Event-based media monitoring methodology for Human Rights Watch

Executive Summary This report, prepared by a team of researchers from the University of Minnesota for Human Rights Watch (HRW), investigates the use of event-based media monitoring (EMM) to review its application, identify its strengths and weaknesses, and offer suggestions on how HRW can better utilize EMM in its own work. Media monitoring systems include both human-operated (manual) and automated systems, both of which we review throughout the report. The process begins with the selection of news sources, proceeds to the development of a coding manual (for manual searches) or “dictionary” (for automated searches), continues with gathering data, and concludes with the coding of news stories. EMM enables the near real-time tracking of events reported by the media, allowing researchers to get a sense of the scope of and trends in an event, but there are limits to what EMM can accomplish on its own. The media will only cover a portion of a given event, so information will always be missing from EMM data. EMM also introduces research biases of various kinds; mitigating these biases requires careful selection of media sources and clearly defined coding manuals or dictionaries. In manual EMM, coding the gathered data requires human researchers to apply codebook rules in order to collect consistent data from each story they read. In automated EMM, computers apply the dictionary directly to the news stories, automatically picking up the desired information. There are trade-offs in each system. Automated EMM can code stories far more quickly, but the software may incorrectly code stories, requiring manual corrections. Conversely, manual EMM allows for a more nuanced analysis, but the investment of time and effort may diminish the tool’s utility. We believe that both manual and automated EMM, when deployed correctly, can effectively support human rights research and advocacy

Efficient data representation for XML in peer-based systems

Purpose - New directions in the provision of end-user computing experiences mean that the best way to share data between small mobile computing devices needs to be determined. Partitioning large structures so that they can be shared efficiently provides a basis for data-intensive applications on such platforms. The partitioned structure can be compressed using dictionary-based approaches and then directly queried without firstly decompressing the whole structure. Design/methodology/approach - The paper describes an architecture for partitioning XML into structural and dictionary elements and the subsequent manipulation of the dictionary elements to make the best use of available space. Findings - The results indicate that considerable savings are available by removing duplicate dictionaries. The paper also identifies the most effective strategy for defining dictionary scope. Research limitations/implications - This evaluation is based on a range of benchmark XML structures and the approach to minimising dictionary size shows benefit in the majority of these. Where structures are small and regular, the benefits of efficient dictionary representation are lost. The authors' future research now focuses on heuristics for further partitioning of structural elements. Practical implications - Mobile applications that need access to large data collections will benefit from the findings of this research. Traditional client/server architectures are not suited to dealing with high volume demands from a multitude of small mobile devices. Peer data sharing provides a more scalable solution and the experiments that the paper describes demonstrate the most effective way of sharing data in this context. Social implications - Many services are available via smartphone devices but users are wary of exploiting the full potential because of the need to conserve battery power. The approach mitigates this challenge and consequently expands the potential for users to benefit from mobile information systems. This will have impact in areas such as advertising, entertainment and education but will depend on the acceptability of file sharing being extended from the desktop to the mobile environment. Originality/value - The original work characterises the most effective way of sharing large data sets between small mobile devices. This will save battery power on devices such as smartphones, thus providing benefits to users of such devices

A fuzzy-based approach for classifying students' emotional states in online collaborative work

(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Emotion awareness is becoming a key aspect in collaborative work at academia, enterprises and organizations that use collaborative group work in their activity. Due to pervasiveness of ICT's, most of collaboration can be performed through communication media channels such as discussion forums, social networks, etc. The emotive state of the users while they carry out their activity such as collaborative learning at Universities or project work at enterprises and organizations influences very much their performance and can actually determine the final learning or project outcome. Therefore, monitoring the users' emotive states and using that information for providing feedback and scaffolding is crucial. To this end, automated analysis over data collected from communication channels is a useful source. In this paper, we propose an approach to process such collected data in order to classify and assess emotional states of involved users and provide them feedback accordingly to their emotive states. In order to achieve this, a fuzzy approach is used to build the emotive classification system, which is fed with data from ANEW dictionary, whose words are bound to emotional weights and these, in turn, are used to map Fuzzy sets in our proposal. The proposed fuzzy-based system has been evaluated using real data from collaborative learning courses in an academic context.Peer ReviewedPostprint (author's final draft

Computerization of African languages-French dictionaries

This paper relates work done during the DiLAF project. It consists in converting 5 bilingual African language-French dictionaries originally in Word format into XML following the LMF model. The languages processed are Bambara, Hausa, Kanuri, Tamajaq and Songhai-zarma, still considered as under-resourced languages concerning Natural Language Processing tools. Once converted, the dictionaries are available online on the Jibiki platform for lookup and modification. The DiLAF project is first presented. A description of each dictionary follows. Then, the conversion methodology from .doc format to XML files is presented. A specific point on the usage of Unicode follows. Then, each step of the conversion into XML and LMF is detailed. The last part presents the Jibiki lexical resources management platform used for the project.Comment: 8 page

Re-imagining French lexicography: The dictionnaire vivant de la langue française

The Dictionnaire vivant de la langue française (DVLF), developed by The ARTFL Project at the University of Chicago, represents an experimental, interactive, and community-based approach to French lexicography. The DVLF enables broad public access to a wide variety of linguistic tools and resources, with the goal of changing user interaction with dictionaries and providing better descriptions of emergent word use. In this article we describe the history of the DVLF and provide a survey of similar community-oriented electronic dictionaries. We then proceed to a presentation of the dictionary’s many features, including the variety of its definitions and mechanisms for user interaction. The article concludes with a discussion of ARTFL’s plans for the future developement of the DVLF