## 1 Introduction  
Opportunistic social networks (OSNs) [1; 2], a type of complex intermittently connected network architecture with a foundation in opportunistic communication and node profiles, have emerged from both a type of delay tolerant network (DTN) [3] and social network service (SNS) [4], and it has been considered a promising network model to improve data transmission reliability. Additionally, the structure in OSNs is characterized by several features such as node mobility, network topology, and social attributes [5]. As reliable and continuous data connectivity can not be available in OSNs, nodes in unpredictable online communities typically take the opportunity of encountering the peers created by their social attributes to communicate with each other at unpredictable intervals [6]. In recent years, the research interests in OSNs mainly consist of the routing-forwarding approach [5], mobility patterns [7], security with privacy [8], and data storage and management [9]. With the increasing expansion of 5G networks and big data environments [10], the high-quality requirements for mass-data multiprocessing have led to a tremendous growth in the number of smart mobile devices, such as Bluetooth devices, smart phones, laptops or other wearable sensor devices [10]. This is more conducive for the construction of opportunistic mobile social networks in urban or remote areas [11]. Moreover, the dawn of big data environments and 5G networks means that the high-fast, low-consumption and low-delay data transmission mode requires higher storage spaces and processing capacity from smart mobile devices. The data packets circulating in peer-to-peer communication may be text messages, pictures, or files such as video that occupy relatively large cache spaces [12]. However, due to the insufficient storage spaces and relatively low processing capacity of smart mobile devices, it is particularly difficult for nodes to implement the peer-to-peer communication between message applications in 5G networks and big data environments. Therefore, opportunistic mobile social networks propose that an end-to-end communication connectivity can be established using several suitable relay nodes to carry out the store-carry-forward mechanism [13], which indicates mobile nodes with smart devices temporarily store data packets in their buffer memory and carry them until they encounter other suitable relay nodes to forward these data packets. Therefore it is extremely important for a node in OSNs to select suitable next hop nodes for data dissemination [14]. As various applicable routing-forwarding strategies [15] have been proposed to tackle the problem of data dissemination for different scenarios in OSNs, most of them make an accurate message delivery decision by comprehensively assessing reliable social information associated with nodes such as social features [6], human mobility [1], contact history [13], or level of credibility. As a general rule, the social features of humans, which can be easily extracted from portable mobile devices, are the most widely utilized metric information for relay node selection in OSNs. On the one hand, as the main carriers of mobile devices, humans in opportunistic mobile social networks commonly present multiple different social attributes, even including several significant features that facilitate the data transmission process [16]; on the other hand, it is practicable to implement the recognition of relay nodes or destinations via the social features of humans in OSNs [17]. Additionally, how to utilize the effective social features of humans to implement an efficient data dissemination process between source nodes and destinations, is one of the most long-standing and elusive challenges in OSNs. With the aim of yielding low overhead, low network delay, and high delivery ratio, multitudinous routing approaches employ various strategies to create a better data dissemination environment in the OSNs.