Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p

Commercialised dialogue and Web 2.0 interactivity : Characterising discourses in digital advertising environments

This thesis aims to provide an original context for the emergent use of Web 2.0 technologies by brands and their communication agents (advertising, PR and marketing) as they engage consumers in (a branded form of) dialogue. This is achieved by exploring the content and style of brand-consumer communications in Web 2.0 platforms, by appraising advertising discourses in collaborative and interactive environment of Twitter. This study focuses in particular on the use of language and the role that other communicative modes play in Web 2.0-mediated interactions and the possible implications they might have on brand-consumer power relations. This thesis adopts a critical inter-disciplinary approach, and is designed to inform the emerging field of digital commercial communications. More specifically, by applying social and cultural theories of new media with the social Web, this study sets out to contribute to emerging literature and debates on the socio-economic implications of Web 2.0 communications in the context of advertising. Critical theories of advertising and new media have been utilised to shape a framework for analysing communications in collaborative and often interactive digital advertising settings. This, and a body of primary research through first-hand interviews, plus analysis of exemplar of Web 2.0-mediated brand-consumer communications, enables me to consider more broadly the ways in which capitalism has been repositioned within the new digital environment. To achieve this, this study has appropriated two research methods in it’s handling of primary evidence. The first part of analysis appraises eight semi-structured interviews I conducted with digital strategy makers and ‘brand ambassadors’ working within the contemporary international advertising industry, acting on behalf of multinational brands. The second part analyses the content of brand-consumer communications within Web 2.0 platforms, notably Twitter, through four different sectors spanning service, product and cause sectors. Cases are of Starbucks Coffee (fast moving consumer goods), Dell Computers (IT sector), Burberry (luxury fashion) and Yes Scotland (a political cause). The overarching aim is to assess product or service-driven digital advertising strategies that have most effectively exploited (or best lend themselves to) social Web platforms to leverage their ideology and generate social supports. The results of my analysis suggest that although Web 2.0-mediated communications between brands and consumers exhibit some characteristics of participatory culture, the actual nature of the conversational qualities and the types of interaction spans a much wider spectrum. Some discourses are in fact monologue, while others contain consumer-generated responsive dialogue and more proportionate mutual discourse. The latter ultimately contributes in co-creating and shaping discourses that reinforce and stabilise existing hierarchical relations between producers and consumer

On the application of artificial intelligence and human computation to the automation of agile software task effort estimation

Software effort estimation (SEE), as part of the wider project planning and product road mapping process, occurs throughout a software development life cycle. A variety of effort estimation methods have been proposed in the literature, including algorithmic methods, expert based methods, and more recently, methods based on techniques drawn from machine learning and natural language processing. In general, the consensus in the literature is that expert-based methods such as Planning Poker are more reliable than automated effort estimation. However, these methods are labour intensive and difficult to scale to large-scale projects. To address this limitation, this thesis investigates the feasibility of using human computation techniques to coordinate crowds of inexpert workers to predict expert-comparable effort estimates for a given software development task. The research followed an empirical methodology and used four different methods: literature review, replication, a series of laboratory experiments, and ethnography. The literature uncovered the lack of suitable datasets that include the attributes of descriptive text (corpus), actual cost, and expert estimates for a given software development task. Thus, a new dataset was developed to meet the necessary requirements. Next, effort estimation based on recent natural language processing advancements was evaluated and compared with expert estimates. The results suggest that there was no significant improvement, and the automated approach was still outperformed by expert estimates. Therefore, the feasibility of scaling the Planning Poker effort estimation method by using human computation in a micro-task crowdsourcing environment was explored. A series of pilot experiments were conducted to find the proper design for adapting Planning Poker to a crowd environment. This resulted in designing a new estimation method called Crowd Planning Poker (CPP). The pilot experiments revealed that a significant proportion of the crowd submitted poor quality assignments. Therefore, an approach to actively managing the quality of SEE work was proposed and evaluated before being integrated into the CPP method. A substantial overall evaluation was then conducted. The results demonstrated that crowd workers were able to discriminate between tasks of varying complexity and produce estimates that were comparable with those of experts and at substantially reduced cost compared with small teams of domain experts. It was further noted in the experiments that crowd workers provide useful insights as to the resolution of the task. Therefore, as a final step, fine-grained details about crowd workers’ behaviour, including actions taken and artifacts reviewed, were used in an ethnographic study to understand how crowd effort estimation takes place in a crowd. Four persona archetypes were developed to describe the crowd behaviours, and the results of the behaviour analysis were confirmed by surveying the crowd workers

Suffolk University Academic Catalog and Student Handbook, College of Arts and Sciences and Sawyer Business School, 2020-2021

This catalog contains information for both the undergraduate and graduate programs. The student handbook is included here as a separate pdf document. Please contact the Archives if you need assistance navigating this catalog or finding information on policies, degree requirements, or course descriptions.https://dc.suffolk.edu/cassbs-catalogs/1182/thumbnail.jp