11,823 research outputs found

    People on Drugs: Credibility of User Statements in Health Communities

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    Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

    Eliciting Societal Values About Cyberstalking Policy Decisions

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    Cyberstalking is a significant challenge in the era of Internet and technology. When dealing with cyberstalking, institutions and governments alike have a problem in how to manage it and where to allocate resources. Hence, it is important to understand how individuals feel about the problem of cyberstalking so it can be managed in the context of cybersecurity. To do this the problem question is twofold: First, what objectives are important based on the values of the general public with regard to the prevention of cyberstalking. Second, what are the possible scenarios for the implementation of these objectives that organizations, governments and society at large can look to that will guide their decision making process. In this paper we utilize Keeney’s (1990) public value forum to elicit public values which can form the basis for the decision making process in preventing cyberstalking so institutions and governments can allocate resources prudently

    Research Directions, Challenges and Issues in Opinion Mining

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    Rapid growth of Internet and availability of user reviews on the web for any product has provided a need for an effective system to analyze the web reviews. Such reviews are useful to some extent, promising both the customers and product manufacturers. For any popular product, the number of reviews can be in hundreds or even thousands. This creates difficulty for a customer to analyze them and make important decisions on whether to purchase the product or to not. Mining such product reviews or opinions is termed as opinion mining which is broadly classified into two main categories namely facts and opinions. Though there are several approaches for opinion mining, there remains a challenge to decide on the recommendation provided by the system. In this paper, we analyze the basics of opinion mining, challenges, pros & cons of past opinion mining systems and provide some directions for the future research work, focusing on the challenges and issues

    Pragmatic meta analytic studies: learning the lessons from naturalistic evaluations of multiple cases

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    This paper explores the concept of pragmatic meta‐analytic studies in eLearning. Much educational technology literature focuses on developers and teachers describing and reflecting on their experiences. Few connections are made between these experiential ‘stories’. The data set is fragmented and offers few generalisable lessons. The field needs guidelines about what can be learnt from such single‐case reports. The pragmatic meta‐analytic studies described in this paper have two common aspects: (1) the cases are related in some way, and (2) the data are authentic, that is, the evaluations have followed a naturalistic approach. We suggest that examining a number of such cases is best done by a mixed‐methods approach with an emphasis on qualitative strategies. In the paper, we overview 63 eLearning cases. Three main meta‐analytic strategies were used: (1) meta‐analysis of the perception of usefulness across all cases, (2) meta‐analysis of recorded benefits and challenges across all cases, and (3) meta‐analysis of smaller groups of cases where the learning design and/or use of technology are similar. This study indicated that in Hong Kong the basic and non‐interactive eLearning strategies are often valued by students, while their perceptions of interactive strategies that are potentially more beneficial fluctuate. One possible explanation relates to the level of risk that teachers and students are willing to take in venturing into more innovative teaching and learning strategies

    What’s going on in my city? Recommender systems and electronic participatory budgeting

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    In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities – Cambridge, Miami and New York City–, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area

    Methodology to predict construction contractors’ performance using non-price measures

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    Despite being one of the largest industry sectors in the world, construction continues to suffer from underperformance. Contractors are the driving force behind built assets, and selecting high-performing contractors is crucial to the success of construction projects. However, the industry lacks a systematic and purpose-driven method of assessing contractors’ performance using objective metrics. Furthermore, contractors do not have a systematic way to gauge their own performance in the pursuit of continuous improvement. Although there are numerous approaches to the measurement of contractors’ performance, the literature suggests that most are complicated and highly dependent on data that are difficult to attain. The research presented in this thesis addresses this knowledge gap by creating a model for predicting construction contractors’ performance based on directly attributable measures that are quantitatively measurable and easily accessible. The findings of this research make a number of contributions to theory and practice. The developed performance model—the Contractors’ Performance Index (CPIx) provides a performance score based on seven non-price CMoPs. As the CPIx is based on factors that are within the control of the contractor, it provides a fair and independent assessment of performance that is not influenced by other factors. In an industry significantly driven by pricebased decisions that are solely based on non-price measures, the CPIx shifts the focus towards other aspects such as quality, health and safety, sustainability and productivity when evaluating performance, leaving price based measures for commercial considerations. Contractors can use the CPIx to self-evaluate their levels of project and organisational performance. If implemented as a sector-based performance evaluator, it can then be used to develop industry benchmarks for different categories of construction. The CPIx is presented as a prototype mobile application that can be conveniently used by various stakeholders to track performance within the construction industry
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