Digital business discourse

Digital Business Discourse offers a distinctively language- and discourse-centered approach to digitally mediated business and professional communication, providing a timely and comprehensive assessment of the current digital communication practices of today's organisations and workplaces. It is the first dedicated publication to address how computer-mediated communication technologies affect institutional discourse practices, bringing together scholarship from a range of disciplinary backgrounds, including organisational and management studies, rhetorical and communication studies, communication training and discourse analysis. Covering a wide spectrum of communication technologies, such as email, instant messaging, message boards, Twitter, corporate blogs and consumer reviews, the chapters gather research drawing on empirical data from real professional contexts. In this way, the book contributes to both academic scholarship and business communication training, enabling researchers, trainers and practitioners to deepen their understanding of the impact of new communication technologies on professional and corporate communication practices.

Introduction: business communication in the digital age – fresh perspectives

Digital Business Discourse offers a distinctively language- and discourse-centered approach to digitally mediated business and professional communication, providing a timely and comprehensive assessment of the current digital communication practices of today's organisations and workplaces. It is the first dedicated publication to address how computer-mediated communication technologies affect institutional discourse practices, bringing together scholarship from a range of disciplinary backgrounds, including organisational and management studies, rhetorical and communication studies, communication training and discourse analysis. Covering a wide spectrum of communication technologies, such as email, instant messaging, message boards, Twitter, corporate blogs and consumer reviews, the chapters gather research drawing on empirical data from real professional contexts. In this way, the book contributes to both academic scholarship and business communication training, enabling researchers, trainers and practitioners to deepen their understanding of the impact of new communication technologies on professional and corporate communication practices.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

A seismic shift? An evaluation of the impact of new media on perceptions of freedom of expression and privacy

New media platforms have changed the media landscape forever, as they have altered our perceptions of the limits of communication, and reception of information. Platforms such as Facebook, Twitter and WhatsApp enable individuals to circumvent the traditional mass media, converging audience and producer to create millions of ‘citizen journalists’. This new breed of journalist uses these platforms as a way of, not only receiving news, but of instantaneously, and often spontaneously, expressing opinions and venting and sharing emotions, thoughts and feelings. They are liberated from cultural and physical restraints, such as time, space and location, and they are not constrained by factors that impact upon the traditional media, such as editorial control, owner or political bias or the pressures of generating commercial revenue. A consequence of the way in which these platforms have become ingrained within our social culture is that habits, conventions and social norms, that were once informal and transitory manifestations of social life, are now infused within their use. What were casual and ephemeral actions and/or acts of expression, such as conversing with friends or colleagues or swapping/displaying pictures, or exchanging thoughts that were once kept private, or maybe shared with a select few, have now become formalised and potentially permanent, on view for the world to see. Incidentally, ‘traditional’ journalists and media outlets are also utilising new media, as it allows them to react, and disseminate news, instantaneously, within a hyper-competitive marketplace. However, in a world where we are saturated, not only by citizen journalists, but by traditional media outlets, offering access to news and opinion twenty-four hours a day, via multiple new media platforms, there is increased pressure to ‘break’ news fast and first. This paper will argue that new media, and the culture and environment it has created, for citizen journalists, traditional journalists and the media generally, has altered our perceptions of the limits and boundaries of freedom of expression dramatically, and that the corollary to this seismic shift is the impact on the notion of privacy and private life. Consequently, this paper will examine what a reasonable expectation of privacy may now mean, in a new media world.

On-line puzzle game based assessment & training for ADHD

Attention-deficit hyperactivity disorder (ADHD) is the most prevalent and impairing neurodevelopmental disorder, with worldwide estimates of 5.29%. ADHD is clinically characterized by hyperactivity-impulsivity and inattention, with neuropsychological deficits in executive functions, attention, working memory and inhibition. These cognitive processes rely on prefrontal cortex function; cognitive training programs enhance performance of ADHD participants supporting the idea of neuronal plasticity. Here we propose the development of an on-line puzzle game based assessment and training tool in which participants must deduce the ‘winning symbol’ out of N distracters. To increase ecological validity of assessments strategically triggered Twitter/Facebook notifications will challenge the ability to ignore distracters. In the UK, significant cost for the disorder on health, social and education services, stand at £23m a year. Thus the potential impact of neuropsychological assessment and training to improve our understanding of the pathophysiology of ADHD, and hence our treatment interventions and patient outcomes, cannot be overstated.

Adverse drug reaction classification with deep neural networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.