Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

Do We Choose What We Desire? – Persuading Citizens to Make Consistent and Sustainable Mobility Decisions

A dilemma in urban mobility with tremendous effects on citizens’ wellbeing is the unconscious antipode between their short- and long-term goals. People do not anticipate all consequences of their modal choices and thus make decisions that might be incoherent with their desires, e.g. taking their own car due to convenience but causing a congested city. Omnipresent Information Systems on smartphones provide the necessary information and coordination capabilities to support people for sustainable and individually coherent mobility decisions on a mass scale. Building upon extant work in travel behavior and social psychology, a framework is proposed to coordinate research efforts in the development of persuading measures for sustainable mobility decisions. This framework accounts for user heterogeneity, motivation and wellbeing as influential dimensions in the mobility decision process. Tied to social influence the derived measures contribute to a behavioral change in people’s mobility behavior leading to a higher wellbeing level in urban areas

Visualization, Feature Selection, Machine Learning: Identifying the Responsible Group for Extreme Acts of Violence

The toll of human casualties and psychological impacts on societies make any study on violent extremism worthwhile, let alone attempting to detect patterns among them. This paper is an effort to predict which violent extremist organization (VEO), among 14 currently active ones throughout the world, is responsible for a violent act based on 14 features, including its human and structural tolls, its target type and value, intelligence, and weapons utilized in the attack. Three main steps in our paper include: 1) the visualization of the violent acts through linear and non-linear dimensionality reduction techniques; 2) sequential forward feature selection based on the generalization accuracy of three machine learning models–decision tree, and linear and nonlinear SVM; and 3) employing multilayer perceptron to predict the VEO based on the selected features of a violent act. Top-ranked selected features were related to the target type and plan and the multilayer perceptron achieved up to 40% test accuracy

A Crowdsourcing Approach to Identify Common Method Bias and Self-Representation

Pertinent questions on the measurement of social indicators are: the verification of data gained online (e.g., controlling for self-representation on social networks), and appropriate uses in community management and policy-making. Across platforms like Facebook, LinkedIn, Twitter, and blogging services, users (sub)consciously represent themselves in a way which is appropriate for their intended audience (Qui et al., 2012; Zhao et al., 2008). However, scholars in the social sciences and computer science have not yet adequately addressed controlling for self-representation, or the propensity to display or censor oneself, in their analyses (Zhao et al., 2008; Das and Kramer, 2013). As such researchers on these platforms risk working with ‘gamified’, socially responding, or online disinhibitive (trolls) personas which goes above and beyond efforts to contain Common Method Biases (CMB) (Linville, 1985; Suler, 2004; Podsakoff et al., 2003). What has not been approached in a systematic way is the verification of such data on offline and actual personality. In this paper, we focus on the alignment of traditional survey methods with unobtrusive methods to gather profile data from online social media via crowdsourcing platforms

Predicting Events Surrounding the Egyptian Revolution of 2011 Using Learning Algorithms on Micro Blog Data

We aim to predict activities of political nature in Egypt which influence or reflect societal-scale behavior and beliefs by using learning algorithms on Twitter data. We focus on capturing domestic events in Egypt from November 2009 to November 2013. To this extent we study underlying communication patterns by evaluating content-based and meta-data information in classification tasks without targeting specific keywords or users. Classification is done using Support Vector Machines (SVM) and Support Distribution Machines (SDM). Latent Dirichlet Allocation (LDA) is used to create content-based input patterns for the classifiers while bags of Twitter meta-information are used with the SDM to classify meta-data features. The experiments reveal that user centric approaches based on metadata can outperform methods employing content-based input despite the use of well established natural language processing algorithms. The results show that distributions over users-centric meta information provides an important signal when detecting and predicting events

FBWatch: Extracting, Analyzing and Visualizing Public Facebook Profiles

An ever-increasing volume of social media data facilitates studies into behavior patterns, consumption habits, and B2B exchanges, so called Big Data. Whilst many tools exist for platforms such as Twitter, there is a noticeable absence of tools for Facebook-based studies that are both scalable and accessible to social scientists. In this paper, we present FBWatch, an open source web application providing the core functionality to fetch public Facebook profiles en masse in their entirety and analyse relationships between profiles both online and offline. We argue that FBWatch is a robust interface for social researchers and business analysts to identify analyze and visualize relationships, discourse and interactions between public Facebook entities and their audiences

Applying Well-being Assessment for Service Design

Service design is transformative when it has a measurable, even optimizing, positive affect on human well-being. Any prospect for such felicitous outcomes, however, requires accurate assessment or measurement of well-being in and for target populations. Such assessment raises two immediate issues: conceptualization (How should well-being be conceptually operationalized?) and measurement (Given an operationalization of well-being, how can it be measured?). We begin to explore and address both questions in this paper by reviewing existing conceptualizations of well-being and then by describing the relevance of well-being measurement (and it methodologies) which are presently available

Is Quality Control Pointless?

Intrinsic to the transition towards, and necessary for the success of digital platforms as a service (at scale) is the notion of human computation. Going beyond ‘the wisdom of the crowd’, human computation is the engine that powers platforms and services that are now ubiquitous like Duolingo and Wikipedia. In spite of increasing research and population interest, several issues remain open and in debate on large-scale human computation projects. Quality control is first among these discussions. We conducted an experiment with three different tasks of varying complexity and five different methods to distinguish and protect against constantly under-performing contributors. We illustrate that minimal quality control is enough to repel constantly under-performing contributors and that this effect is constant across tasks of varying complexity

An Extended Conceptual Framework for Transformative Service Research

Transformative service research (TSR), a recently-envisioned branch of service science, is about understanding connections between service offerings and well-being. It has at the core of its conceptualization the goal of improving the well-being of individuals. A founding statement characterizes TSR as: “the integration of consumer and service research that centers on creating uplifting changes and improvements in the well-being of consumer entities: individuals (consumers and employees), communities and the ecosystem” (Anderson et al. 2013). It is also clear that service touches innumerable aspects of daily life. It is then natural that the field of service science explores mitigation of negative and enhancement of positive service experiences beyond the value co-creation and customer satisfaction paradigms. This is well summed up in the conversation between the switch from goods-dominant to service-dominant logic (Vargo et al. 2008)

Towards a Requirement Framework for Online Participation Platforms

Online participation platforms (OPPs) are frequently used by public institutions to involve citizens in political opinion forming and decision making. A literature re-view reveals different approaches to evaluate these OPPs. These approaches focus only on partial requirements of participation processes. In this research in progress, we develop and pretest an interdisciplinary literature-based requirement frame-work. It includes the categories usability, security, information, transparency, inte-gration, and mobilisation. Our aim is to close the research gap of a context-specific analysis and evaluation of OPPs