Social and Digital Skills on Social Media Use in Tanzania

The study explores the relationship between digital and non-digital skills on social media use in Tanzania. The study examines the role played by three types of skills: social skills, business support skills, and digital skills on extensive and intensive social media use. Specifically, the study attempts to scrutinize relevant skills needed by a user when encounters a challenge while using social media. Researchers used the Heckman selection model on the national representative sample.Results suggest that digital skills are essential to social media use. In addition, social skills, the ability to request assistance from the close network when experiencing challenges during interaction with social media platforms, play a crucial role in fostering social media use. The results pose an imperative argument to the literature in the way that digital skills are acquired. It suggests that individuals within a close network of people possessing higher digital skills are most likely to acquire those digital skills. Hence enable them to cultivate individual social media use

Leveraging contextual-cognitive relationships into mobile commerce systems

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosophyMobile smart devices are becoming increasingly important within the on-line purchasing cycle. Thus the requirement for mobile commerce systems to become truly context-aware remains paramount if they are to be effective within the varied situations that mobile users encounter. Where traditionally a recommender system will focus upon the user – item relationship, i.e. what to recommend, in this thesis it is proposed that due to the complexity of mobile user situational profiles the how and when must also be considered for recommendations to be effective. Though non-trivial, it should be, through the understanding of a user’s ability to complete certain cognitive processes, possible to determine the likelihood of engagement and therefore the success of the recommendation. This research undertakes an investigation into physical and modal contexts and presents findings as to their relationships with cognitive processes. Through the introduction of the novel concept, disruptive contexts, situational contexts, including noise, distractions and user activity, are identified as having significant effects upon the relationship between user affective state and cognitive capability. Experimental results demonstrate that by understanding specific cognitive capabilities, e.g. a user’s perception of advert content and user levels of purchase-decision involvement, a system can determine potential user engagement and therefore improve the effectiveness of recommender systems’ performance. A quantitative approach is followed with a reliance upon statistical measures to inform the development, and subsequent validation, of a contextual-cognitive model that was implemented as part of a context-aware system. The development of SiDISense (Situational Decision Involvement Sensing system) demonstrated, through the use of smart-phone sensors and machine learning, that is was viable to classify subjectively rated contexts to then infer levels of cognitive capability and therefore likelihood of positive user engagement. Through this success in furthering the understanding of contextual-cognitive relationships there are novel and significant advances that are now viable within the area of m-commerce

Of echo chambers and contrarian clubs: Exposure to political disagreement among German and Italian users of Twitter

Scholars have debated whether social media platforms, by allowing users to select the information to which they are exposed, may lead people to isolate themselves from viewpoints with which they disagree, thereby serving as political “echo chambers.” We investigate hypotheses concerning the circumstances under which Twitter users who communicate about elections would engage with (a) supportive, (b) oppositional, and (c) mixed political networks. Based on online surveys of representative samples of Italian and German individuals who posted at least one Twitter message about elections in 2013, we find substantial differences in the extent to which social media facilitates exposure to similar versus dissimilar political views. Our results suggest that exposure to supportive, oppositional, or mixed political networks on social media can be explained by broader patterns of political conversation (i.e., structure of offline networks) and specific habits in the political use of social media (i.e., the intensity of political discussion). These findings suggest that disagreement persists on social media even when ideological homophily is the modal outcome, and that scholars should pay more attention to specific situational and dispositional factors when evaluating the implications of social media for political communication

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection