Online Deception Detection Refueled by Real World Data Collection

The lack of large realistic datasets presents a bottleneck in online deception detection studies. In this paper, we apply a data collection method based on social network analysis to quickly identify high-quality deceptive and truthful online reviews from Amazon. The dataset contains more than 10,000 deceptive reviews and is diverse in product domains and reviewers. Using this dataset, we explore effective general features for online deception detection that perform well across domains. We demonstrate that with generalized features - advertising speak and writing complexity scores - deception detection performance can be further improved by adding additional deceptive reviews from assorted domains in training. Finally, reviewer level evaluation gives an interesting insight into different deceptive reviewers' writing styles.Comment: 10 pages, Accepted to Recent Advances in Natural Language Processing (RANLP) 201

Why You Don’t Get Published: An Editor’s View

The definitive version is available at http://www.wileyonlinelibrary.comThis paper uses content analysis to examine 66 reviews on 33 manuscripts submitted to Accounting and Finance. Selected extracts from reviews are provided to illustrate the issues considered important to reviewers. The main message is that papers need to be work-shopped and more care taken over editorial matters. A checklist for prospective authors is provided

Research Articles in Simplified HTML: a Web-first format for HTML-based scholarly articles

Purpose. This paper introduces the Research Articles in Simplified HTML (or RASH), which is a Web-first format for writing HTML-based scholarly papers; it is accompanied by the RASH Framework, a set of tools for interacting with RASH-based articles. The paper also presents an evaluation that involved authors and reviewers of RASH articles submitted to the SAVE-SD 2015 and SAVE-SD 2016 workshops. Design. RASH has been developed aiming to: be easy to learn and use; share scholarly documents (and embedded semantic annotations) through the Web; support its adoption within the existing publishing workflow. Findings. The evaluation study confirmed that RASH is ready to be adopted in workshops, conferences, and journals and can be quickly learnt by researchers who are familiar with HTML. Research Limitations. The evaluation study also highlighted some issues in the adoption of RASH, and in general of HTML formats, especially by less technically savvy users. Moreover, additional tools are needed, e.g., for enabling additional conversions from/to existing formats such as OpenXML. Practical Implications. RASH (and its Framework) is another step towards enabling the definition of formal representations of the meaning of the content of an article, facilitating its automatic discovery, enabling its linking to semantically related articles, providing access to data within the article in actionable form, and allowing integration of data between papers. Social Implications. RASH addresses the intrinsic needs related to the various users of a scholarly article: researchers (focussing on its content), readers (experiencing new ways for browsing it), citizen scientists (reusing available data formally defined within it through semantic annotations), publishers (using the advantages of new technologies as envisioned by the Semantic Publishing movement). Value. RASH helps authors to focus on the organisation of their texts, supports them in the task of semantically enriching the content of articles, and leaves all the issues about validation, visualisation, conversion, and semantic data extraction to the various tools developed within its Framework

PeerPigeon: A Web Application to Support Generalised Peer Review

Peer Review (also known as Peer Assessment) is an important technique in learning, but can be difficult to support through e-learning due to the complexity and variety of peer review processes. In this paper we present PeerPigeon, a Web 2.0 style application that supports generalised Peer Review by using a canonical model of Peer Review based on a Peer Review Pattern consisting of Peer Review Cycles, each defined in terms of Peer Review Transforms. We also demonstrate how PeerPigeon makes use of a Domain Specific Language based on Ruby to define these plans, and thus cope with the irreducible complexity of the flow of documents around a peer network