Distrust Relations Research Articles

COMPLEX CONTAGION IN SOCIAL SYSTEMS WITH DISTRUST

Social systems are characterized by the presence of group interactions and by the existence of both trust and distrust relations. Although there is a wide literature on signed social networks, where positive signs associated to the links indicate trust, friendship, agreement, while negative signs represent distrust, antagonism, and disagreement, very little is known about the effect that signed interactions can have on the spreading of social behaviors when, not only pairwise, but also higher-order interactions are taken into account. In this paper, we introduce a model of complex contagion on signed simplicial complexes, and we investigate the role played by trust and distrust on the dynamics of a social contagion process, where exposure to multiple sources is needed for the contagion to occur. The presence of higher-order signed structures in our model naturally induces new infection and recovery mechanisms, thus increasing the richness of the contagion dynamics. Through numerical simulations and analytical results in the mean-field approximation, we show how distrust determines the way the system moves from a state where no individuals adopt the social behavior, to a state where a finite fraction of the population actively spreads it. Interestingly, we observe that the fraction of spreading individuals displays a non-monotonic dependence with respect to the average number of connections between individuals. We then investigate how social balance affects social contagion, finding that balanced triads have an ambivalent impact on the spreading process, either promoting or impeding contagion based on the relative abundance of fully trusted relations. Our results shed light on the nontrivial effect of trust on the spreading of social behaviors in systems with group interactions, paving the way to further investigations of spreading phenomena in structured populations.

Joint item recommendation and trust prediction with graph neural networks

Item recommendation and trust prediction are desired by users on social network platforms since they can help users find their favourite items or friends faster. Existing methods usually utilize users’ social relationships to facilitate the item recommendation task and leverage users’ preferences to assist the trust prediction task. While sociological researchers have shown that users’ preferences and users’ social relationships are not isolated but influenced by each other, there are a few studies that model these two tasks jointly. However, in these studies, the incorporation of distrust relations and the dynamic item-specific mutual influence between users’ preferences and users’ social relationships are ignored. In this paper, we propose a joint learning method using graph neural networks for item recommendation and trust prediction tasks, named JoRTGNN. First, users’ distrust relations are incorporated along with trust relations to extract more potential user social relationships, which can benefit both the item recommendation and trust prediction tasks. In addition, a dynamic item-specific mutual influence between users’ preferences and users’ social relationships is highlighted with attention mechanisms for the joint learning tasks. Experiments have been conducted on the Epinions datasets, and the experimental results show a superior performance of JoRTGNN over baselines in item recommendation and trust prediction tasks, which demonstrates that item recommendation and trust prediction can be effectively improved in the process of the mutual influence between users’ preferences and users’ social relationships.

Etiyopya'nın Osmanlılarla İlişkisi: Düşmanlıktan Anlayışa

This study examines the long-standing hostile and distrustful relations between Ethiopia and the Ottoman Empire. The Ethio-Ottoman antagonistic relation was not coincident; somewhat, the interest of the Ottoman to supervise the political, economic, and religious life of the Red Sea and Northeast Africa regions primarily stemmed the hostility. The presence of the Portuguese in the Red-Sea region and its subsequent alignment with Ethiopia further worsened the Ethio-Ottoman belligerency. The positioning of the Ottomans with the Adal Sultanate had also complicated the Ethio-Ottoman relations and brought with them generations of distrust. This article recounts the dynamics of the Ethio-Ottoman relations, mainly focusing on the changing aspects of hostility and understanding. The paper tries to address the Ethio-Ottoman relations in three major sections. The first section of the study explains the root of the Ethio-Ottomans' hostile and distrustful relations. The second section of the paper is devoted to showing the dynamic of the essence of the Ethio-Ottoman relations in the seventeenth, eighteenth, and nineteenth centuries. The last section elucidates the Ethio-Ottoman connections during the era of Sultan Abdülhamit II. The study employs an analytical, interpretive approach to analyze archival documents and secondary works of literature.

France and Russia from 1892 to the Present Day: Looking Back on a “Sinusoidal” Relationship

The contribution endeavors to demonstrate that Franco-Russian relations in the long run have never been linear and that despite a cultural proximity linked to the Francophonie and Francophilia of the Russian elites, political relations have been much more fraught and even conflictual. However, from the last third of the 19th century, a rapprochement took place which led to a military alliance that was brutally ended by the October Revolution. Decades of distrustful and tense relations followed, before General de Gaulle, from 1965—1966, embarked on a policy of détente, understanding and cooperation beyond the differences of political regimes. It is this Gaullian “paradigm” that remains the key word in Franco-Russian relations to this day.

Trust Prediction via Matrix Factorisation

In this article, we propose the PTP-MF (Pairwise Trust Prediction through Matrix Factorisation) algorithm, an approach to predicting the intensity of trust and distrust relations in Online Social Networks (OSNs). Our algorithm maps each OSN user i onto two low-dimensional vectors, namely, the trustor profile (describing her/his inclination to trust others) and the trustee profile (modelling how others perceive i as trustworthy) and it computes the trust a user i places in a user j as the dot product of trustor profile of i and the trustee profile of j . The PTP-MF algorithm incorporates also biases in trustor and trustee behaviour to make more accurate predictions. Experiments on four real-life datasets indicate that the PTP-MF algorithm significantly outperforms other methods in accuracy and it showcases a high scalability.

Distrust Prediction in Signed Social Network

In social networks, the most studies focus on the trust prediction, but distrust cannot get enough attention. The distinct characteristics of distrust relations present challenges to traditional relation prediction. Distrust relations are very sparse in social network, and negative interaction data is too little. We embark on the problem to investigate the distrust prediction with only network topology. After achieving seven social distrusting-inducing factors, we adopt machine learning and optimization methods to model the prediction process. The framework of Distrust prediction in Signed social network (DP-SSN) is proposed, which can predict distrust relations without any interaction data. Empirically, we perform extensive experiments on real-world data to corroborate the effectiveness of the proposed framework.

Leveraging Distrust Relations to Improve Bayesian Personalized Ranking

Distrust based recommender systems have drawn much more attention and became widely acceptable in recent years. Previous works have investigated using trust information to establish better models for rating prediction, but there is a lack of methods using distrust relations to derive more accurate ranking-based models. In this article, we develop a novel model, named TNDBPR (Trust Neutral Distrust Bayesian Personalized Ranking), which simultaneously leverages trust, distrust, and neutral relations for item ranking. The experimental results on Epinions dataset suggest that TNDBPR by leveraging trust and distrust relations can substantially increase various performance evaluations including F1 score, AUC, Precision, Recall, and NDCG.

Socialized healthcare service recommendation using deep learning

Socialized recommender system recommends reliable healthcare services for users. Ratings are predicted on the healthcare services by merging recommendations given by users who has social relations with the active users. However, existing works did not consider the influence of distrust between users. They recommend items only based on the trust relations between users. We therefore propose a novel deep learning-based socialized healthcare service recommender model, which recommends healthcare services with recommendations given by recommenders with both trust relations and distrust relations with the active users. The influences of recommenders, considering both the node information and the structure information, are merged via the deep learning model. Experimental results show that the proposed model outperforms the existing works on prediction accuracy and prediction coverage simultaneously, even for cold start users or users with very sparse trust relations. It is also computational less expensive.

A novel collective matrix factorization model for recommendation with fine‐grained social trust prediction

SummaryRecommender systems are playing an increasing role in improving user satisfaction as they can recommend items which might be highly interested to users. Recent advances have proven that social relations such as trust and distrust relations among users are helpful in improving recommendation accuracy. Traditional social recommendation methods directly utilize unweighted trust and distrust relations into collaborative filtering framework. These methods will lose their power when the trust or distrust relation data is sparse, which significantly hinders the improvement of rating prediction accuracy. To address this problem, we transform the unweighted trust and distrust relations into fine‐grained weighted social trust matrix which is denser and encodes the trust and distrust degree for pair of users. The weighted social trust matrix is then combined with the rating matrix in a collective matrix factorization framework to implement rating prediction task. Experimental results based on Extended Epinions dataset show that the proposed collective matrix factorization model with fine‐grained weighted social trust matrix can achieve better accuracy than conventional social recommendation algorithms such as SoRec and its extensions.

‘I won’t take part!’: Exploring the multiple identities of the ethnographer in two Ghanaian hospitals

This article reflects on how an ethnographer’s approach to field entry, the topic of study, her use of ethnographic methods and contextual factors shaped research participants’ perception of her multiple identities in a Ghanaian hospital environment. Her perceived multiple identities influenced trust and distrust relations between her and her research participants, which impacted on the research project. The article discusses the paradox of the researcher’s negotiation of her multiple identities of ‘insider’ and ‘outsider’ and its influence on the process of data collection, understanding and analysis of the study topic. The article argues that research projects give birth to the ethnographer. Obviously, the observations of the ethnographer, who is the main research instrument, of her own position, are an important source for data collection. Participation and positioning in organizational activities can provide the ethnographer with a personal experience of her study topic and data that enhances understanding of organizational processes.

Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust.

In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.

Distrust in Dependence: The Ancient Challenge of Superior-Subordinate Relations

The oldest surviving advice about management practices concerns leaders’ deceptions of followers. Tension-filled, distrustful relations between leaders and followers pervade the ancient texts. Drawing on the oldest surviving documents, this chapter reviews these common issues and people’s responses to them. The issues encompass: (a) how much leaders and followers should trust each other and speak forthrightly to each other, (b) how leaders manipulate followers and followers manipulate leaders, (c) how much followers respect leaders and leaders respect followers, (d) whether status differences are just, and (e) when leaders act appropriately.

'I Used to Fight with Them but Now I Have Stopped!': Conflict and Doctor-Nurse-Anaesthetists' Motivation in Maternal and Neonatal Care Provision in a Specialist Referral Hospital.

Background and ObjectivesThis paper analyses why and how conflicts occur and their influence on doctors and nurse-anaesthetists' motivation in the provision of maternal and neonatal health care in a specialist hospital.MethodologyThe study used ethnographic methods including participant observation, conversation and in-depth interviews over eleven months in a specialist referral hospital in Ghana. Qualitative analysis software Nvivo 8 was used for coding and analysis of data. Main themes identified in the analysis form the basis for interpreting and reporting study findings.Ethics StatementEthical clearance was obtained from the Ghana Health Service Ethics Review board (approval number GHS-ERC:06/01/12) and from the University of Wageningen. Written consent was obtained from interview participants, while verbal consent was obtained for conversations. To protect the identity of the hospital and research participants pseudonyms are used in the article and the part of Ghana in which the study was conducted is not mentioned.ResultsIndividual characteristics, interpersonal and organisational factors contributed to conflicts. Unequal power relations and distrust relations among doctors and nurse-anaesthetists affected how they responded to conflicts. Responses to conflicts including forcing, avoiding, accommodating and compromising contributed to persistent conflicts, which frustrated and demotivated doctors and nurse-anaesthetists. Demotivated workers exhibited poor attitudes in collaborating with co-workers in the provision of maternal and neonatal care, which sometimes led to poor health worker response to client care, consequently compromising the hospital's goal of providing quality health care to clients.ConclusionTo improve health care delivery in health facilities in Ghana, health managers and supervisors need to identify conflicts as an important phenomenon that should be addressed whenever they occur. Effective mechanisms including training managers and health workers on conflict management should be put in place. Additionally promoting communication and interaction among health workers can foster team spirit. Also resolving conflicts using the collaborating response may help to create a conducive work environment that will promote healthy work relations, which can facilitate the delivery of quality maternal and neonatal health care. However, such an approach requires that unequal power relations, which is a root cause of the conflicts is addressed.

Matrix Factorization with Explicit Trust and Distrust Side Information for Improved Social Recommendation

With the advent of online social networks, recommender systems have became crucial for the success of many online applications/services due to their significance role in tailoring these applications to user-specific needs or preferences. Despite their increasing popularity, in general, recommender systems suffer from data sparsity and cold-start problems. To alleviate these issues, in recent years, there has been an upsurge of interest in exploiting social information such as trust relations among users along with the rating data to improve the performance of recommender systems. The main motivation for exploiting trust information in the recommendation process stems from the observation that the ideas we are exposed to and the choices we make are significantly influenced by our social context. However, in large user communities, in addition to trust relations, distrust relations also exist between users. For instance, in Epinions, the concepts of personal “web of trust” and personal “block list” allow users to categorize their friends based on the quality of reviews into trusted and distrusted friends, respectively. Hence, it will be interesting to incorporate this new source of information in recommendation as well. In contrast to the incorporation of trust information in recommendation which is thriving, the potential of explicitly incorporating distrust relations is almost unexplored. In this article, we propose a matrix factorization-based model for recommendation in social rating networks that properly incorporates both trust and distrust relationships aiming to improve the quality of recommendations and mitigate the data sparsity and cold-start users issues. Through experiments on the Epinions dataset, we show that our new algorithm outperforms its standard trust-enhanced or distrust-enhanced counterparts with respect to accuracy, thereby demonstrating the positive effect that incorporation of explicit distrust information can have on recommender systems.