Abstract Bias in news search engines has been shown to influence users' perceptions of a news topic and contribute to the polarisation of society. As a result, there is a need for news search engines that increase user awareness of biases in the search results. While technical approaches have been developed to mitigate biases in search, very few studies have investigated user preferences in interface designs for potentially raising their awareness of biases in news search engines. In this study, we utilized a participatory design methodology to develop eight prototypes with different features that could potentially be used to raise user awareness of biases in news search engines. We conducted three user studies, involving 132 participants with Computer Science backgrounds, to evaluate these prototypes. Our findings indicate the importance of news search engines that (a) inform users of possible biases in the results (bias visualization approach) and (b) allow users to access alternative search results (results‐reranking approach). Our study provides further insights into the strengths and possible risks of each approach, which are important for future research on designing interfaces for raising user awareness of biases in news search engines.

We present SNuC, the first published corpus of spoken alphanumeric identifiers of the sort typically used as serial and part numbers in the manufacturing sector. The dataset contains recordings and transcriptions of over 50 native British English speakers, speaking over 13,000 multi-character alphanumeric sequences and totalling almost 20 hours of recorded speech. We describe requirements taken into account in the designing the corpus and the methodology used to construct it. We present summary statistics describing the corpus contents, as well as a preliminary investigation into errors in spoken alphanumeric identifiers. We validate the corpus by showing how it can be used to adapt a deep learning neural network based ASR system, resulting in improved recognition accuracy on the task of spoken alphanumeric identifier recognition. Finally, we discuss further potential uses for the corpus and for the tools developed to construct it.

During times of crisis, information access is crucial. Given the opaque processes behind modern search engines, it is important to understand the extent to which the "picture" of the Covid-19 pandemic accessed by users differs. We explore variations in what users "see" concerning the pandemic through Google image search, using a two-step approach. First, we crowdsource a search task to users in four regions of Europe, asking them to help us create a photo documentary of Covid-19 by providing image search queries. Analysing the queries, we find five common themes describing information needs. Next, we study three sources of variation - users' information needs, their geo-locations and query languages - and analyse their influences on the similarity of results. We find that users see the pandemic differently depending on where they live, as evidenced by the 46% similarity across results. When users expressed a given query in different languages, there was no overlap for most of the results. Our analysis suggests that localisation plays a major role in the (dis)similarity of results, and provides evidence of the diverse "picture" of the pandemic seen through Google.

Open‐access mega‐journals (OAMJs) are characterized by their large scale, wide scope, open‐access (OA) business model, and “soundness‐only” peer review. The last of these controversially discounts the novelty, significance, and relevance of submitted articles and assesses only their “soundness.” This article reports the results of an international survey of authors (n = 11,883), comparing the responses of OAMJ authors with those of other OA and subscription journals, and drawing comparisons between different OAMJs. Strikingly, OAMJ authors showed a low understanding of soundness‐only peer review: two‐thirds believed OAMJs took into account novelty, significance, and relevance, although there were marked geographical variations. Author satisfaction with OAMJs, however, was high, with more than 80% of OAMJ authors saying they would publish again in the same journal, although there were variations by title, and levels were slightly lower than subscription journals (over 90%). Their reasons for choosing to publish in OAMJs included a wide variety of factors, not significantly different from reasons given by authors of other journals, with the most important including the quality of the journal and quality of peer review. About half of OAMJ articles had been submitted elsewhere before submission to the OAMJ with some evidence of a “cascade” of articles between journals from the same publisher.

We present a supervised approach to automatically labelling topic clusters of reader comments to online news.We use a feature set that includes both features capturing properties local to the cluster and features that capture aspects from the news article and from comments outside the cluster.We evaluate the approach in an automatic and a manual, task-based setting.Both evaluations show the approach to outperform a baseline method, which uses tf*idf to select comment-internal terms for use as topic labels.We illustrate how cluster labels can be used to generate cluster summaries and present two alternative summary formats: a pie chart summary and an abstractive summary.

Researchers are beginning to explore how to generate summaries of extended argumentative conversations in social media, such as those found in reader comments in on-line news.To date, however, there has been little discussion of what these summaries should be like and a lack of humanauthored exemplars, quite likely because writing summaries of this kind of interchange is so difficult.In this paper we propose one type of reader comment summary -the conversation overview summary -that aims to capture the key argumentative content of a reader comment conversation.We describe a method we have developed to support humans in authoring conversation overview summaries and present a publicly available corpusthe first of its kind -of news articles plus comment sets, each multiply annotated, according to our method, with conversation overview summaries.

This paper presents results comparing user preference for search engine rankings with measures of effectiveness computed from a test collection. It establishes that preferences and evaluation measures correlate: systems measured as better on a test collection are preferred by users. This correlation is established for both "conventional web retrieval" and for retrieval that emphasizes diverse results. The nDCG measure is found to correlate best with user preferences compared to a selection of other well known measures. Unlike previous studies in this area, this examination involved a large population of users, gathered through crowd sourcing, exposed to a wide range of retrieval systems, test collections and search tasks. Reasons for user preferences were also gathered and analyzed. The work revealed a number of new results, but also showed that there is much scope for future work refining effectiveness measures to better capture user preferences.

In this paper we examine user queries with respect to diversity: providing a mix of results across different interpretations. Using two query log analysis techniques (click entropy and reformulated queries), 14.9 million queries from the Microsoft Live Search log were analysed. We found that a broad range of query types may benefit from diversification. Additionally, although there is a correlation between word ambiguity and the need for diversity, the range of results users may wish to see for an ambiguous query stretches well beyond traditional notions of word sense.