Community Question Answering (CQA) forums such as Stack Overflow (SO) are a form of crowdsourced knowledge for software engineers who seek solutions to development and programming challenges. While such a forum provides valuable support to engineers, it often contains low quality content that impacts users' experience and the longevity of new users. Past research shows that most of the low-quality content comes from violating general Netiquette Rules (NRs). In the past, several researchers have worked on analysing the content of SO and suggested approaches to increase its quality. However, to the best of our knowledge, there is no previous work that has reviewed the scale of scientific attention that is given to this cause and the recommendations that have been made. We have conducted a Systematic Mapping Study (SMS) using five relevant databases, reviewing 1,489 papers and selecting 18 that are relevant to help to address this gap. We have found that SO has attracted increasing research interest on reducing NRs violations to improve the quality of communication on SO. Interestingly, the majority of papers used manual qualitative and quantitative analysis approaches to investigate this area. We have found that further research is required to identify more violation features, generalisable sources of data and that the use of computational analysis approaches are still needed in this area.

Abstract This paper reviews types and uses of card sorting and how a relatively unpopularized variation of card sorting—‘repeated single-criterion sorting’—can be applied to the information architecture design of digital music services. A total of 52 respondents were asked to sort, using their own choice of criteria, 12 popular songs using an online card sorting tool. Once respondents had chosen a construct for a particular sort e.g. ‘Genre’, they placed each card into a named category, e.g. ‘Rock’, ‘Pop’, and were encouraged to repeat this process until they could think of no more constructs. High levels of agreement were found for a small number of constructs such as ‘genre’, ‘gender’ and ‘speed of song’, but the remaining constructs were individual to each respondent e.g. ‘songs that make me cry’. The results highlighted differences with current approaches to music categorization, as well as the potential for repeated single-criterion sorting to be used to design faceted navigation structures and form part of the user-centred design process.

Current Learning Analytics (LA) systems are primarily designed with University staff members as the target audience; very few are aimed at students, with almost none being developed with direct student involvement and undertaking a comprehensive evaluation. This paper describes a HEFCE funded project that has employed a variety of methods to engage students in the design, development and evaluation of a student facing LA dashboard. LA was integrated into the delivery of 4 undergraduate modules with 169 student sign-ups. The design of the dashboard uses a novel approach of trying to understand the reasons why students want to study at university and maps their engagement and predicted outcomes to these motivations, with weekly personalised notifications and feedback. Students are also given the choice of how to visualise the data either via a chart-based view or to be represented as themselves. A mixed-methods evaluation has shown that students' feelings of dependability and trust of the underlying analytics and data is variable. However, students were mostly positive about the usability and interface design of the system and almost all students once signed-up did interact with their LA. The majority of students could see how the LA system could support their learning and said that it would influence their behaviour. In some cases, this has had a direct impact on their levels of engagement. The main contribution of this paper is the transparent documentation of a User Centred Design approach that has produced forms of LA representation, recommendation and interaction design that go beyond those used in current similar systems and have been shown to motivate students and impact their learning behaviour.

The increasing volume of malicious content in social networks requires automated methods to detect and eliminate such content. This paper describes a supervised machine learning classification model that has been built to detect the distribution of malicious content in online social networks (ONSs). Multisource features have been used to detect social network posts that contain malicious Uniform Resource Locators (URLs). These URLs could direct users to websites that contain malicious content, drive-by download attacks, phishing, spam, and scams. For the data collection stage, the Twitter streaming application programming interface (API) was used and VirusTotal was used for labelling the dataset. A random forest classification model was used with a combination of features derived from a range of sources. The random forest model without any tuning and feature selection produced a recall value of 0.89. After further investigation and applying parameter tuning and feature selection methods, however, we were able to improve the classifier performance to 0.92 in recall.

Background: Since the introduction of the systematic review process to Software Engineering in 2004, researchers have investigated a number of ways to mitigate the amount of effort and time taken to filter through large volumes of literature.

BACKGROUND: Semantically-enriched browsing has enhanced the browsing experience by providing contextualized dynamically generated Web content, and quicker access to searched-for information. However, adoption of Semantic Web technologies is limited and user perception from the non-IT domain sceptical. Furthermore, little attention has been given to evaluating semantic browsers with real users to demonstrate the enhancements and obtain valuable feedback. The Sealife project investigates semantic browsing and its application to the life science domain. Sealife's main objective is to develop the notion of context-based information integration by extending three existing Semantic Web browsers (SWBs) to link the existing Web to the eScience infrastructure. METHODS: This paper describes a user-centred evaluation framework that was developed to evaluate the Sealife SWBs that elicited feedback on users' perceptions on ease of use and information findability. Three sources of data: i) web server logs; ii) user questionnaires; and iii) semi-structured interviews were analysed and comparisons made between each browser and a control system. RESULTS: It was found that the evaluation framework used successfully elicited users' perceptions of the three distinct SWBs. The results indicate that the browser with the most mature and polished interface was rated higher for usability, and semantic links were used by the users of all three browsers. CONCLUSION: Confirmation or contradiction of our original hypotheses with relation to SWBs is detailed along with observations of implementation issues.