Families and Networks of Internet Memes: The Relationship Between Cohesiveness,Uniqueness, and Quiddity Concreteness

This study employs a large‐scale quantitative analysis to reveal structural patterns of internet memes, focusing on 2 forces that bind them together: the quiddities of each meme family and the generic attributes of the broader memetic sphere. Using content and network analysis of 1013 meme instances (including videos, images, and text), we explore memes' prevalent quiddity types and generic features, and the ways in which they relate to each other. Our findings show that (a) higher cohesiveness of meme families is associated with a greater uniqueness of their generic attributes; and (b) the concreteness of meme quiddities is associated with cohesiveness and uniqueness. We discuss the implications of these findings to the understanding of internet memes and participatory culture.     

Social welfare maximization in participatory smartphone sensing

Participatory smartphone sensing has lately become more and more popular as a new paradigm for performing large-scale sensing, in which each smartphone contributes its sensed data for a collaborative sensing application. Most existing studies consider that smartphone users are strictly strategic and completely rational, which try to maximize their own payoffs. A number of incentive mechanisms are designed to encourage smartphone users to participate, which can achieve only suboptimal system performance. However, few existing studies can maximize a system-wide objective which takes both the platform and smartphone users into account. This paper focuses on the crucial problem of maximizing the system-wide performance or social welfare for a participatory smartphone sensing system. There are two great challenges. First, the social welfare maximization cannot be realized on the platform side because the cost of each user is private and unknown to the platform in reality. Second, the participatory sensing system is a large-scale real-time system due to the huge number of smartphone users who are geo-distributed in the whole world. A price-based decomposition framework is proposed in our previous work (Liu and Zhu, 2013), in which the platform provides a unit price for the sensing time spent by each user and the users return the sensing time via maximizing the monetary reward. This pricing framework is an effective incentive mechanism as users are motivated to participate for monetary rewards from the platform. In this paper, we propose two distributed solutions, which protect users’ privacy and achieve optimal social welfare. The first solution is designed based on the Lagrangian dual decomposition. A poplar iterative gradient algorithm is used to converge to the optimal value. Moreover, this distributed method is interpreted by our pricing framework. In the second solution, we first equivalently convert the original problem to an optimal pricing problem. Then, a distributed solution under the pricing framework via an efficient price-updating algorithm is proposed. Experimental results show that both two distributed solutions can achieve the maximum social welfare of a participatory smartphone system.     

Viral marketing for dedicated customers

Viral marketing has attracted considerable concerns in recent years due to its novel idea of leveraging the social network to propagate the awareness of products. Specifically, viral marketing first targets a limited number of users (seeds) in the social network by providing incentives, and these targeted users would then initiate the process of awareness spread by propagating the information to their friends via their social relationships. Extensive studies have been conducted for maximizing the awareness spread given the number of seeds (the Influence Maximization problem). However, all of them fail to consider the common scenario of viral marketing where companies hope to use as few seeds as possible yet influencing at least a certain number of users. In this paper, we propose a new problem, called J-MIN-Seed, whose objective is to minimize the number of seeds while at least J users are influenced. J-MIN-Seed, unfortunately, is NP-hard. Therefore, we develop an approximate algorithm which can provide error guarantees for J-MIN-Seed. We also observe that all existing studies on viral marketing assume that all users in the social network are of interest for the product being promoted (i.e., all users are potential consumers of the product), which, however, is not always true. Motivated by this phenomenon, we propose a new paradigm of viral marketing where the company can specify which types of users in the social network are of interest when promoting a specific product. Under this new paradigm, we re-define our J-MIN-Seed problem as well as the Influence Maximization problem and design some algorithms with provable error guarantees for the new problems. We conducted extensive experiments on real social networks which verified the effectiveness of our algorithms.     

Memes in a Digital World: Reconciling with a Conceptual Troublemaker

This paper re‐examines the concept of “meme” in the context of digital culture. Defined as cultural units that spread from person to person, memes were debated long before the digital era. Yet the Internet turned the spread of memes into a highly visible practice, and the term has become an integral part of the netizen vernacular. After evaluating the promises and pitfalls of memes for understanding digital culture, I address the problem of defining memes by charting a communication‐oriented typology of 3 memetic dimensions: content, form, and stance. To illustrate the utility of the typology, I apply it to analyze the video meme “Leave Britney Alone.” Finally, I chart possible paths for further meme‐oriented analysis of digital content.     

A multiscale approach to network event identification using geolocated twitter data

The large volume of data associated with social networks hinders the unaided user from interpreting network content in real time. This problem is compounded by the fact that there are limited tools available for enabling robust visual social network exploration. We present a network activity visualization using a novel aggregation glyph called the clyph. The clyph intuitively combines spatial, temporal, and quantity data about multiple network events. We also present several case studies where major network events were easily identified using clyphs, establishing them as a powerful aid for network users and owners.     

Discovering and tracking query oriented active online social groups in dynamic information network

The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies.

     

Personalization on a peer-to-peer television system

We introduce personalization on Tribler, a peer-to-peer (P2P) television system. Personalization allows users to browse programs much more efficiently according to their taste. It also enables to build social networks that can improve the performance of current P2P systems considerably, by increasing content availability, trust and the realization of proper incentives to exchange content. This paper presents a novel scheme, called BuddyCast, that builds such a social network for a user by exchanging user interest profiles using exploitation and exploration principles. Additionally, we show how the interest of a user in TV programs can be predicted from the zapping behavior by the introduced user-item relevance models, thereby avoiding the explicit rating of TV programs. Further, we present how the social network of a user can be used to realize a truly distributed recommendation of TV programs. Finally, we demonstrate a novel user interface for the personalized peer-to-peer television system that encompasses a personalized tag-based navigation to browse the available distributed content. The user interface also visualizes the social network of a user, thereby increasing community feeling which increases trust amongst users and within available content and creates incentives of to exchange content within the community.     

Co-occurrence prediction in a large location-based social network

Location-based social network (LBSN) is at the forefront of emerging trends in social network services (SNS) since the users in LBSN are allowed to “check-in” the places (locations) when they visit them. The accurate geographical and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co-occurrences and social ties, and the results show that the co-occurrences are strongly correlative with the social ties. Second, we present a study of predicting two users whether or not they will meet (co-occur) at a place in a given future time, by exploring their check-in habits. In particular, we first introduce two new concepts, bag-of-location and bag-of-time-lag, to characterize user’s check-in habits. Based on such bag representations, we define a similarity metric called habits similarity to measure the similarity between two users’ check-in habits. Then we propose a machine learning formula for predicting co-occurrence based on the social ties and habits similarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.     

Detecting Communities in <Emphasis Type="Italic">K</Emphasis>-Partite <Emphasis Type="Italic">K</Emphasis>-Uniform (Hyper)Networks

In social tagging systems such as Delicious and Flickr,users collaboratively manage tags to annotate resources.Naturally,a social tagging system can be modeled as a (user,tag,resource) hypernetwork,where there are three different types of nodes,namely users,resources and tags,and each hyperedge has three end nodes,connecting a user,a resource and a tag that the user employs to annotate the resource.Then how can we automatically cluster related users,resources and tags,respectively? This is a problem of community detection in a 3-partite,3-uniform hypernetwork.More generally,given a K-partite K-uniform (hyper)network,where each (hyper)edge is a K-tuple composed of nodes of K different types,how can we automatically detect communities for nodes of different types? In this paper,by turning this problem into a problem of finding an efficient compression of the (hyper)network’s structure,we propose a quality function for measuring the goodness of partitions of a K-partite K-uniform (hyper)network into communities,and develop a fast community detection method based on optimization.Our method overcomes the limitations of state of the art techniques and has several desired properties such as comprehensive,parameter-free,and scalable.We compare our method with existing methods in both synthetic and real-world datasets.     

Node reactivation model to intensify influence on network targets

In this paper, we study a novel problem of influence maximization on social networks: Given a period of promotion time, a set of target users and a network in which each node can be activated by its neighbors multiple times, we aim at determining the k most influential seeds to maximize the total frequency of activations received by these target users. The promising viral marketing paradigm on social network is different from the current research in two main aspects. First, instead of maximizing the message spread over the entire social network, we focus on the target market since the business vendors almost specify the group of target users before designing its marketing strategy. Second, the status of a user is no longer a binary indicator representing either active or inactive. In the new model, the user status turns to be an integer value reflecting the amount of influences delivered to that user. In this paper, we prove the NP-hard nature of this challenging problem. We further present several strategies, including an efficient heuristic algorithm based on the simulated annealing optimization concept and a greedy algorithm as the baseline, to select the initial k seeds in pursuit of resulting quality close to the optimal one. As demonstrated in the empirical study on real data, instead of only providing the flexibility of striking a compromise between the execution efficiency and the resulting quality, our proposed heuristic algorithm can achieve high efficiency and meanwhile can obtain the target acceptance frequency even better than the greedy result in some cases, demonstrating its prominent feasibility to resolve the challenging problem efficiently.     

Where Do Local Experts Go? Evaluating User Geo-Topical Similarity for Top-<Emphasis Type="Italic">N</Emphasis> Place Recommendation

Recent years have witnessed the ever-growing popularity of location-based social network (LBSN) services. Top-N place recommendation, which aims at retrieving N most appealing places for a target user, has thus gained increasing importance. Yet existing solutions to this problem either provide non-personalized recommendations by selecting nearby popular places, or resort to collaborative filtering (CF) by treating each place as an independent item, overlooking the geographical and semantic correlations among places. In this paper, we propose GoTo, a collaborative recommender that provides top-N personalized place recommendation in LBSNs. Compared with existing methods, GoTo achieves its effectiveness by exploiting the wisdom of the so-called local experts, namely those who are geographically close and have similar preferences with regard to a certain user. At the core of GoTo lies a novel user similarity measure called geo-topical similarity, which combines geographical and semantic correlations among places for discovering local experts. In specific, the geo-topical similarity uses Gaussian mixtures to model users’ real-life geographical patterns, and extracts users’ topical preferences from the attached tags of historically visited places. Extensive experiments on real LBSN datasets show that compared with baseline methods, GoTo can improve the performance of top-N place recommendation by up to 50% in terms of accuracy.     

Friends Classification of Ego Network Based on Combined Features

Ego networks consist of a user and his/her friends and depending on the number of friends a user has, makes them cumbersome to deal with. Social Networks allow users to manually categorize their “circle of friends”, but in today’s social networks due to the unlimited number of friends a user has, it is imperative to find a suitable method to automatically administrate these friends. Manually categorizing friends means that the user has to regularly check and update his circle of friends whenever the friends list grows. This may be time consuming for users and the results may not be accurate enough. In this paper, to solve this problem, we present a method, which combining user attributes, network structure and contact frequent three aspects. Efficiently using the profile of users, we first identify the relationship between them and then we attempt to solve the problem of community identification when a user’s profile is missing or inaccessible by use of ego network structural features. Lastly, to obtain more accurate results and realize updates automatically, we attempt to find those friends who have frequent contacts with the user. We compare the performance of the proposed algorithm with other methods, and the results show that our method has significant advantages to them.     

Efficient load-balancing routing for wireless mesh networks

Wireless mesh networks (WMNs) consist of static wireless routers, some of which, called gateways, are directly connected to the wired infrastructure. User stations are connected to the wired infrastructure via wireless routers. This paper presents a simple and effective management architecture for WMNs, termed configurable access network (CAN). Under this architecture, the control function is separated from the switching function, so that the former is performed by an network operation center (NOC) which is located in the wired infrastructure. The NOC monitors the network topology and user performance requirements, from which it computes a path between each wireless router and a gateway, and allocates fair bandwidth for carrying the associated traffic along the selected route. By performing such functions in the NOC, we offload the network management overhead from wireless routers, and enable the deployment of simple/low-cost wireless routers. Our goal is to maximize the network utilization by balancing the traffic load, while providing fair service and quality of service (QoS) guarantees to the users. Since, this problem is NP-hard, we devise approximation algorithms that provide guarantees on the quality of the approximated solutions against the optimal solutions. The simulations show that the results of our algorithms are very close to the optimal solutions.     

Classification of adaptive memetic algorithms: a comparative study.

Adaptation of parameters and operators represents one of the recent most important and promising areas of research in evolutionary computations; it is a form of designing self-configuring algorithms that acclimatize to suit the problem in hand. Here, our interests are on a recent breed of hybrid evolutionary algorithms typically known as adaptive memetic algorithms (MAs). One unique feature of adaptive MAs is the choice of local search methods or memes and recent studies have shown that this choice significantly affects the performances of problem searches. In this paper, we present a classification of memes adaptation in adaptive MAs on the basis of the mechanism used and the level of historical knowledge on the memes employed. Then the asymptotic convergence properties of the adaptive MAs considered are analyzed according to the classification. Subsequently, empirical studies on representatives of adaptive MAs for different type-level meme adaptations using continuous benchmark problems indicate that global-level adaptive MAs exhibit better search performances. Finally we conclude with some promising research directions in the area.     

Identification and characterisation of Facebook user profiles considering interaction aspects

ABSTRACT

The great number of social network users and the expansion of this kind of tool in the last years demand the storage of a great volume of information regarding user behaviour. In this article, we utilise interaction records from Facebook users and metrics from complex networks study, to identify different user behaviours using clustering techniques. We found three different user profiles regarding interactions performed in the social network: viewer, participant and content producer. Moreover, the groups we found were characterised by the C4.5 decision-tree algorithm. The 'viewer' mainly observes what happens in the network. The ‘participant’ interacts more often with the content, getting a higher value of closeness centrality. Therefore, users with a participant profile are responsible, for example, for the faster transmission of information in the virtual environment, a crucial function for the Facebook social network. We noted too that ‘content producer’ users had a greater quantity of publications in their pages, leading to a superior degree of input interactions than the other two profiles. Finally, we also verify that the profiles are not mutually exclusive, that is, the user of a profile can at determined moment perform the behaviour of another profile.     

Cultural differences,experience with social networks and the nature of “true commitment” in Facebook

Formed on an analysis of design practices, the behaviour chain model stipulates that social network designer’s ultimate aim is to encourage users to adopt the social network site by entering a phase of true commitment. During this phase, social network users are driven to connect to known or unknown others by engaging in instrumental uses that create value and content and involve others, while staying active and loyal by investing time in the site. This paper investigates how designer’s intentions, as captured by the behaviour chain model, materialise through users’ reported practices in the social network site Facebook. A total of 423 Facebook users from 5 countries answered a questionnaire that allowed us to examine how 2 user characteristics, experience with the site, and culture, shape the nature of true commitment. Our findings show that experience with the site and even more so, culture, have an effect on users’ motivations for using Facebook, as well as their instrumental uses and the time they invest on the site. This analysis reifies the behaviour chain model by allowing designers to understand how the features they design are embodied in users’ practices.     

Estimating meme fitness in adaptive memetic algorithms for combinatorial problems

Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated. Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling. The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search. The results also show that local reward schemes outperform global reward schemes in combinatorial spaces, unlike in continuous spaces. An analysis of evolving meme behaviour is used to explain these findings.     

SPGProfile: Speak Group Profile

In this paper, we propose an XML-based recommender system, called SPGProfile. It is a type of collaborative information filtering system. SPGProfile uses ontology-driven social networks, where nodes represent social groups. A social group is an entity that defines a group based on demographic, ethnic, cultural, religious, age, or other characteristics. In the SPGProfile framework, query results are filtered and ranked based on the preferences of the social groups to which the user belongs. If the user belongs to social group G_x, results will be filtered based on the preferences of G_x and the preferences of each ancestor social group of G_x in the social network. SPGProfile can be used for various practical applications, such as Internet or other businesses that market preference-driven products. In the ontology, the preferences of a social group are identified from either: (1) the preferences of its member users or (2) from published studies about the social group. We describe and experimentally compare these two approaches. We also experimentally evaluate the search effectiveness and efficiency of SPGProfile and compare it to two existing search engines.     

Not Even Past: Information Aging and Temporal Privacy in Online Social Networks

Online social networks (OSNs) make information accessible for unlimited periods and provide easy access to past information by arranging information in time lines or by providing sophisticated search mechanisms. Despite increased concerns over the privacy threat that is posed by digital memory, there is little knowledge about retrospective privacy: the extent to which the age of the exposed information affects sharing preferences. In this article, we investigate how information aging impacts users’ sharing preferences on Facebook. Our findings are based on a between-subjects experiment (n = 272), in which we measured the impact of time since first publishing an OSN post on its sharing preferences. Our results quantify how willingness to share is lower for older Facebook posts and show that older posts have lower relevancy to the user’s social network and are less representative of the user’s identity. We show that changes in the user’s social circles, the occurrence of significant life changes and a user’s young age are correlated with a further decrease in the willingness to keep sharing past information. We discuss our findings by juxtaposing digital memory theories and privacy theories and suggest a vision for mechanisms that can help users manage longitudinal privacy.     

How do we encourage an egoist to act socially in an ad hoc mobile network?

We consider a flow-based model for an ad hoc mobile network where users may need to use transit nodes in order to be able to communicate. Under the assumption that every node is willing to cooperate, we derive the set of Karush–Kuhn–Tucker equations that define the socially-optimal flows on each of the routes. We then look at the problem from an ‘egoist’ point of view, in which the user at node i cares only about maximising his/her utility under the constraint that the flows on routes which do not use node i are fixed at the socially-optimal value.This leads us to a consideration of extra constraints that could be introduced to induce the egoist user at node i to behave in a socially-optimal way. We show how to derive the parameters of such constraints, and give them interpretations in terms of schemes in which nodes’ transmission rates are constrained by the rates at which they accumulate ‘credit’.