Quantity versus Quality: The Impact of Environmental Disclosures on the reputations of UK plcs

The theoretical framework of this paper integrates quality-signalling theory and the resource based view of the firm to test the differential effects of the quantity and quality of environmental disclosures on the firm’s environmental reputation. Uniquely, the study uses a quality-adjusted method of content analysis, so that sentences are not merely counted but also weighted to reflect their likely significance. Investments in research and development and diversification, as potential methods of enhancing of environmental reputation, are also considered. In doing so the paper complements and extends the work of Toms (2002). The results confirm the framework and models tested in the original paper on more recent data and also suggest that quality of environmental disclosure rather than mere quantity has a stronger effect on the creation of environmental reputation amongst executive and investor stakeholder groups. Research and development expenditure, and under certain circumstances, diversification, also add to reputation

People on Drugs: Credibility of User Statements in Health Communities

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

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

On the nobility of urban notables

The claim to be a descendant of the Prophet Muhammad (teseyyüd) was a widespread phenomenon that afflicted the Ottoman Empire from the sixteenth century onwards. Historians of the Arab lands were the first to observe the unnatural increase in the number of sadat/ashraf, particularly in the eighteenth century. They also observed a high degree of correlation between wealth, notability and Muham madan pedigree. It has been noted, for example, that in eighteenth-century Da mascus, the average wealth of the ashraf was three times that of the commoners, and most of them “were members of otherwise prominent families”. In Aleppo, they constituted the elite of the civilian population with 58% of the notable families counted among their ranks according to one study. By the end of the eighteenth century, these families held more than 30% of the lifetime tax-farms. Although there is reason to expect electiv

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

DTRM: A new reputation mechanism to enhance data trustworthiness for high-performance cloud computing

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Cloud computing and the mobile Internet have been the two most influential information technology revolutions, which intersect in mobile cloud computing (MCC). The burgeoning MCC enables the large-scale collection and processing of big data, which demand trusted, authentic, and accurate data to ensure an important but often overlooked aspect of big data - data veracity. Troublesome internal attacks launched by internal malicious users is one key problem that reduces data veracity and remains difficult to handle. To enhance data veracity and thus improve the performance of big data computing in MCC, this paper proposes a Data Trustworthiness enhanced Reputation Mechanism (DTRM) which can be used to defend against internal attacks. In the DTRM, the sensitivity-level based data category, Metagraph theory based user group division, and reputation transferring methods are integrated into the reputation query and evaluation process. The extensive simulation results based on real datasets show that the DTRM outperforms existing classic reputation mechanisms under bad mouthing attacks and mobile attacks.This work was supported by the National Natural Science Foundation of China (61602360, 61772008, 61472121), the Pilot Project of Fujian Province (formal industry key project) (2016Y0031), the Foundation of Science and Technology on Information Assurance Laboratory (KJ-14-109) and the Fujian Provincial Key Lab of Network Security and Cryptology Research Fund (15012)

Disclosure measurement in the empirical accounting literature: A review article

This is the first study to provide an extensive and critical review of different techniques used in the empirical accounting literature to measure disclosure. The purpose is to help future researchers to identify exemplars and to select suitable techniques or to develop their own techniques. It also provides in depth discussion of current measurement issues related to disclosure and identifies gaps in the current literature which future research may aim to cover

Enforcement and Environmental Compliance: A Statistical Analysis of the Pulp and Paper Industry

This paper explores empirically the impact of changes of enforcement efforts on environmental compliance. Our strategy is to link observed fines and other enforcement actions to subsequent compliance behavior. We find that, on the margin, the impact of a fine for water pollutant violations is about a two-thirds reduction in the statewide violation rate in the year following a fine. This surprisingly large result obtains through the regulator’s enhanced reputation. We find that the deterrence impact on other firms in a state is almost as strong as the impact on the sanctioned firm. In contrast to fines, non-monetary sanctions contribute no detected impact on compliance.Fines, Reputation, Pollution, Compliance, Enforcement