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This essay explores a model for inequality in Nigerian rural Hausaland based on fieldwork carried out from 1977 to 1979, with follow-up visits in 1996 and from 1997 to 1998. In the model, rural dif...

Smart Parking schemes cannot succeed without the engagement and support of the drivers who may benefit from their use. This study investigates engagement with a Smart Parking service in the London Borough of Westminster. Factors likely to influence the use of Smart Parking services were identified from a literature review and incorporated into an explanatory model comprising 9 factors connected by 16 hypotheses. To test the model, residents of Westminster and visitors to the area were surveyed, resulting in a total of 212 valid responses. The responses were used to test a structural equation model, using confirmatory factor analysis. The results of the analysis indicated that Awareness of the scheme; Perceived Ease of Use; Perceived Usefulness; Cost saving; Perceived Privacy and Perceived Security all had a direct impact on Usage, with Awareness being the most influential factor. The results also highlighted the fact that, despite efforts by Westminster Council to publicise the scheme, 74% of respondents had little awareness of it, suggesting the need for improved publicity.

Paraphrase types have been proposed by researchers as the paraphrasing mechanisms underlying acts of plagiarism. Synonymous substitution, word reordering and insertion/deletion have been identified as some of the common paraphrasing strategies used by plagiarists. However, similarity reports generated by most plagiarism detection systems provide a similarity score and produce matching sections of text with their possible sources. In this research we propose methods to identify two important paraphrase types – synonymous substitution and word reordering in paraphrased, plagiarised sentence pairs. We propose a three staged approach that uses context matching and pretrained word embeddings for identifying synonymous substitution and word reordering. Our proposed approach indicates that the use of Smith Waterman Algorithm for Plagiarism Detection and ConceptNet Numberbatch pretrained word embeddings produces the best performance in terms of $$\hbox {F}_1$$ F 1 scores. This research can be used to complement similarity reports generated by currently available plagiarism detection systems by incorporating methods to identify paraphrase types for plagiarism detection.

Purpose To design effective task-responsive search systems, sufficient understanding of users’ tasks must be gained and their characteristics described. Although existing multi-dimensional task schemes can be used to describe users’ search and work tasks, they do not take into account the information use environment (IUE) that contextualises the task. The paper aims to discuss these issues. Design/methodology/approach With a focus on English primary schools, in four stages a multi-dimensional task scheme was developed that distinguishes between task characteristics generic to all environments, and those that are specific to schools. In Stage 1, a provisional scheme was developed based upon the existing literature. In the next two stages, through interviews with teachers and observations of school children, the provisional scheme was populated and revised. In Stage 4, whether search tasks with the same information use can be distinguished by their characteristics was examined. Findings Ten generic characteristics were identified (nature of work task, search task originator, search task flexibility, search task doer, search task necessity, task output, search goal, stage in work task, resources and information use) and four characteristics specific to primary schools (curricular area, use in curricular area, planning and location). For the different information uses, some characteristics are more typical than others. Practical implications The resulting scheme, based on children’s real-life information seeking, should be used in the design and evaluation of search systems and digital libraries that support school children. More generally, the scheme can also be used in other environments. Originality/value This is the first study to develop a multi-dimensional task scheme that considers encompasses the IUE.

Museum websites have been designed to provide access for different types of users, such as museum staff, teachers and the general public. Therefore, understanding user needs and demographics is paramount to the provision of user-centred features, services and design. Various approaches exist for studying and grouping users, with a more recent emphasis on data-driven and automated methods. In this paper, we investigate user groups of a large national museum’s website using multivariate analysis and machine learning methods to cluster and categorise users based on an existing user survey. In particular, we apply the methods to the dominant group - general public - and show that sub-groups exist, although they share similarities with clusters for all users. We find that clusters provide better results for categorising users than the self-assigned groups from the survey, potentially helping museums develop new and improved services.

Museums are increasing access to their collections and providing richer user experiences via web-based interfaces. However, they are seeing high numbers of users looking at only one or two pages within 10 s and then leaving. To reduce this rate, a better understanding of the type of user who visits a museum website is required. Existing models for museum website users tend to focus on groups that are readily accessible for study or provide little detail in their definitions of the groups. This paper presents the results of a large-scale user survey for the National Museums Liverpool museum website in which data on a wide range of user characteristics were collected regarding their current visit to provide a better understanding of their motivations, tasks, engagement and domain knowledge. Results show that the frequently understudied general public and non-professional users make up the majority (approximately 77%) of the respondents.

This article proposes that the value of information is a topic worth revisiting in the contemporary era. Although the topic has been of perennial interest to information professionals and others, since at the least the early 1980s, we believe that it is timely to revisit this question in the context of a more connected and networked environment of data, information, and knowledge. The principal argument is that existing models of information exchange and use do not sufficiently take account of the multiplicity of networked users as a source of value, for example, their implicit and explicit interactions with other users, and with the information system. We briefly review existing kinds of value that have been theorized, operationalized, and measured in the information science literature. Principally, these are the notions of information as embedded value; and information and information systems as adding value. To these notions we add the further notion of connected or cocreated value. We conclude our opinion article with a set of questions intended to orient future research into the question of the value of information in the contemporary era.

An increasing number of students are studying abroad requiring that they interact with information in languages other than their mother tongue. The UK in particular has seen a large growth in international students within Higher Education. These nonnative English speaking students present a distinct user group for university information services, such as university libraries. This article presents the findings from an in-depth study to understand differences between the search processes of home and international students. Data were collected using an online survey and diary-interview to capture thoughts and feelings in a more naturalistic way. International students are found to have similar information search processes to those of home students, but sometimes face additional difficulties in assessing search results such as confusion when dealing with differing cultural perspectives. The potential implications for information service providers, particularly university libraries, are discussed, suc...

There have been multiple calls for integrating topics related to fair- ness, accountability, transparency, ethics (FATE) and social justice into Data Science curricula, but little exploration of how this might work in practice. This paper presents the findings of a collabora- tive auto-ethnography (CAE) engaged in by a MSc Data Science teaching team based at University of Sheffield (UK) Information School where FATE/Critical Data Studies (CDS) topics have been a core part of the curriculum since 2015/16. In this paper, we adopt the CAE approach to reflect on our experiences of working at the intersection of disciplines, and our progress and future plans for integrating FATE/CDS into the curriculum. We identify a series of challenges for deeper FATE/CDS integration related to our own competencies and the wider socio-material context of Higher Edu- cation in the UK. We conclude with recommendations for ourselves and the wider FATE/CDS orientated Data Science community

This paper reflects on the ways in which the concepts of agency, time, and morality can be linked together. It does so by considering how the kind of thinking which goes on in social anthropology and economic anthropology can contribute to our understanding of ‘agency’. It then relates the discussion of agency to the concepts of time and morality. The mode of reasoning in social anthropology can be described as cyclical: social theory generates general questions that can be asked of a human society in particular times and places. Those questions stimulate empirical investigation, which results in a descriptive analysis of human social reality – known in social anthropology as ethnography. In turn, ethnography often challenges existing paradigms in social theory, because it throws up puzzles about human social interaction that can only be resolved by revising existing theory. Thus, there is a cycle in which theory stimulates ethnography, which throws up puzzles that can only be resolved by revising or changing theory. The same cycle can be discerned in economic anthropology, that sub-branch of social anthropology which understands ‘economy’ as a holistic social process interweaving production, exchange, and consumption (Narotzky 1997). The ethnography of micro-economic reality has provided rich evidence about human decision-making that can be used to address general theoretical questions. From its origin in studies of gift exchange (eg., Malinowski 1922) to contemporary studies of money and debt (eg., Graeber 2011), economic anthropology, like its parent discipline, has been preoccupied with theoretical questions like the tension between ‘agency’ and ‘structure’, ‘individual’ and ‘society’.

Purpose The purpose of this paper is to describe a new supervised machine learning study on the prediction of meeting participant’s personal note-taking from spoken dialogue acts uttered shortly before writing. Design/methodology/approach This novel approach of providing cues for finding important meeting events that would be worth recording in a meeting summary looks at temporal overlaps of multiple people’s note-taking. This research uses data of 124 meetings taken from the AMI meeting corpus. Findings The results show that several machine learning methods that the authors compared were able to classify the data significantly better than a random approach. The best model, decision trees with feature selection, achieved 70 per cent accuracy for the binary distinction writing for any number of participants simultaneously or no writing, whereas the performance for a more fine-grained distinction of the number of participants taking notes showed only about 30 per cent accuracy. Research limitations/implications The findings suggest that meeting participants take personal notes in accordance with the utterance of previously uttered speech acts, particularly dialogue acts about disfluencies and assessments appear to influence the note-taking activities. However, further research is necessary to examine other domains and to determine in what way this behaviour is helpful as a feature source for automatic meeting summarisation, which is useful for more efficiently satisfying people’s information needs about meeting contents. Practical implications The reader of an Information Systems (IS) journal would be interested in this paper because the work described and the findings gained could lead to the development of novel information systems that facilitate the work for businesses and individuals. Innovative meeting capture and retrieval applications, satisfying automatic summaries of important meeting points and sophisticated note-taking tools that suggest content automatically could make people’s daily lives more convenient in the future. Social implications There are wider implications in terms of productivity and efficiency. Business value is increased for the organisation, as human knowledge is built more or less automatically. There are also cognitive and social implications for individuals and possibly an impact on the society as a whole. It is also important for globalisation, social media and mobile devices. Originality/value The topic is new and original, as there has not been much research on it yet. Similar work was carried out recently (Murray, 2015; Bothin and Clough 2014). This is why it is relevant to an IS journal and interesting for the reader. In particular, dialogue acts about disfluencies and assessments appear to influence the note-taking activities. This behaviour is helpful as a feature source for automatic meeting summarisation, which is useful for more efficiently satisfying people’s information needs about meeting contents.

Clickbait, the intentional use of exaggerated and misleading content to entice people to click on a link to a particular web page, is a phenomenon that has grown rapidly in recent years. Clickbait has become problematic in the post-truth era as it can de-value digital content and erode people’s trust. The practice is especially common in social media where wider audiences can be reached more rapidly. Despite studies of clickbait being conducted on various social media sites, there has been little investigation of WeChat, the most popular social networking site in China. In this paper, we investigate clickbait behaviour in WeChat by analysing two samples (17,898 and 18,316 articles) for clickbait using supervised clickbait classifiers where an F1-measure of 0.834 is obtained using Naive Bayes. We train and test the classifier by manually annotating a sample of 3000 examples for clickbait. Results show that approximately 70% of our WeChat examples are likely to be clickbait. We find that articles from publishers categorised as Funny, Anime, Entertainment and Culture exhibit the most clickbait, as well as posts from publishers in specific regions, such as Guangdong, Beijing and Jiangsu. We discuss the implications of results and provide recommendations. As far as we are aware, this is the first large-scale study of clickbait activity in WeChat.

The websites of Cultural Heritage institutions attract the full range of users, from professionals to novices, for a variety of tasks. However, many institutions are reporting high bounce rates and therefore seeking ways to better engage users. The analysis of transaction logs can provide insights into users’ searching and navigational behaviours and support engagement strategies. In this paper we present the results from a transaction log analysis of web server logs representing user-system interactions from the seven websites of National Museums Liverpool (NML). In addition, we undertake an exploratory cluster analysis of users to identify potential user groups that emerge from the data. We compare this with previous studies of NML website users.

CLEF first launched a multi-lingual visual information retrieval task in 2003 as part of the ImageCLEF track. Several such tasks subsequently followed, encompassing both the medical and non-medical domains. The main aim of such ad hoc image retrieval tasks was to investigate the effectiveness of retrieval approaches that exploit textual and visual evidence in the context of large and heterogeneous collections of images that are searched for by users with diverse information needs. This chapter presents an overview of the image retrieval activities within ImageCLEF from 2003 to 2011, focusing on the non-medical domain and, in particular, on the photographic retrieval and Wikipedia image retrieval tasks. We review the available test collections built in the context of these activities, present the main evaluation results, and summarise the contributions and lessons learned.

Purpose The information use environment (IUE) – the context within which the search activity takes place – is critical to understanding the search process as this will affect how the value of information is determined. The purpose of this paper is to investigate what factors influence search in English primary schools (children aged 4–11) and how information found is subsequently used. Design/methodology/approach Ten teachers, selected using maximal variation sampling, describe search-related activities within the classroom. The resulting interview data were analysed thematically for the influence of the environment on search and different information uses. The findings were then validated against three classroom observations. Findings 12 categories of information use were identified, and 5 aspects of the environment (the national curriculum, best practice, different skills of children and teachers, keeping children safe, and limited time and resource) combine to influence and shape search in this setting. Research limitations/implications Findings support the argument that it is the IUE that is the key influence of search activity. What makes children a distinct user group is linked to the environment within which they use information rather than age, as advocated in previous studies. Practical implications The features of search systems and practical guidance for teachers and children should be designed to support information use within the IUE. Originality/value As far as the authors are aware, this is the first study to consider the influence of the IUE on how search is enacted within primary schools.

The availability of parallel corpora is limited, especially for under-resourced languages and narrow domains. On the other hand, the number of comparable documents in these areas that are freely available on the Web is continuously increasing. Algorithmic approaches to identify these documents from the Web are needed for the purpose of automatically building comparable corpora for these under-resourced languages and domains. How do we identify these comparable documents? What approaches should be used in collecting these comparable documents from different Web sources? In this chapter, we firstly present a review of previous techniques that have been developed for collecting comparable documents from the Web. Then we describe in detail three new techniques to gather comparable documents from three different types of Web sources: Wikipedia, news articles, and narrow domains.

The concept of comparability, or linguistic relatedness, or closeness between textual units or corpora has many possible applications in computational linguistics. Consequently, the task of measuring comparability has increasingly become a core technological challenge in the field, and needs to be developed and evaluated systematically. Many practical applications require corpora with controlled levels of comparability, which are established by comparability metrics. From this perspective, it is important to understand the linguistic and technological mechanisms and implications of comparability and develop a systematic methodology for developing, evaluating and using comparability metrics. This chapter presents our approach to developing and using such metrics for machine translation (MT), especially for under-resourced languages. We address three core areas: (1) systematic meta-evaluation (or calibration) of the metrics on the basis of parallel corpora; (2) the development of feature-selection techniques for the metrics on the basis of aligned comparable texts, such as Wikipedia articles and (3) applying the developed metrics for the tasks of MT for under-resourced languages and measuring their effectiveness for corpora with unknown degrees of comparability. This has led to redefining the vague linguistic concept of comparability in terms of task-specific performance of the tools, which extract phrase-level translation equivalents from comparable texts.

There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results.

There is growing evidence that search engines produce results that are socially biased, reinforcing a view of the world that aligns with prevalent social stereotypes. One means to promote greater transparency of search algorithms - which are typically complex and proprietary - is to raise user awareness of biased result sets. However, to date, little is known concerning how users perceive bias in search results, and the degree to which their perceptions differ and/or might be predicted based on user attributes. One particular area of search that has recently gained attention, and forms the focus of this study, is image retrieval and gender bias. We conduct a controlled experiment via crowdsourcing using participants recruited from three countries to measure the extent to which workers perceive a given image results set to be subjective or objective. Demographic information about the workers, along with measures of sexism, are gathered and analysed to investigate whether (gender) biases in the image search results can be detected. Amongst other findings, the results confirm that sexist people are less likely to detect and report gender biases in image search results.

There is much concern about algorithms that underlie information services and the view of the world they present. We develop a novel method for examining the content and strength of gender stereotypes in image search, inspired by the trait adjective checklist method. We compare the gender distribution in photos retrieved by Bing for the query "person" and for queries based on 68 character traits (e.g., "intelligent person") in four regional markets. Photos of men are more often retrieved for "person," as compared to women. As predicted, photos of women are more often retrieved for warm traits (e.g., "emotional") whereas agentic traits (e.g., "rational") are represented by photos of men. A backlash effect, where stereotype-incongruent individuals are penalized, is observed. However, backlash is more prevalent for "competent women" than "warm men." Results underline the need to understand how and why biases enter search algorithms and at which stages of the engineering process.

The 2014 Conference and Labs of the Evaluation Forum (CLEF) event was held at the University of Sheffield, UK, on September 15–18, 2014. The conference was entitled “Information Access Evaluation meets Multilinguality, Multimodality and Interaction” and addressed issues around multilingual and multimodal information access, information interaction, as well as the evaluation of search systems. CLEF celebrated its 15th anniversary this year having been conceived in 2000 as the Cross-Language Evaluation Forum aimed at stimulating research and innovation in multimodal and multilingual information access and retrieval. Over the years, it has actively nurtured and engaged a vibrant, multidisciplinary research community in the study, design, and implementation of evaluation methods for multiple tasks using diverse data sets and in many languages. In the first 10 years, CLEF conducted a series of experimental labs that were reported annually at workshops held in conjunction with the European Conference on Digital Libraries (ECDL). In 2010, now a mature and well-respected evaluation forum, it expanded to include a complementary peer-reviewed conference for reporting the evaluation of information access and retrieval systems regardless of data type, format, language, etc. Since then CLEF has continued that format with keynotes, contributed papers, lab sessions, and poster sessions, including reports from other benchmarking initiatives from around the world. The CLEF 2014 conference had more than 150 participants from different academic institutions and industrial organisations. Though the majority (114) of participants come from Europe, there was also considerable interest in CLEF worldwide, with 20 participants from the Americas, 16 from Asia, 1 from Australia, and 2 from the Middle East.

We describe a novel user evaluation study on the value of people's personal meeting notes for a question-answering task involving meeting data taken from the Augmented Multiparty Interaction (AMI) corpus. A survey on task perceptions and note-taking strategies in meetings was also conducted. The results suggest that written notes taken by multiple meeting participants contain the majority of the most informative meeting information and are thus likely to support information systems that employ automatic meeting understanding and summarisation. The users were able to achieve 79% and 76% accuracy in the task with handwritten and typed notes respectively as information source. Contrary to that, they scored 36% accuracy only using short extracts from the meeting speech and 98% correctness using abstractive human model summaries. The survey showed that people are generally very experienced with attending meetings and note-taking and usually record the most important meeting events in a concise way. This provides evidence for the fact that personal notes are suitable as keywords and indices into a meeting recording. In general, task results correlated positively with perceived success. Users liked well-structured abstractive human reference summaries but found meeting speech extracts difficult to work with. However, they also disliked the appearance of the notes, in particular the fact that the handwritten ones were hard to read. This emphasises the need for the development of innovative handwriting recognition methods and user interfaces that employ new presentation techniques, for example, key area highlighting. Meeting participants would also benefit from corporate training for better communication and note-taking skills. Copyright © 2014 John Wiley & Sons, Ltd.

Objective We aim to identify duplicate pairs of Medline citations, particularly when the documents are not identical but contain similar information. Materials and methods Duplicate pairs of citations are identified by comparing word n-grams in pairs of documents. N-grams are modified using two approaches which take account of the fact that the document may have been altered. These are: (1) deletion, an item in the n-gram is removed; and (2) substitution, an item in the n-gram is substituted with a similar term obtained from the Unified Medical Language System Metathesaurus. N-grams are also weighted using a score derived from a language model. Evaluation is carried out using a set of 520 Medline citation pairs, including a set of 260 manually verified duplicate pairs obtained from the Deja Vu database. Results The approach accurately detects duplicate Medline document pairs with an F1 measure score of 0.99. Allowing for word deletions and substitution improves performance. The best results are obtained by combining scores for n-grams of length 1–5 words. Discussion Results show that the detection of duplicate Medline citations can be improved by modifying n-grams and that high performance can also be obtained using only unigrams (F1=0.959), particularly when allowing for substitutions of alternative phrases.

In this paper we present results from an investigation of religious information searching based on analyzing log files from a large general-purpose search engine. From approximately 15 million queries, we identified 124,422 that were part of 60,759 user sessions. We present a method for categorizing queries based on related terms and show differences in search patterns between religious searches and web searching more generally. We also investigate the search patterns found in queries related to 5 religions: Christianity, Hinduism, Islam, Buddhism, and Judaism. Different search patterns are found to emerge. Results from this study complement existing studies of religious information searching and provide a level of detailed analysis not reported to date. We show, for example, that sessions involving religion-related queries tend to last longer, that the lengths of religion-related queries are greater, and that the number of unique URLs clicked is higher when compared to all queries. The results of the study can serve to provide information on what this large population of users is actually searching for.

Evaluation is instrumental to developing and managing effective information retrieval systems. For this process, enlisting crowdsourcing has proven viable. However, less understood are crowdsourcing's limits for evaluation, particularly for domain-specific search. The authors compare relevance assessments gathered using crowdsourcing with those from a domain expert to evaluate different search engines in a large government archive. Although crowdsourced judgments rank the tested search engines in the same order as expert judgments, crowdsourced workers appear unable to distinguish different levels of highly accurate search results the way expert assessors can.

Measuring the similarity of interlanguage-linked Wikipedia articles often requires the use of suitable language resources (e.g., dictionaries and MT systems) which can be problematic for languages with limited or poor translation resources. The size of Wikipedia can also present computational demands when computing similarity. This paper presents a ‘lightweight’ approach to measure cross-lingual similarity in Wikipedia using section headings rather than the entire Wikipedia article, and language resources derived from Wikipedia and Wiktionary to perform translation. Using an existing dataset we evaluate the approach for 7 language pairs. Results show that the performance using section headings is comparable to using all article content, dictionaries derived from Wikipedia and Wiktionary are sufficient to compute cross-lingual similarity and combinations of features can further improve results.

Museums are increasing access to their collections via web-based interfaces, but are seeing high numbers of users looking at only one or two pages within 10 s and then leaving. To decrease this rate, a better understanding of the type of user who visits a museum web-site is required. Existing models for museum web-site users tend to focus on a small number of groups or provide little detail in their definitions of the groups. This paper presents the results of a large scale museum user survey in which data on a wide range of user characteristics was collected to provide well founded definitions for the user group’s motivations, tasks, engagement, and domain knowledge. The results highlight that the general public and non-professional users make up the majority of users and allow us to clearly define these two groups.

People use digital cultural heritage sites in different ways and for various purposes. In this paper we explore what information people search for and why when using Europeana, one of the world’s largest aggregators of cultural heritage. We gathered a probability sample of 240 search requests from users via an online survey and used qualitative content analysis complemented with Shatford-Panofsky’s mode/facet analysis for analysing requests to visual archives to investigate the following: (i) the broad type of search task; (ii) the subject content of searches; and (iii) motives for searching and uses of the information found. Results highlight the rich diversity of searches conducted using Europeana. Contributions include: collection and analysis of a comprehensive sample of Europeana search requests, a scheme for categorising information use, and deeper insights into the users and uses of Europeana.

Homoglyphs can be used for disguising plagiarized text by replacing letters in source texts with visually identical letters from other scripts. Most current plagiarism detection systems are not able to detect plagiarism when text has been obfuscated using homoglyphs. In this work, we present two alternative approaches for detecting plagiarism in homoglyph obfuscated texts. The first approach utilizes the Unicode list of confusables to replace homoglyphs with visually identical letters, while the second approach uses a similarity score computed using normalized hamming distance to match homoglyph obfuscated words with source words. Empirical testing on datasets from PAN-2015 shows that both approaches perform equally well for plagiarism detection in homoglyph obfuscated texts.

The 2014 Conference and Labs of the Evaluation Forum (CLEF) event was held at the University of Sheffield, UK, on September 15–18, 2014. The conference was entitled “Information Access Evaluation meets Multilinguality, Multimodality and Interaction” and addressed issues around multilingual and multimodal information access, information interaction, as well as the evaluation of search systems. CLEF celebrated its 15th anniversary this year having been conceived in 2000 as the Cross-Language Evaluation Forum aimed at stimulating research and innovation in multimodal and multilingual information access and retrieval. Over the years, it has actively nurtured and engaged a vibrant, multidisciplinary research community in the study, design, and implementation of evaluation methods for multiple tasks using diverse data sets and in many languages. In the first 10 years, CLEF conducted a series of experimental labs that were reported annually at workshops held in conjunction with the European Conference on Digital Libraries (ECDL). In 2010, now a mature and well-respected evaluation forum, it expanded to include a complementary peer-reviewed conference for reporting the evaluation of information access and retrieval systems regardless of data type, format, language, etc. Since then CLEF has continued that format with keynotes, contributed papers, lab sessions, and poster sessions, including reports from other benchmarking initiatives from around the world. The CLEF 2014 conference had more than 150 participants from different academic institutions and industrial organisations. Though the majority (114) of participants come from Europe, there was also considerable interest in CLEF worldwide, with 20 participants from the Americas, 16 from Asia, 1 from Australia, and 2 from the Middle East.

On August 1st, 2013 the First Workshop on the Exploration, Navigation and Retrieval of Information in Cultural Heritage (ENRICH 2013) was held as part of the SIGIR 2013 conference in Dublin, Ireland. An invited talk was given by Prof. Jaap Kamps. There were 3 full papers and 3 short papers presented in addition to a poster and demonstration session. Finally, there was a lively discussion on determining the research roadmap for this specialist area of IR research. This contribution discusses the events of the workshop and outlines future directions for the community.

Paper has been the format of choice for disseminating geographic information for millennia; however the arrival of the internet and mobile technologies has created new modes of map consumption. This paper investigates the future role of paper mapping in a society where access to online digital mapping is freely available. The research consists of an online survey to investigate how people use and view online/digital and paper maps, together with a task-based user study to investigate the suitability of each map format for a variety of given tasks. Similar to previous research, we categorize participants into two groups (experts and non-experts/novices) based on their level of geographic skill. Results demonstrate that geographic knowledge and the context of use clearly relate to preference of map format. As geographic skill increases, there is a greater preference for paper maps; non-experts prefer online/digital maps. Also, paper is the preferred medium for planning and executing navigation on foot across all groups. Overall, results suggest that paper maps are here to stay for the foreseeable future and remain the preferred choice for some in the digital age.

PurposeThe purpose of this paper is to investigate how healthcare professionals search for and select the medical images they need within medical settings.Design/methodology/approachIn total, 29 healthcare professionals participated in the study. Using a think‐aloud technique and face‐to‐face interviews, the authors asked participants to explain how they looked for medical images and how they judged the relevancy of retrieved images.FindingsA total of 15 criteria were applied by participants when determining the relevance of medical images in relation to their information needs. Topicality was found to be the primary and most important criterion used by participants.Originality/valueThis study compares the relevance criteria used for medical images with those identified in the literature and highlights six additional criteria that have not been suggested in previous work.

Information Retrieval (IR) research has traditionally focused on serving the best results for a single query - so-called ad hoc retrieval. However, users typically search iteratively, refining and reformulating their queries during a session. A key challenge in the study of this interaction is the creation of suitable evaluation resources to assess the effectiveness of IR systems over sessions. This paper describes the TREC Session Track, which ran from 2010 through to 2014, which focussed on forming test collections that included various forms of implicit feedback. We describe the test collections; a brief analysis of the differences between datasets over the years; and the evaluation results that demonstrate that the use of user session data significantly improved effectiveness.

The workshop aims to bring together researchers and practitioners to review and discuss ways of providing effective access to large-scale collections of cultural heritage content. The scale, variety and availability of cultural heritage content, combined with the variety of user groups with respect to background knowledge, specialist experience and needs is challenging in the context of existing access methods. In particular, we consider going beyond keyword search in large-scale cultural heritage digital libraries, in support of exploration and discovery. Our purpose for the workshop is to consider the opportunities and challenges presented by new and existing technologies, as well as the needs and experiences of diverse user communities. Our goal is to assess the current state-of the-art, to identify opportunities and establish future research priorities, informed by the combined knowledge and experience of academics and practitioners.

Data-driven approaches have become increasingly popular as a means for analyzing transaction logs from web search engines and digital libraries, for example using cluster analysis to identify common patterns of search and navigation behavior. However, steps must be taken to ensure that results are reliable and repeatable. Although clustering patterns of user interaction behavior has been previously explored, one aspect that has received less attention is cluster stability that can be used to aid cluster validation. In this paper we compute stability based on the Jaccard coefficient to investigate the cluster stability when using different subsets of transaction log data from WorldCat.org. Results provide insights into different types of search behaviors and highlight that clusters of varying degrees of stability will result from the clustering process. However, we show that additional investigation beyond the results of cluster stability is required to fully validate the resulting clusters.

The identification of duplicated and plagiarized passages of text has become an increasingly active area of research. In this paper, we investigate methods for plagiarism detection that aim to identify potential sources of plagiarism from MEDLINE, particularly when the original text has been modified through the replacement of words or phrases. A scalable approach based on Information Retrieval is used to perform candidate document selection—the identification of a subset of potential source documents given a suspicious text—from MEDLINE. Query expansion is performed using the ULMS Metathesaurus to deal with situations in which original documents are obfuscated. Various approaches to Word Sense Disambiguation are investigated to deal with cases where there are multiple Concept Unique Identifiers (CUIs) for a given term. Results using the proposed IR-based approach outperform a state-of-the-art baseline based on Kullback-Leibler Distance.