Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground

The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).Peer reviewedPublisher PD

Measuring the impact of research outputs from the Institute for Poverty, Land and Agrarian Studies (PLAAS) on the scholarly domain and in social media, 1995-2015

Scholarly communication has changed with the growth in technology, particularly the internet and the social web. The changes include a broader definition of the scholarly communication format, and the role of social media in the research process, amongst others. This study sought to record the body of work that PLAAS had produced over a 20-year period (1995 to 2015) and to measure its visibility and impact through bibliometrics and altmetrics. It was the first time that such a study had been done. The Web of Science Citation Index and Scopus are two commercial databases that have recently been joined by Google Scholar, the first open database of scholarly items with citation counts based on the entire contents of the World Wide Web. Scopus and Google Scholar were used in this study. Methods used in the study included the compilation of a full bibliographic record of the outputs during that period. Citation analysis and publication counts were conducted, per author, within Scopus and Google Scholar. Altmetric analysis was achieved with the Altmetric Explorer database, and by studying three PLAAS grey literature outputs in more depth for altmetric indicators. The last method used was a small survey based on an online multiple-choice questionnaire of researchers at PLAAS to investigate their attitudes to a selection of the social media platforms commonly used by scholars. The full list of outputs, once compiled, showed a composition of 54% grey literature published by PLAAS and 46% journal articles and monographs. The results showed that bibliometrics, as a purely quantitative indicator, can be useful in measuring the impact of a body of work on the scholarly domain and in this study indicated high publication and citation rates. The authors of the highest number of PLAAS outputs and with the highest citation counts and h-indices, were found to be the same throughout the study. These authors are closely associated with the Institute and have contributed to the good academic reputation of its research. The study was inconclusive with regard to the impact on social media platforms as none of the grey literature from PLAAS had a unique identifier which made it difficult to track; in addition, the use of social media by the Institute and its researchers was intermittent and uneven in covering all the PLAAS-published outputs that were produced. Key recommendations for PLAAS to improve the visibility and impact of their outputs in scholarly and social contexts were to use unique identifiers, to track their social media activity and to keep author profiles up to date. Further use and application of the research design in other research units and departments at UWC will generate results that are useful to research management at UWC

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

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

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

2023 SOARS Conference Program

Program for the 2023 Showcase of Osprey Advancements in Research and Scholarship (SOARS

Discovering and Mitigating Social Data Bias

abstract: Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago. Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect. The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them. The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Can computers foster human users' creativity? Theory and praxis of mixed-initiative co-creativity

This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.peer-reviewe

Can computers foster human users' creativity? Theory and praxis of mixed-initiative co-creativity

This article discusses the impact of artificially intelligent computers to the process of design, play and educational activities. A computational process which has the necessary intelligence and creativity to take a proactive role in such activities can not only support human creativity but also foster it and prompt lateral thinking. The argument is made both from the perspective of human creativity, where the computational input is treated as an external stimulus which triggers re-framing of humans’ routines and mental associations, but also from the perspective of computational creativity where human input and initiative constrains the search space of the algorithm, enabling it to focus on specific possible solutions to a problem rather than globally search for the optimal. The article reviews four mixed-initiative tools (for design and educational play) based on how they contribute to human-machine co-creativity. These paradigms serve different purposes, afford different human interaction methods and incorporate different computationally creative processes. Assessing how co-creativity is facilitated on a per-paradigm basis strengthens the theoretical argument and provides an initial seed for future work in the burgeoning domain of mixed-initiative interaction.peer-reviewe