“I can haz emoshuns?”: understanding anthropomorphosis of cats among internet users

The attribution of human-like traits to non-human animals, termed anthropomorphism, can lead to misunderstandings of animal behaviour, which can result in risks to both human and animal wellbeing and welfare. In this paper, we, during an inter-disciplinary collaboration between social computing and animal behaviour researchers, investigated whether a simple image-tagging application could improve the understanding of how people ascribe intentions and emotions to the behaviour of their domestic cats. A web-based application, Tagpuss, was developed to present casual users with photographs drawn from a database of 1631 images of domestic cats and asked them to ascribe an emotion to the cat portrayed in the image. Over five thousand people actively participated in the study in the space of four weeks, generating over 50,000 tags. Results indicate Tagpuss can be used to identify cat behaviours that lay-people find difficult to distinguish. This highlights further expert scientific exploration that focuses on educating cat owners to identify possible problems with their cat’s welfare

Semantic Stability in Social Tagging Streams

One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language streams alone

Semantic Tagging on Historical Maps

Tags assigned by users to shared content can be ambiguous. As a possible solution, we propose semantic tagging as a collaborative process in which a user selects and associates Web resources drawn from a knowledge context. We applied this general technique in the specific context of online historical maps and allowed users to annotate and tag them. To study the effects of semantic tagging on tag production, the types and categories of obtained tags, and user task load, we conducted an in-lab within-subject experiment with 24 participants who annotated and tagged two distinct maps. We found that the semantic tagging implementation does not affect these parameters, while providing tagging relationships to well-defined concept definitions. Compared to label-based tagging, our technique also gathers positive and negative tagging relationships. We believe that our findings carry implications for designers who want to adopt semantic tagging in other contexts and systems on the Web.Comment: 10 page

Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes

In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10

Folksonomy: the New Way to Serendipity

Folksonomy expands the collaborative process by allowing contributors to index content. It rests on three powerful properties: the absence of a prior taxonomy, multi-indexation and the absence of thesaurus. It concerns a more exploratory search than an entry in a search engine. Its original relationship-based structure (the three-way relationship between users, content and tags) means that folksonomy allows various modalities of curious explorations: a cultural exploration and a social exploration. The paper has two goals. Firstly, it tries to draw a general picture of the various folksonomy websites. Secundly, since labelling lacks any standardisation, folksonomies are often under threat of invasion by noise. This paper consequently tries to explore the different possible ways of regulating the self-generated indexation process.taxonomy; indexation; innovation and user-created content

Enriching videos with light semantics

This paper describes an ongoing prototypical framework to annotate and retrieve web videos with light semantics. The proposed framework reuses many existing vocabularies along with a video model. The knowledge is captured from three different information spaces (media content, context, document). We also describe ways to extract the semantic content descriptions from the existing usergenerated content using multiple approaches of linguistic processing and Named Entity Recognition, which are later identified with DBpedia resources to establish meanings for the tags. Finally, the implemented prototype is described with multiple search interfaces and retrieval processes. Evaluation on semantic enrichment shows a considerable (50% of videos) improvement in content description

Enabling Semantics-Aware Collaborative Tagging and Social Search in an Open Interoperable Tagosphere

To make the most of a global network effect and to search and filter the Long Tail, a collaborative tagging approach to social search should be based on the global activity of tagging, rating and filtering. We take a further step towards this objective by proposing a shared conceptualization of both the activity of tagging and the organization of the tagosphere in which tagging takes place. We also put forward the necessary data standards to interoperate at both data format and semantic levels. We highlight how this conceptualization makes provision for attaching identity and meaning to tags and tag categorization through a Wikipedia-based collaborative framework. Used together, these concepts are a useful and agile means of unambiguously defining terms used during tagging, and of clarifying any vague search terms. This improves search results in terms of recall and precision, and represents an innovative means of semantics-aware collaborative filtering and content ranking

Business Domain Modelling using an Integrated Framework

This paper presents an application of a “Systematic Soft Domain Driven Design Framework” as a soft systems approach to domain-driven design of information systems development. The framework combining techniques from Soft Systems Methodology (SSM), the Unified Modelling Language (UML), and an implementation pattern known as “Naked Objects”. This framework have been used in action research projects that have involved the investigation and modelling of business processes using object-oriented domain models and the implementation of software systems based on those domain models. Within this framework, Soft Systems Methodology (SSM) is used as a guiding methodology to explore the problem situation and to develop the domain model using UML for the given business domain. The framework is proposed and evaluated in our previous works, and a real case study “Information Retrieval System for academic research” is used, in this paper, to show further practice and evaluation of the framework in different business domain. We argue that there are advantages from combining and using techniques from different methodologies in this way for business domain modelling. The framework is overviewed and justified as multimethodology using Mingers multimethodology ideas