Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks

The Dynamics of Influencer Marketing

YouTube, Instagram, Facebook, Vimeo, Twitter, etc. have their own logics, dynamics and different audiences. This book analyses how the users of these social networks, especially those of YouTube and Instagram, become content prescribers, opinion leaders and, by extension, people of influence. What influence capacity do they have? Why are intimate or personal aspects shared with unknown people? Who are the big beneficiaries? How much is vanity and how much altruism? What business is behind these social networks? What dangers do they contain? What volume of business can we estimate they generate? How are they transforming cultural industries? What legislation is applied? How does the legislation affect these communications when they are sponsored? Is the privacy of users violated with the data obtained? Who is the owner of the content? Are they to blame for ""fake news""? In this changing, challenging and intriguing environment, The Dynamics of Influencer Marketing discusses all of these questions and more. Considering this complexity from different perspectives: technological, economic, sociological, psychological and legal, the book combines the visions of several experts from the academic world and provides a structured framework with a wide approach to understand the new era of influencing, including the dark sides of it. It will be of direct interest to marketing scholars and researchers while also relevant to many other areas affected by the phenomenon of social media influence

Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

The Network of Knowledge approach: improving the science and society dialogue on biodiversity and ecosystem services in Europe

The absence of a good interface between scientific and other knowledge holders and decision-makers in the area of biodiversity and ecosystem services has been recognised for a long time. Despite recent advancements, e.g. with the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), challenges remain, particularly concerning the timely provision of consolidated views from different knowledge domains. To address this challenge, a strong and flexible networking approach is needed across knowledge domains and institutions. Here, we report on a broad consultation process across Europe to develop a Network of Knowledge on biodiversity and ecosystem services (NoK), an approach aiming at (1) organising institutions and knowledge holders in an adaptable and responsive framework and (2) informing decision-makers with timely and accurate biodiversity knowledge. The consultation provided a critical analysis of the needs that should be addressed by a NoK and how it could complement existing European initiatives and institutions at the interface between policy and science. Among other functions, the NoK provides consolidated scientific views on contested topics, identification of research gaps to support relevant policies, and horizon scanning activities to anticipate emerging issues. The NoK includes a capacity building component on interfacing activities and contains mechanisms to ensure its credibility, relevance and legitimacy. Such a network would need to ensure credibility, relevance and legitimacy of its work by maximizing transparency and flexibility of processes, quality of outputs, the link to data and knowledge provision, the motivation of experts for getting involved and sound communication and capacity building

Contemporary Research on Business and Management

This book contains selected papers presented at the 4th International Seminar of Contemporary Research on Business and Management (ISCRBM 2020), which was organized by the Alliance of Indonesian Master of Management Program (APMMI) and held in Surubaya, Indonesia, 25-27 November 2020. It was hosted by the Master of Management Program Indonesia University and co-hosts Airlangga University, Sriwijaya University, Trunojoyo University of Madura, and Telkom University, and supported by Telkom Indonesia and Triputra. The seminar aimed to provide a forum for leading scholars, academics, researchers, and practitioners in business and management area to reflect on current issues, challenges and opportunities, and to share the latest innovative research and best practice. This seminar brought together participants to exchange ideas on the future development of management disciplines: human resources, marketing, operations, finance, strategic management and entrepreneurship

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17