Sanat Kumar Bista

Recommendation Based Trust Model with an Effective Defence Scheme for MANETs

The reliability of delivering packets through multi-hop intermediate nodes is a significant issue in the mobile ad hoc networks (MANETs). The distributed mobile nodes establish connections to form the MANET, which may include selfish and misbehaving nodes. Recommendation based trust management has been proposed in the literature as a mechanism to filter out the misbehaving nodes while searching for a packet delivery route. However, building a trust model that adopts recommendations by other nodes in the network is a challenging problem due to the risk of dishonest recommendations like bad-mouthing, ballot-stuffing, and collusion. This paper investigates the problems related to attacks posed by misbehaving nodes while propagating recommendations in the existing trust models. We propose a recommendation based trust model with a defence scheme, which utilises clustering technique to dynamically filter out attacks related to dishonest recommendations between certain time based on number of interactions, compatibility of information and closeness between the nodes. The model is empirically tested under several mobile and disconnected topologies in which nodes experience changes in their neighbourhood leading to frequent route changes. The empirical analysis demonstrates robustness and accuracy of the trust model in a dynamic MANET environment.

A trust model-based analysis of social networks

In this paper, we analyse the sustainability of social networks using STrust, our social trust model. The novelty of the model is that it introduces the concept of engagement trust and combines it with the popularity trust to derive the social trust of the community as well as of individual members in the community. This enables the recommender system to use these different types of trust to recommend different things to the community, and identify (and recommend) different roles. For example, it recommends mentors using the engagement trust and leaders using the popularity trust. We then show the utility of the model by analysing data from two types of social networks. We also study the sustainability of a community through our social trust model. We observe that a 5% drop in highly trusted members causes more than a 50% drop in social capital that, in turn, raises the question of sustainability of the community. We report our analysis and its results.

Gamification for Online Communities: A Case Study for Delivering Government Services

Gamification, the idea of inserting game dynamics into portals or social networks, has recently evolved as an approach to encourage active participation in online communities. For an online community to start and proceed on to a sustainable operation, it is important that members are encouraged to contribute positively and frequently. We decided to introduce gamification in an online community that we designed and developed with the Australian Governments Department of Human Services to support welfare recipients transitioning from one payment to another. We first defined a formal model of gamification and a gamification design process. In instantiating our model to the online community, we realised that our context applied a number of constraints on the gamification elements that could be introduced. In this paper, we outline the design and implementation of a gamification model for online communities and its instantiation into our context, with its specific requirements. While we cannot comment on the success of gamification to drive user engagement in our context (for lack of the possibility of a controlled experiment), we found our implementation of badges-based gamification a helpful way to provide a useful abstraction on the life of the community, providing feedback enabling us to monitor and analyze the community. We thus show how feedback provided by such gamification data has a potential to be useful to community providers to better understand the community needs and addressing them appropriately to maintain a level of engagement in the community.

The design of an online community for welfare recipients

We present the design of an online community targeting people currently in receipt of welfare payments to help them find a job and become financially self-sufficient. We developed and deployed this online community in partnership with the Australian Governments Department of Human Services. In this paper, we report on the process of designing this community, its main components and unique features, and the initial data we have collected.

Ethical considerations in an online community: the balancing act

With the emergence and rapid growth of Social Media, a number of government departments in several countries have embraced Social Media as a privilege channel to interact with their constituency. We are exploring, in collaboration with the Australian Department of Human Services, the possibility to exploit the potential of social networks to support specific groups of citizens. To this end, we have developed Next Step, an online community to help people currently receiving welfare payments find a job and become financially self-sufficient. In this paper, we explore some ethical issues that arise when governments engage directly with citizens, in particular with communities in difficult situations, and when researchers are involved. We describe some of the challenges we faced and how we addressed them. Our work highlights the complexity of the problem, when an online community involves a government department and a welfare recipient group with a dependency relationship with that department. It becomes a balancing act, with the need to ensure privacy of the community members whilst still fulfilling the government’s legal responsibilities. While difficult, these issues must be addressed if governments are to engage with their citizens using Social Media.

Next step: an online community to support parents in their transition to work

We present an online community targeting parents currently in receipt of welfare payments to help them return to work. We provide a tour of the online community, explaining some of our design choices and presenting the different (and sometimes unique) features provided to its members.

Engagement and Cooperation in Social Networks: Do Benefits and Rewards Help?

Engagement serves as an important metrics in judging the success of any online social network. It is thus important to identify how specific mechanisms can help build engagement. In this work, we investigate how rewarding members for their honesty can offer better engagement and raise the level of cooperativeness in the society. We define a write-rate game to simulate activities in an online forum like setting and conduct experiments with it. Our results show that providing right reward for honest actions promotes engagement and cooperation in the society.

Behavior-Based Propagation of Trust in Social Networks with Restricted and Anonymous Participation

Increasing interactions and engagements in social networks through monetary and material incentives is not always feasible. Some social networks, specifically those that are built on the basis of fairness, cannot incentivize members using tangible things and thus require an intangible way to do so. In such networks, a personalized recommender could provide an incentive for members to interact with other members in the community. Behavior‐based trust models that generally compute social trust values using the interactions of a member with other members in the community have proven to be good for this. These models, however, largely ignore the interactions of those members with whom a member has interacted, referred to as “friendship effects.” Results from social studies and behavioral science show that friends have a significant influence on the behavior of the members in the community. Following the famous Spanish proverb on friendship “Tell Me Your Friends and I Will Tell You Who You Are,” we extend our behavior‐based trust model by incorporating the “friendship effect” with the aim of improving the accuracy of the recommender system. In this article, we describe a trust propagation model based on associations that combines the behavior of both individual members and their friends. The propagation of trust in our model depends on three key factors: the density of interactions, the degree of separation, and the decay of friendship effect. We evaluate our model using a real data set and make observations on what happens in a social network with and without trust propagation to understand the expected impact of trust propagation on the ranking of the members in the recommended list. We present the model and the results of its evaluation. This work is in the context of moderated networks for which participation is by invitation only and in which members are anonymous and do not know each other outside the community. Copyright

Interaction-Based Recommendations for Online Communities

A key challenge in online communities is that of keeping a community active and alive. All online communities work hard to keep their members through various initiatives, such as personalisation and recommendation technologies. In online communities aimed at supporting behavioural change, that is, in domains such as diet, lifestyle, or the environment, the main reason for participation is not to connect with real-world friends for sharing and communicating, but to meet and gain support from like-minded people in an online environment. Introducing personalisation and recommendation features in these networks is challenging, as traditional approaches leverage the densely populated friendship relations found in typical social networks, and these are not present in these new community types. We address this challenge by looking beyond the articulated friendships of a community for evidence of relationships. In particular, we look at the interactions of members of an online community with other members and resources. In this article, we present a social behaviour model and apply it to two types of recommendation systems, a people recommender and a content recommender system. We evaluate our systems using the interaction logs of an online diet and lifestyle community in which 5,000 Australians participated in a 12-week programme. Our results show that our social behaviour-based recommendation algorithms outperform baselines, friendship-based, and link-prediction algorithms.

Data Abstraction and Visualisation in Next Step: Experiences from a Government Services Delivery Trial

Online Communities offering support services from a government body can attract a large number of participants and thus grow quickly in terms of interaction volume. This big data can pose a challenge to the community members as well as moderators in reviewing the development of discussions and in understanding the social behaviour of the community. For the data to be useful, it is thus important to employ appropriate data abstraction and visualisation tools. We implemented a gamification based data abstraction and social behaviour based visualisation method in the context of Next Step, a year long trial conducted in partnership with the Australian Government Department of Human Services to deliver support services to citizens in a transitioning phase. The trial has been useful in drawing lessons that could be important for a scaled-up implementation of such government services. In this paper, we present our experiences from the implementation and discuss future prospects.