Print news agencies have been under pressure from falling sales and advertising revenue and increased competition. As the Internet became the dominant medium, news agencies invested heavily in their websites and apps, providing their news for free, rather than selling a print edition. Reducing the cost of production and removing access barriers such as geographic location had the potential to increase readership and advertising, covering costs and maintaining profits. Unfortunately, this business model has for the most part failed. Many higher quality news agencies are now implementing paywalls on their news websites to once again monetize their product. Others have begun to emulate the look and feel of tabloid news websites to increase readership and stickiness and advertising revenue. This study shows the negative impact of such visual tabloidization on initial impressions of credibility, which may have long term detrimental effects on the news agency.

The second WEPIR 2019 workshop brings together researchers with different backgrounds interested in continuing to explore and advance the evaluation of personalisation in information retrieval. The workshop builds on the first WEPIR workshop held at CHIIR 2018, and will focus on further developing a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. In particular, the planned outcome of the workshop is to progress the work from WEPIR 2018 towards the development of concrete proposals for novel and innovative methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.

The purpose of the WEPIR 2018 workshop is to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in information retrieval. The workshop focus is on the development of a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. The planned outcome of the workshop is the proposal of methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.

This poster briefly elucidates on a crowdsourced exploratory study demonstrating the impact of common features of news websites' design on perceived bias. Type of news website, user characteristics, and the overall design, were shown to impact on perceived bias. The poster also reports the results of a novel method to validate and extend the initial results through comparative reevaluation. These confirmed the initial results and revealed additional significant findings. Lastly, the paper proposes a model of bias for personalizing news websites.

This paper proposes a learning model that tries to infer a user's topical expertise using multiple types of user-related data from Twitter such as tweets posted by the user and the characteristics of their followers. It considers inference consistency of different types of user data in the process of learning and aims to deliver accurate and effective inference results, even in cases where some types of data are missing for a user, e.g. the user has yet to post any tweets. Experiments conducted on a large-scale Twitter dataset show that our model outperforms several baseline approaches which use only a single type of user data for inference.

Bias, whether real or perceived by the user, is inherent in news media. In this paper, we demonstrate that user characteristics, the design and common technical features of news websites impact users' perception of bias. A complex bias evaluation process was conducted using crowdsourced participants on webpages from nine popular news websites. Each webpage was subject to one of eight distortions which removed individual features of the design. Along with the control, a 9x9 experiment was conducted with participants asked to rate their perception of positive or negative bias in the design of the webpages. This tested the impact that removing each feature had on the user's perception of bias. Significant differences were found between how participants rated some distorted webpages and their respective controls. The category of news website was also found to influence the perception of bias. Furthermore, certain groups of users were found to have a predilection for rating certain categories of websites as more or less biased and were influenced by particular features of the design.

Understanding the expertise of users in social networking sites like Twitter is a key component for many applications such as user recommendation and talent seeking. A range of interactions between users on Twitter can provide important information that implicitly reflects a user's expertise. This paper proposes a learning model that tries to infer a user's topical expertise from Twitter using information such as tweets posted by the user and the characteristics of their followers. The model takes various types of user-related data from Twitter as input and considers their inference consistency in the process of learning. It aims to deliver accurate and effective inference results, even in cases where some types of data are missing for a user, e.g. the user has yet to post any tweets. The experiments reported in the paper were conducted on a large-scale Twitter dataset. Experimental results show that our model outperforms several baseline approaches and outperforms approaches which use only a single type of user data for inference.

Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships, and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user's past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile, respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.

Search personalization that considers the social dimension of the web has attracted a significant volume of research in recent years. A user profile is usually needed to represent a user?s interests in order to tailor future searches. Previous research has typically constructed a profile solely from a user?s usage information. When the user has only limited activities in the system, the effect of the user profile on search is also constrained. This research addresses the setting where a user has only a limited amount of usage information. We build enhanced user profiles from a set of annotations and resources that users have marked, together with an external knowledge base constructed according to usage histories. We present two probabilistic latent topic models to simultaneously incorporate social annotations, documents and the external knowledge base. Our web search strategy is achieved using personalized social query expansion. We introduce a topical query expansion model to enhance the search by utilizing individual user profiles. The proposed approaches have been intensively evaluated on a large public social annotation dataset. Results show that our models significantly outperformed existing personalized query expansion methods which use user profiles solely built from past usage information in personalized search.

The ability to understand the expertise of users in Social Networking Sites (SNSs) is a key component for delivering effective information services such as talent seeking and user recommendation. However, users are often unwilling to make the effort to explicitly provide this information, so existing methods aimed at user expertise discovery in SNSs primarily rely on implicit inference. This work aims to infer a user's expertise based on their posts on the popular micro-blogging site Twitter. The work proposes a sentiment-weighted and topic relation-regularized learning model to address this problem. It first uses the sentiment intensity of a tweet to evaluate its importance in inferring a user's expertise. The intuition is that if a person can forcefully and subjectively express their opinion on a topic, it is more likely that the person has strong knowledge of that topic. Secondly, the relatedness between expertise topics is exploited to model the inference problem. The experiments reported in this paper were conducted on a large-scale dataset with over 10,000 Twitter users and 149 expertise topics. The results demonstrate the success of our proposed approach in user expertise inference and show that the proposed approach outperforms several alternative methods.

‘Big Data’ can mean something quite different in the context of Humanities. The way Humanities scholars frame their inquiries often leverages collections that are an order of magnitude smaller than the full, industrial scale, there is significant value to be found in the Humanistic sense of ‘Big’. In particular, the variety of the data, and the richness of the explorations, means that high-quality knowledge systems are required. More meaning is needed than the surface analytics often demonstrated in other ‘Big’ scenarios. This paper examines how a specific collection related to the 1916 Rising in Ireland was analyzed. The result was a process to extract entities that underpinned a highly-effective personalized knowledge-driven exploration of that collection by users. It demonstrates the mutual benefit of natural language at scale with rich humanistic inquiry to communicate improved experiences for a much broader range of users than would otherwise be possible.

The Exploration, Navigation and Retrieval of Information in Cultural Heritage Workshop (ENRICH 2013) offers a forum to 1) discuss the challenges and opportunities in Information Retrieval research in the area of Cultural Heritage; 2) encourage collaboration between researchers engaged in work in this specialist area of Information Retrieval, and to foster the formation of a research community; and 3) identify a set of actions which the community should undertake to progress the research agenda. The workshop will foster a new stream of Information Retrieval research and support the design of search tools that can help end-users fully exploit the wonderful Cultural Heritage material that is available across the globe.

Content slicing addresses the need of adaptive systems to reuse open corpus material by converting it into re-composable information objects. However this conversion is highly dependent upon the ability to correctly fragment pages into structurally sound atomic pieces. A recently suggested approach to fragmentation, which relies on densitometric page representation, claims to achieve high accuracy and time performance. Although it has been well received within the research community, a full evaluation of this approach and identification of strengths and weaknesses across a range of characteristics hasn't been performed. This paper proposes an independent evaluation of the approach with respect to granularity control, accuracy, time performance, content diversity and linguistic dependency. Moreover, this paper also provides a significant contribution to address important weaknesses discovered during the analysis, in order to improve the suitability and impact of the original algorithm within the context of content slicing.

The field of information retrieval still strives to develop models which allow semantic information to be integrated in the ranking process to improve performance in comparison to standard bag-of-words based models. Cross-lingual information retrieval is an example of where such a model is required, as content or concepts often need to be matched across languages. To overcome this problem, a conceptual model has been adopted in ranking an entire corpus which normally exploits latent/implicit features of the text. One of the drawbacks of this model is that the computational cost is significant and often intractable in modern test collections. Therefore, approaches utilizing concept-based models for re-ranking initial retrieval results have attracted a considerable amount of study, in particular the latent concept model. However, fitting such a model to a smaller collection is less meaningful than fitting it into the whole corpus. This paper proposes a late fusion method which incorporates scores generated by using external knowledge to enhance the space produced by the latent concept method. This method is further demonstrated to be suitable for multilingual re-ranking purposes. To illustrate the effectiveness of the proposed method, experiments were conducted over test collections across three languages. The results demonstrate that the method can comfortably achieve improvements in retrieval performance over several re-ranking methods.

The World Wide Web (WWW) provides access to a vast array of educational content, a great deal of which is ideal for incorporation into eLearning experiences. However sourcing, harvesting and incorporating appropriate content has proven to be a complex and arduous task. This paper introduces a system that enables the discovery and classification of educational content from open corpus sources, such as the WWW, and facilitates the incorporation of such content into eLearning systems. The open corpus content service (OCCS) discovers, harvests and indexes content through the implementation of a focused Web crawler, content classifier and indexer. This reduces the cognitive load placed on the educator by content authoring, allowing them to focus on the pedagogical design of eLearning offerings.