, privacy, confidentiality, governance, and limited capabilities. This study adopts an ethnographic lens to observe how information is created and used during the patient journey and the social factors impacting on the adoption of the PCEHR at the micro-level in order to develop a conceptual model that will encourage the sharing of patient information within the cycle of care. Objective: This study aims to firstly, establish a basic understanding of healthcare professional attitudes toward a national platform for sharing patient summary information in the form of a PCEHR. Secondly, the studies aims to map the flow of patient related information as it traverses a patient’s personal cycle of care. Thus, an ethnographic approach was used to bring a “real world” lens to information flow in a series of case studies in the Australian healthcare system to discover themes and issues that are important from the patient’s perspective. Design: Qualitative study utilising ethnographic case studies. Setting: Case studies were conducted at primary and allied healthcare professionals located in Brisbane Queensland between October 2013 and July 2014. Results: In the first dimension, it was identified that healthcare professionals’ concerns about trust and medico-legal issues related to patient control and information quality, and the lack of clinical value available with the PCEHR emerged as significant barriers to use. The second dimension of the study which attempted to map patient information flow identified information quality issues, clinical workflow inefficiencies and interoperability misconceptions resulting in duplication of effort, unnecessary manual processes, data quality and integrity issues and an over reliance on the understanding and communication skills of the patient. Conclusion: Opportunities for process efficiencies, improved data quality and increased patient safety emerge with the adoption of an appropriate information sharing platform. More importantly, large scale eHealth initiatives must be aligned with the value proposition of individual stakeholders in order to achieve widespread adoption. Leveraging an Australian national eHealth infrastructure and the PCEHR we offer a practical example of a service driven digital ecosystem suitable for co-creating value in healthcare.