Social Media Usage Patterns: Research Note Regarding the Lack of Universal Validated Measures for Active and Passive Use

The existing literature regarding social media use provides extant evidence supporting the claim that usage patterns ultimately have the capability of impacting users. However, the vast majority of the literature is based upon experimental laboratory settings where participants are observed by researchers. The current article asserts that there is a significant deficiency within the discipline regarding the validated measurement of usage patterns of social networking sites (SNSs) and offers guidance for those who may want to develop a general measure

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

Exploring the information behaviour of users of Welsh Newspapers Online through web log analysis

Purpose – Webometric techniques have been applied to many websites and online resources, especially since the launch of Google Analytics (GA). To date, though, there has been little consideration of information behaviour in relation to digitised newspaper collections. The purpose of this paper is to address a perceived gap in the literature by providing an account of user behaviour in the newly launched Welsh Newspapers Online (WNO). Design/methodology/approach – The author collected webometric data for WNO using GA and web server content logs. These were analysed to identify patterns of engagement and user behaviour, which were then considered in relation to existing information behaviour. Findings – Use of WNO, while reminiscent of archival information seeking, can be understood as centring on the web interface rather than the digitised material. In comparison to general web browsing, users are much more deeply engaged with the resource. This engagement incorporates reading online, but users’ information seeking utilises website search and browsing functionality rather than filtering in newspaper material. Information seeking in digitised newspapers resembles the model of the “user” more closely than that of the “reader”, a value-laden distinction which needs further unpacking. Research limitations/implications – While the behaviour discussed in this paper is likely to be more widely representative, a larger longitudinal data set would increase the study’s significance. Additionally, the methodology of this paper can only tell us what users are doing, and further research is needed to identify the drivers for this behaviour. Originality/value – This study provides important insights into the underinvestigated area of digitised newspaper collections, and shows the importance of webometric methods in analysing online user behaviour

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing