Featured Articles is a section of the Special Technical Community on Social Networks that will shed light to pieces of social network research that are considered interesting and promising. Each month one research article will be selected from the IEEE Computer Society Digital Library to be featured in this section. The content featured here will be curated by Symeon Papadopoulos. Eventually, the featured articles will become freely available from this page.
Abstract: Automatic image annotation has been extensively studied, mostly from a content-based approach, whose effectiveness is restricted by the “semantic gap” between low-level image features and semantic annotations, and by the irrelevance of annotations to image content. We propose a social diffusion analysis approach to image annotation, which exploits abundant social diffusion records about how images are disseminated within online social networks. Specifically, we propose a common-interest model to analyze social diffusion records, with the assumption that the diffusion pattern of an image in social networks is highly related to the relevance between image annotations and user preferences. In our proposed model, user preferences are represented as common interests of pairwise users instead of individual user interests. We find the notion of common interests not only facilitates the analysis of social diffusion patterns, but also leads to more accurate profiling of user preferences compared to individual interests. Based on the common-interest model, we design an image annotation framework via social diffusion analysis, which consists of the mining of common interests from social diffusion records, the feature extraction from diffusion graphs and common interests, and the automatic annotation by the learning-to-rank method. Experimental results on real-world data sets show that our proposed common-interest based approach outperforms individual-interest based methods, and also achieves superior performance than state-of-the-art content-based image annotation methods.
Chao Chen, Jun Zhang, Yang Xiang, Wanlei Zhou, Jonathan Oliver, "Spammers Are Becoming "Smarter" on Twitter", IT Professional, vol.18, no. 2, pp. 66-70, Mar.-Apr. 2016, doi:10.1109/MITP.2016.36
Abstract: While researchers develop various approaches to detect Twitter spam, spammers thwart their efforts with more complex spamming strategies. The authors' in-depth analysis of more than 570 million tweets revealed three new spamming strategies: coordinated posting behavior, finite-state machine-based spam template, and passive spam.
Gaurav Misra, Jose M. Such, "How Socially Aware Are Social Media Privacy Controls?", Computer, vol.49, no. 3, pp. 96-99, Mar. 2016, doi:10.1109/MC.2016.83
Abstract: Social media sites are key mediators of online communication. Yet the privacy controls for these sites are not fully socially aware, even when privacy management is known to be fundamental to successful social relationships.
Virgilio A.F. Almeida, Danilo Doneda, Yasodara Cordova, "Whither Social Media Governance?", IEEE Internet Computing, vol.20, no. 2, pp. 82-84, Mar.-Apr. 2016, doi:10.1109/MIC.2016.32
Abstract: Due to their enormous popularity, social network platforms such as Facebook, YouTube, and Twitter are constantly viewed as stand-ins for public spaces. Although they have their own rules, there are ethical aspects that demand governance to guarantee their compliance with human rights. This article seeks to explore the essentials that could impact social networks' governance, drawing attention to aspects of possible solutions.
Abstract: With the rapid penetration of mobile devices, more users prefer to watch multimedia live-streaming via their mobile terminals. Quality of service provision is normally a critical challenge in such multimedia sharing environments. In this article, we propose a new cloud-based WMSN to efficiently deal with multimedia sharing and distribution. We first motivate the use of cloud computing and social contexts in sharing live streaming. Then our WMSN architecture is presented with the description of the different components of the network. After that, we focus on distributed resource management and formulate the bandwidth allocation problem in a gametheoretical framework that is further implemented in a distributed manner. In addition, we note the potential selfish behavior of mobile users for resource competition and propose a cheat-proof mechanism to motivate mobile users to share bandwidth. Illustrative results demonstrate the best responses of different users in the game equilibrium as well as the effectiveness of the proposed cheating avoidance scheme.
Lei Li, Jianping He, Meng Wang, Xindong Wu, "Trust Agent-Based Behavior Induction in Social Networks", IEEE Intelligent Systems, vol.31, no. 1, pp. 24-30, Jan.-Feb. 2016, doi:10.1109/MIS.2016.6
Abstract: The essence of social networks is that they can influence people's public opinions and group behaviors form quickly. Negative group behavior influences societal stability significantly, but existing behavior-induction approaches are too simple and inefficient. To automatically and efficiently induct behavior in social networks, this article introduces trust agents and designs their features according to group behavior features. In addition, a dynamics control mechanism can be generated to coordinate participant behaviors in social networks to avoid a specific restricted negative group behavior.
January 2016: Cross-Platform Identification of Anonymous Identical Users in Multiple Social Media Networks
Abstract: The last few years have witnessed the emergence and evolution of a vibrant research stream on a large variety of online social media network (SMN) platforms. Recognizing anonymous, yet identical users among multiple SMNs is still an intractable problem. Clearly, cross-platform exploration may help solve many problems in social computing in both theory and applications. Since public profiles can be duplicated and easily impersonated by users with different purposes, most current user identification resolutions, which mainly focus on text mining of users’ public profiles, are fragile. Some studies have attempted to match users based on the location and timing of user content as well as writing style. However, the locations are sparse in the majority of SMNs, and writing style is difficult to discern from the short sentences of leading SMNs such as Sina Microblog and Twitter. Moreover, since online SMNs are quite symmetric, existing user identification schemes based on network structure are not effective. The real-world friend cycle is highly individual and virtually no two users share a congruent friend cycle. Therefore, it is more accurate to use a friendship structure to analyze cross-platform SMNs. Since identical users tend to set up partial similar friendship structures in different SMNs, we proposed the Friend Relationship-Based User Identification (FRUI) algorithm. FRUI calculates a match degree for all candidate User Matched Pairs (UMPs), and only UMPs with top ranks are considered as identical users. We also developed two propositions to improve the efficiency of the algorithm. Results of extensive experiments demonstrate that FRUI performs much better than current network structure-based algorithms.
Artemis D. Avgerou, Yannis C. Stamatiou, "Privacy Awareness Diffusion in Social Networks", IEEE Security & Privacy, vol.13, no. 6, pp. 44-50, Nov.-Dec. 2015, doi:10.1109/MSP.2015.136
Abstract: Can everyday social interactions establish and maintain privacy awareness? Game theory and an innovative diffusion model show that privacy awareness can be spread to large populations by taking advantage of individuals' social-network connections.
Abstract: Thanks to the availability of user profiles and records of activity, online social network analysis can discover complex individual and social behavior patterns. The emergence of trust between users of online services is one of the most important phenomena, but it's also hard to detect in records of users' interactions, and even harder to replicate by abstract, generative models. Here, the authors investigate the emergence of "trusted" users (over time) by studying the evolution of topological and centrality measures of the network of trust within the overall social network. To do so, large datasets of user activity are studied from Ciao and Epinions (two online platforms with an explicit notion of trust controlled by users), and Prosper (a micro-lending site where trust remains implicit). The implications of such findings are discussed, particularly regarding how to enable trust in online platforms and interaction, with implications for trust-based activities.
Abstract: Community formation analysis of dynamic networks has been a hot topic in data mining which has attracted much attention. Recently, there are many studies which focus on discovering communities successively from consecutive snapshots by considering both the current and historical information. However, these methods cannot provide us with much historical or successive information related to the detected communities. Different from previous studies which focus on community detection in dynamic networks, we define a new problem of tracking the progression of the community strength—a novel measure that reflects the community robustness and coherence throughout the entire observation period. To achieve this goal, we propose a novel framework which formulates the problem as an optimization task. The proposed community strength analysis also provides foundation for a wide variety of related applications such as discovering how the strength of each detected community changes over the entire observation period. To demonstrate that the proposed method provides precise and meaningful evolutionary patterns of communities which are not directly obtainable from traditional methods, we perform extensive experimental studies on one synthetic and five real datasets: Social evolution, tweeting interaction, actor relationships, bibliography, and biological datasets. Experimental results show that the proposed approach is highly effective in discovering the progression of community strengths and detecting interesting communities.
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