Improving Robustness of Scale-Free Networks to Message Distortion

Vast numbers of organizations and individuals communicate every day by sending messages over social networks. These messages, however, are subject to change as they propagate through the network. This paper attempts to calculate the distortion of a message as it propagates in a social network with a scale free topology, and to establish a remedial process in which a node will correct the distortion during the diffusion process, in order to improve the robustness of scale-free networks to message distortion. We test a model that we created using a simulation of different types of scale-free networks, and we compared different sets of corrective nodes, hubs, regular (non-hubs) nodes, and a combination of hubs and regular nodes. The findings show that using hubs that correct the distorted message while it\u27s diffused, decrease a global error measurement of the distortion, and as a result improve the robustness of the network

Technology of Location Hiding by Spoofing the Mobile Operator IP Address

This paper discusses the issues of hiding geolocation when sending messages on social networks, holding teleconferences, and sending emails. Single-board computers were used to form the experimental setup. One was equipped with an access point and a SIP server, and the other was running a SIP client. The system was tested with different audio codecs. We also checked the substitution of geo-location when working in social networks. In addition, the issues of choosing the optimal codec for SIP clients and the key length are considere

Detecting Online Harassment in Social Networks

Online Harassment is the process of sending messages over electronic media to cause psychological harm to a victim. In this paper, we propose a pattern-based approach to detect such messages. Since user generated texts contain noisy language, we perform a normalization step first to transform the words into their canonical forms. Additionally, we introduce a person identification module that marks phrases which relate to a person. Our results show that these preprocessing steps increase the classification performance. The pattern-based classifier uses the information provided by the preprocessing steps to detect patterns that connect a person to profane words. This technique achieves a substantial improvement compared to existing approaches. Finally, we discuss the portability of our approach to Social Networks and its possible contribution to tackle the abuse of such applications for the distribution of Online Harassment

Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks

Viral campaigns are crucial methods for word-of-mouth marketing in social communities. The goal of these campaigns is to encourage people for activity. The problem of incentivised and non-incentivised campaigns is studied in the paper. Based on the data collected within the real social networking site both approaches were compared. The experimental results revealed that a highly motivated campaign not necessarily provides better results due to overlapping effect. Additional studies have shown that the behaviour of individual community members in the campaign based on their service profile can be predicted but the classification accuracy may be limited.Comment: In proceedings of the 2nd International Conference on Social Computing and its Applications, SCA 201

Competing for attention in social communication markets

We investigate the incentives for social communication in the new social media technologies. Three features of online social communication are represented in the model. First, new social media platforms allow for increased connectivity; i.e., they enable sending messages to many more receivers, for the same fixed cost, compared to traditional word of mouth. Second, users contribute content because they derive status- or image-based utility from being listened to by their peers. Third, we capture the role of social differentiation, or how social distance between people affects their preferences for messages. In the model, agents endogenously decide whether to be a sender of information and then compete for the attention of receivers. An important point of this paper is that social communication incentives diminish even as the reach or the span of communication increases. As the span of communication increases, competition between senders for receiver attention becomes more intense, resulting in senders competing with greater equilibrium messaging effort. This in turn leads to lower equilibrium payoffs and the entry of fewer senders. This result provides a strategic rationale for the socalled participation inequality phenomenon, which is a characteristic of many social media platforms. We also show that social differentiation may enhance or deter sender entry depending on whether it can be endogenously influenced by senders. Finally, we examine how the underlying network structure (in terms of its density and its degree distribution) affects communication and uncover a nonmonotonic pattern in that increased connectivity first increases and then reduces the entry of senders

Reciprocal Recommendation System for Online Dating

Online dating sites have become popular platforms for people to look for potential romantic partners. Different from traditional user-item recommendations where the goal is to match items (e.g., books, videos, etc) with a user's interests, a recommendation system for online dating aims to match people who are mutually interested in and likely to communicate with each other. We introduce similarity measures that capture the unique features and characteristics of the online dating network, for example, the interest similarity between two users if they send messages to same users, and attractiveness similarity if they receive messages from same users. A reciprocal score that measures the compatibility between a user and each potential dating candidate is computed and the recommendation list is generated to include users with top scores. The performance of our proposed recommendation system is evaluated on a real-world dataset from a major online dating site in China. The results show that our recommendation algorithms significantly outperform previously proposed approaches, and the collaborative filtering-based algorithms achieve much better performance than content-based algorithms in both precision and recall. Our results also reveal interesting behavioral difference between male and female users when it comes to looking for potential dates. In particular, males tend to be focused on their own interest and oblivious towards their attractiveness to potential dates, while females are more conscientious to their own attractiveness to the other side of the line