Information Propagation In Online Social Networks Research Articles

Toward pragmatic modeling of privacy information propagation in online social networks

The growing popularity of online social networks (OSNs) in recent years has generated a lot of concern on personal privacy. One approach of protecting privacy in OSNs is to intervene in the flow of privacy information, making the study of the dynamics of privacy information propagation necessary for the design of effective privacy protection mechanisms. Although previous work on information propagation has produced some models, these models are not adequate for privacy information since they do not reflect the main characteristics of privacy information. In this paper, we propose a model for privacy information propagation. We first analyze the structural characteristics of privacy information and then design the model by incorporating these characteristics. A unique feature of the model is that it infers the privacy attitudes of the information recipients to the privacy concerning subject implicated in the privacy information to determine the forwarding decisions of the recipients. Thus, by mapping the heterogeneous tendency of information forwarding by the recipients to a limited number of privacy attitudes, the model can predict the decisions on forwarding privacy information and thus describe the macroscopic process of privacy information propagation. Results of the experiment based on real OSN datasets show that the proposed model can be used to learn both the scope and the trend of privacy information propagation in OSNs, demonstrating the importance of the privacy attitudes of recipients on privacy information propagation. The properties of the model are also studied through experiment to examine the impact of various factors on privacy information propagation in OSNs.

Topic based time-sensitive influence maximization in online social networks

With the exponential expansion of online social networks (OSNs), an extensive research on information diffusion in OSNs has been emerged in recent years. One of the key research is influence maximization (IM). IM is a problem to find a seed set with k nodes, so as to maximize the range of information propagation in OSNs. Most research adopts general greedy, degree discount, and centrality measures, etc. to find the seed node set. However, the time sensitivity is ignored in terms of propagation delay and duration under some specific situations such as emergent epidemiological bulletins and natural disaster warning. In the meantime, prior studies have mainly based on static network topology, but the user’s online/offline status and the topic preference of interaction make it difficult for traditional methods to be applied well in reality. Considering the above analysis, in this paper, we focus on the problem of time-sensitive influence maximization and give its formal description. Here, a dynamic network is constructed in terms of users’ online status. To solve the problem, we propose a topic based time-sensitive dynamic propagation model considering user topic preference and interaction delay. Besides, two algorithms are used in our work to find a seed set that could maximize the influence in OSNs with the time constraint, namely topic-based time-sensitive greedy algorithm (TTG) and topic-based time-sensitive heuristic algorithm (TTH), respectively. Extensive experiments on Twitter dataset demonstrate the efficiency and influence performance of the proposed algorithms on evaluation metrics.

SEIM: Search economics for influence maximization in online social networks

The influence of online social networks (OSN), which can be regarded as part of our life, is evident today. As expected, a great deal of useful information about the humans is hidden in the data, such as interpersonal relationship and personal preference. The influence maximization problem (IMP) is one of the well-known problems in this research domain that has attracted the attention of researchers from different disciplines in recent years. One of the reasons is that it can speed up the propagation of information in OSN if we can find out users that have maximum influence on other users. However, traditional rule-based and heuristic algorithms may not be able to find useful information out of these data because the data are generally large and complex. Although many recent studies attempted to use metaheuristic algorithms to solve the IMP, there is still plenty of room for improvement. The proposed algorithm, called search economics for influence maximization (SEIM), is motivated by the concept of return on investment to design its search strategies. As far as the proposed algorithm is concerned, the search strategy of SEIM is like making a good plan to determine how to invest the high potential investment subjects (i.e., regions) in the market (i.e., search space). The experimental results show that the proposed algorithm is significantly better than the other state-of-the-art influence maximization problem algorithms compared in this paper in terms of the quality of the end result and the number of objective function evaluations.

Scalable influence blocking maximization in social networks under competitive independent cascade models

Bad information propagation in online social networks (OSNs) can cause undesirable effects. The opposite good information propagating competitively with bad information can restrain the propagation of bad information. In this paper, we address the Influence Blocking Maximization (IBM) problem aiming to find a set of influential people initiating good information propagation to maximize the blocking effect on the bad information propagation in OSNs. The problem is studied on two competitive propagation models describing competitive propagation processes in two classic situations in OSNs. Two models are derived from the Independent Cascade Model (ICM). Greedy algorithms for IBM problem under two competitive propagation models are slow and not scalable. Thus, we design two heuristics CMIA-H and CMIA-O based on the maximum influence arborescence (MIA) structure to efficiently solve the IBM problem under two competitive propagation models, respectively. Extensive experiments are conducted on real-world and synthetic datasets to compare the proposed algorithms with the greedy algorithms and other baseline heuristics. The results demonstrate that both CMIA-H and CMIA-O achieve matching influence blocking performance to the greedy algorithms and consistently outperform other baseline heuristics, while they are several orders of magnitude faster than the greedy algorithms.

On Charactering of Information Propagation in Online Social Networks

Online social networks (OSNs) are becoming an important propagation platform for Word of mouth (WOM). Therefore, it is of great significance to study the propagation of WOM in OSNs. A WOM propagation model named N-P-N is proposed in this paper, and some simulation experiments are carried out to investigate the mechanism of WOM propagation. From the sensitivity analysis of degree of initial information source node, it can be seen that the degree of initial information source node determines the scope and speed of the propagation of WOM in OSNs in some extent. Then the sensitivity analysis of number of initial information source nodes shows that the initial source nodes are crucial for controlling the propagation of negative information in OSNs. Moreover, from the user behavior respect, it is found that different user behavior in OSNs causes different propagation results, the more users who are willing to diffuse WOM, the more scope WOM can propagate and the faster the information diffuses. Findings in this paper are helpful for enterprises to form an effective WOM.