Tell Me Your Story and I Will Tell Your Sales: A Topic Model Analysis of Narrative Style and Firm Performance on Etsy

Strategy scholars have widely recognized the central role that narratives play in the construction of organizational identities. Moreover, storytelling is an important strategic asset that firms can leverage to inspire employees, excite investors and engage customers' attention. This chapter illustrates how advancements in computational linguistic may offer opportunities to analyze the stylistic elements that make a story more convincing. Specifically, we use a topic model to examine how narrative conventionality influences the performance of 78,758 craftsmen selling their handmade items in the digital marketplace of Etsy. Our findings provide empirical evidence that effective narratives display enough conventional features to align with audience expectations, yet preserve some uniqueness to pique audience interest. By elucidating our approach, we hope to stimulate further research at the interface of style, language and strategy

Cognitive Translation Studies. Models and methods at the cutting edge

open2siThis work was partially supported by National Social Science Foundation of China (grant number 17BYY048).Several indicators seem to suggest that, through nearly six decades of development, Cognitive Translation Studies (CTS) may be taking shape as an autonomous field of study. The main challenges ahead seem to be building sounder theoretical models and carrying out more rigorous methodological scrutiny. These two strands converge as central themes in the 11 contributions to this special issue of LANS-TTS. To provide a context for theoretical modelling and to frame critical discussions of the methods included in this volume, we first trace the present landscape of CTS and how it evolved so as to test Holmes’ criteria for disciplines: founding new channels of communication and sharing a “disciplinary utopia”. The contributions are arranged into four thematic categories as applied to CTS, namely, scientometrics, framing or reframing our field, the reliability and validity of popular research methods, and new methods or novel approaches. This article closes with a call to reflect on some fundamental issues on the next steps of humankind regarding communication, with ever-growing societal demands and expectations that call for refreshing our notions of translation in the context of increasingly diversified forms of multilectal mediated communication.openMunoz Martín, Ricardo -Xiao, KairongMunoz Martín, Ricardo -Xiao, Kairon

Linguistic Threat Assessment: Understanding Targeted Violence through Computational Linguistics

Language alluding to possible violence is widespread online, and security professionals are increasingly faced with the issue of understanding and mitigating this phenomenon. The volume of extremist and violent online data presents a workload that is unmanageable for traditional, manual threat assessment. Computational linguistics may be of particular relevance to understanding threats of grievance-fuelled targeted violence on a large scale. This thesis seeks to advance knowledge on the possibilities and pitfalls of threat assessment through automated linguistic analysis. Based on in-depth interviews with expert threat assessment practitioners, three areas of language are identified which can be leveraged for automation of threat assessment, namely, linguistic content, style, and trajectories. Implementations of each area are demonstrated in three subsequent quantitative chapters. First, linguistic content is utilised to develop the Grievance Dictionary, a psycholinguistic dictionary aimed at measuring concepts related to grievance-fuelled violence in text. Thereafter, linguistic content is supplemented with measures of linguistic style in order to examine the feasibility of author profiling (determining gender, age, and personality) in abusive texts. Lastly, linguistic trajectories are measured over time in order to assess the effect of an external event on an extremist movement. Collectively, the chapters in this thesis demonstrate that linguistic automation of threat assessment is indeed possible. The concluding chapter describes the limitations of the proposed approaches and illustrates where future potential lies to improve automated linguistic threat assessment. Ideally, developers of computational implementations for threat assessment strive for explainability and transparency. Furthermore, it is argued that computational linguistics holds particular promise for large-scale measurement of grievance-fuelled language, but is perhaps less suited to prediction of actual violent behaviour. Lastly, researchers and practitioners involved in threat assessment are urged to collaboratively and critically evaluate novel computational tools which may emerge in the future

Advancing the development and use of climate-change scenarios : A multi-scale analysis to explore socio-economic European futures

Climate change is one of the greatest challenges of our time and requires unprecedented changes to mitigate greenhouse gas emissions and adapt to climate-change impacts. Different viewpoints and definitions are used by scientists, decision makers and stakeholders to meaning of this challenge. The complexity of this diversity is amplified by the lack of a clear goal and methodology for the exploration of alternative futures in the form of future climate-change scenarios. Such scenarios need, at the same time, to be scientifically credible (credibility) and to reflect different viewpoints (legitimacy) in order to be generalised enough while representing contextual diversity (consistency) to be relevant for decision-making (salience). This thesis develops and analyse European and Central Asian socio-economic scenarios based on the global Shared Socio-economic Pathways (SSPs) to evaluate their credibility, legitimacy, consistency and relevance, with novel analytical methodologies. State-of-the-art scenario methodologies are framed on grounds of the objectives (exploratory and normative) and their links across scales (tight and loose links) and types (qualitative and quantitative). The first methodology is based on a fuzzy-set methodology to link qualitative (narratives) and quantitative (input variables to integrated assessment modelling) scenarios by assessing the different uncertainties resulting from their inherent complexities. In the second and third methodologies, a quantitative pan-European urbanisation model, stakeholder-led narratives and a qualitative concept of archetype are used discuss both the quantitative and qualitative scalability of the scenarios in a multi-scale approach. The fourth methodology combines a capital-capacities framework to link the goal of exploratory scenarios in relation to their relevance to decision-making by assessing their potential to achieve a (normative) desirable future. Overall, results suggest that linking directly the uncertainties contributes to more transparent qualitative and quantitative conversion and therefore yield more credible scenarios. When analysed across scales, global and European scenarios are consistent with both downscaled scenarios and local stakeholder-led narratives contribute to the creation of holistic and more legitimate scenarios. However, important divergences have emerged too. For instance, the scenario with high challenges to mitigation and low challenges to adaptation (SSP5) varies hugely across the European continent. The local versions of SSP5 tend to diverge from the global archetype more than the other SSPs. This divergence reflects different worldviews that challenge state-of-the-art knowledge and can ultimately question the role of global scenarios in guiding local scenario versions with a nested approach. I recommend the role of both narratives and quantifications to be equally important in capturing different uncertainties, stakes and worldviews, as well as a reframing of SSP uncertainty space as one of challenges to societal transformation, rather than one of challenges to mitigation and adaptation.</p

Making Machines Learn. Applications of Cultural Analytics to the Humanities

The digitization of several million books by Google in 2011 meant the popularization of a new kind of humanities research powered by the treatment of cultural objects as data. Culturomics, as it is called, was born, and other initiatives resonated with such a methodological approach, as is the case with the recently formed Digital Humanities or Cultural Analytics. Intrinsically, these new quantitative approaches to culture all borrow from techniques and methods developed under the wing of the exact sciences, such as computer science, machine learning or statistics. There are numerous examples of studies that take advantage of the possibilities that treating objects as data has to offer for the understanding of the human. This new data science that is now applied to the current trends in culture can also be replicated to study more traditional humanities. Led by proper intellectual inquiry, an adequate use of technology may bring answers to questions intractable by other means, or add evidence to long held assumptions based on a canon built from few examples. This dissertation argues in favor of such approach. Three different case studies are considered. First, in the more general sense of the big and smart data, we collected and analyzed more than 120,000 pictures of paintings from all periods of art history, to gain a clear insight on how the beauty of depicted faces, in the framework of neuroscience and evolutionary theory, has changed over time. A second study covers the nuances of modes of emotions employed by the Spanish Golden Age playwright Calderón de la Barca to empathize with his audience. By means of sentiment analysis, a technique strongly supported by machine learning, we shed some light into the different fictional characters, and how they interact and convey messages otherwise invisible to the public. The last case is a study of non-traditional authorship attribution techniques applied to the forefather of the modern novel, the Lazarillo de Tormes. In the end, we conclude that the successful application of cultural analytics and computer science techniques to traditional humanistic endeavours has been enriching and validating

A systematic survey of online data mining technology intended for law enforcement

As an increasing amount of crime takes on a digital aspect, law enforcement bodies must tackle an online environment generating huge volumes of data. With manual inspections becoming increasingly infeasible, law enforcement bodies are optimising online investigations through data-mining technologies. Such technologies must be well designed and rigorously grounded, yet no survey of the online data-mining literature exists which examines their techniques, applications and rigour. This article remedies this gap through a systematic mapping study describing online data-mining literature which visibly targets law enforcement applications, using evidence-based practices in survey making to produce a replicable analysis which can be methodologically examined for deficiencies

Entrepreneurial Literary Theory: A Debate on Research and the Future of Academia

Across the world at present, researchers and teachers are being exhorted to become entrepreneurial. Universities are being restructured accordingly. The debate presented in this book considers what that involves and portends for academia. Literary studies are often regarded as the most resistant to – unfit for – entrepreneurial purposes. Literary research is therefore taken as a baseline for this debate. The uneasy place of literary research within profit-driven academia is revealing of the prevailing conditions for scholarship in all areas. Questions that are raised and discussed here include: What does doing research for the public good mean? What is the relationship between profits and benefits from research? What are applied and basic research? Are concepts of academic freedom and disinterestedness meaningful? What is the relationship between corporate and academic research? Are skills and knowledge different? Can pursuits like close reading and text interpretation be made profitable? What is literary value and how can it be measured? Can the literary system be modelled to profitable ends? Can university teaching be automatized? What are the differences between a standard publication agreement and a scholarly publication agreement? How can digital and open-access academic publication be made profitable? Does the academic monograph have a future? What sorts of knowledge and skills inform entrepreneurial leadership

Moving the financial accounting research front forward: the UK contribution

The purpose of this paper is to review the recent UK contribution to the field of financial accounting research, set against the backdrop of the global (mainly US) research effort. A systematic overview of recent research in the field is presented, based upon an analysis of 261 articles published between 1998 and 2002 in seven general, non-US journals. These are the journals that UK academics publish in most frequently and 115 of the articles are UK-authored. It is found that the research areas of MBAR and disclosure currently dominate conventional financial accounting research. The comparison of findings across institutional settings offers fruitful lines of inquiry for research within these main areas (i.e. studies of value relevance, analysts' forecasts, voluntary disclosure and earnings management). While most research is seen to follow the highly quantitative, economics-based US tradition, a significant amount of UK research adopts a more qualitative approach, and distinctive UK contributions are evident in a number of areas (in particular, the disclosure process and corporate social reporting). There are signs that UK researchers are helping researchers in other countries contribute to the global body of scholarly knowledge