The Multidimensional Study of Viral Campaigns as Branching Processes

Viral campaigns on the Internet may follow variety of models, depending on the content, incentives, personal attitudes of sender and recipient to the content and other factors. Due to the fact that the knowledge of the campaign specifics is essential for the campaign managers, researchers are constantly evaluating models and real-world data. The goal of this article is to present the new knowledge obtained from studying two viral campaigns that took place in a virtual world which followed the branching process. The results show that it is possible to reduce the time needed to estimate the model parameters of the campaign and, moreover, some important aspects of time-generations relationship are presented.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201

Studying Paths of Participation in Viral Diffusion Process

Authors propose a conceptual model of participation in viral diffusion process composed of four stages: awareness, infection, engagement and action. To verify the model it has been applied and studied in the virtual social chat environment settings. The study investigates the behavioral paths of actions that reflect the stages of participation in the diffusion and presents shortcuts, that lead to the final action, i.e. the attendance in a virtual event. The results show that the participation in each stage of the process increases the probability of reaching the final action. Nevertheless, the majority of users involved in the virtual event did not go through each stage of the process but followed the shortcuts. That suggests that the viral diffusion process is not necessarily a linear sequence of human actions but rather a dynamic system.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201

IoT Design Challenges and the Social IoT Solution

The IoT (Internet of Things) promises to be the major phenomenon in information technology in the near term. By some forecasts more than half of all new IT system deployments by 2020 will incorporate some form of IoT technology. Currently, however, there is no dominant IoT platform and no universal IoT design standards currently in use. This contributes to Architectural Heterogeneity which in turn contributes to high integration costs and inhibits IoT benefits realisation. The use of universal design standards presents one solution to this problem. Social Internet of Things (SIoT) methods use the way that people manage social relationships as a reference architecture for the way to manage the interaction between the various Things in an IoT network. This paper discusses some of the current IoT design challenges and presents solutions couched in SIoT that can be used as standards for future IoT designs to reduce Architectural Heterogeneity

On recommending hashtags in Twitter networks

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

A survey of recommender systems in Twitter

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

The use of software tools and autonomous bots against vandalism: eroding Wikipedia’s moral order?

English - language Wikipedia is constantly being plagued by vandalistic contributions on a massive scale. In order to fight them its volunteer contributors deploy an array of software tools and autonomous bots. After an analysis of their functioning and the ‘ coactivity ’ in use between humans and bots, this research ‘ discloses ’ the moral issues that emerge from the combined patrolling by humans and bots. Administrators provide the stronger tools only to trusted users, thereby creating a new hierarchical layer. Further, surveillance exhibits several troubling features : questionable profiling practices, the use of the controversial measure of reputation, ‘ oversurveillance ’ where quantity trumps quality, and a prospective loss of the required moral skills whenever bots take over from humans. The most troubling aspect, though, is that Wikipedia has become a Janus - faced institution. One face is the basic platform of MediaWiki software, transparent to all. Its other face is the anti - vandalism system, which, in contrast, is opaque to the average user, in particular as a result of the algorithms and neural networks in use. Finally it is argued that this secrecy impedes a much needed discussion to unfold ; a discussion that should focus on a ‘ rebalancing ’ of the anti - vandalism system and the development of more ethical information practices towards contributors

Toward Point-of-Interest Recommendation Systems: A Critical Review on Deep-Learning Approaches

In recent years, location-based social networks (LBSNs) that allow members to share their location and provide related services, and point-of-interest (POIs) recommendations which suggest attractive places to visit, have become noteworthy and useful for users, research areas, industries, and advertising companies. The POI recommendation system combines different information sources and creates numerous research challenges and questions. New research in this field utilizes deep-learning techniques as a solution to the issues because it has the ability to represent the nonlinear relationship between users and items more effectively than other methods. Despite all the obvious improvements that have been made recently, this field still does not have an updated and integrated view of the types of methods, their limitations, features, and future prospects. This paper provides a systematic review focusing on recent research on this topic. First, this approach prepares an overall view of the types of recommendation methods, their challenges, and the various influencing factors that can improve model performance in POI recommendations, then it reviews the traditional machine-learning methods and deep-learning techniques employed in the POI recommendation and analyzes their strengths and weaknesses. The recently proposed models are categorized according to the method used, the dataset, and the evaluation metrics. It found that these articles give priority to accuracy in comparison with other dimensions of quality. Finally, this approach introduces the research trends and future orientations, and it realizes that POI recommender systems based on deep learning are a promising future work