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.     

On the dynamics of judgment: does the butterfly effect take place in human working memory?

Why do people hesitate—to do something, or not to do something—even when the data available to them remain constant? The neural model of human working memory (WM) we present in this paper explains hesitation as an emergent property of a complex dynamic structure of stored/processed information. WM is considered as a geometric space inhabited by a “society” of memes, i.e., complex informational structures. A large population of identical memes can cause a feeling, judgment, or intention in an individual. The memes navigate all over WM and interact with one another in a way resembling genetic cross-over; hence, new memes are born at several places in WM. Since the birth of contradictory memes is possible, populations of memes contributing to contradictory feelings, judgments, and plans grow in WM and fight for domination. A computer simulation of the process showed that WM's state sometimes goes to a two-focal “strange” attractor. Hence, sudden mental shifts, as, say, from love to hate and back from hate to love, may be caused by minute fluctuations in the densities of meme streams entering WM. The complex system theory calls this phenomenon the “butterfly effect”. We argue that this effect takes place in the human mind and also can take place in an advanced robot.     

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.     

EVOLUTION AND MEMES: THE HUMAN BRAIN AS A SELECTIVE IMITATION DEVICE

The meme is an evolutionary replicator, defined as information copied from person-to-person by imitation. I suggest that taking memes into account may provide a better understanding of human evolution in the following way. Memes appeared in human evolution when our ancestors became capable of imitation. From this time on, two replicators memes and genes coevolved. Successful memes changed the selective environment, favoring genes for the ability to copy them. I have called this process memetic drive. Meme-gene coevolution produced a big brain that is especially good at copying certain kinds of memes. This is an example of the more general process in which a replicator and its replication machinery evolve together. The human brain has been designed not just for the benefit of human genes, but for the replication of memes. It is a selective imitation device. Some problems of definition are discussed and suggestions made for future research.     

Meme ranking to maximize posts virality in microblogging platforms

Microblogging is a modern communication paradigm in which users post bits of information, or “memes” as we call them, that are brief text updates or micromedia such as photos, video or audio clips. Once a user post a meme, it become visible to the user community. When a user finds a meme of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough the social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted by their contacts) to show to users when they log into the system. The objective is to maximize the overall activity of the network, that is, the total number of reposts that occur. We deeply characterize the problem showing that not only exact solutions are unfeasible, but also approximated solutions are prohibitive to be adopted in an on-line setting. Therefore we devise a set of heuristics and we compare them trough an extensive simulation based on the real-world Yahoo! Meme social graph, using parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods.     

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.     

Meme media architectures for re-editing and redistributing intellectual assets over the Web

While current Web technologies have allowed us to publish intellectual assets in world-wide repositories, and to browse the resulting massive accumulation, we have no effective tools yet to flexibly re-edit and redistribute such intellectual assets for their reuse in different contexts. Open Hypermedia Systems addressed the problem of augmenting third-party applications in 90 s, and more recently Web augmentation. We need extended OHS technologies for the advanced reuse of Web-published intellectual assets through re-editing and redistributing them. Meme media and meme pool technologies will work as such extended Open Hypermedia Systems technologies to annotate, re-edit, and redistribute Web-published assets. This paper reviews the IntelligentPad and IntelligentBox meme media architectures together with their potential applications, and proposes both the use of XML/XSL or XHTML to define two-dimensional meme media objects. When applied to Web contents, meme media technologies make the World Wide Web operate as a meme pool, where people can publish their intellectual assets as Web pages, access some Web pages to extract some of their parts as meme media objects through drag-and-drop operations, visually combine these meme media objects together with other meme media objects to compose new intellectual assets, and publish these assets again as Web pages. Our framework creates a new vista in the circulation and reuse of our knowledge represented as multimedia documents and/or application programs, especially in the field of science.     

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.     

A tutorial for competent memetic algorithms: model, taxonomy, and design issues

The combination of evolutionary algorithms with local search was named "memetic algorithms" (MAs) (Moscato, 1989). These methods are inspired by models of natural systems that combine the evolutionary adaptation of a population with individual learning within the lifetimes of its members. Additionally, MAs are inspired by Richard Dawkin's concept of a meme, which represents a unit of cultural evolution that can exhibit local refinement (Dawkins, 1976). In the case of MA's, "memes" refer to the strategies (e.g., local refinement, perturbation, or constructive methods, etc.) that are employed to improve individuals. In this paper, we review some works on the application of MAs to well-known combinatorial optimization problems, and place them in a framework defined by a general syntactic model. This model provides us with a classification scheme based on a computable index D, which facilitates algorithmic comparisons and suggests areas for future research. Also, by having an abstract model for this class of metaheuristics, it is possible to explore their design space and better understand their behavior from a theoretical standpoint. We illustrate the theoretical and practical relevance of this model and taxonomy for MAs in the context of a discussion of important design issues that must be addressed to produce effective and efficient MAs.     

Belief revision via Lamarckian evolution

We present a system for performing belief revision in a multi-agent environment. The system is called GBR (Genetic Belief Revisor) and it is based on a genetic algorithm. In this setting, different individuals are exposed to different experiences. This may happen because the world surrounding an agent changes over time or because we allow agents exploring different parts of the world. The algorithm permits the exchange of chromosomes from different agents and combines two different evolution strategies, one based on Darwin’s and the other on Lamarck’s evolutionary theory. The algorithm therefore includes also a Lamarckian operator that changes the memes of an agent in order to improve their fitness. The operator is implemented by means of a belief revision procedure that, by tracing logical derivations, identifies the memes leading to contradiction. Moreover, the algorithm comprises a special crossover mechanism for memes in which a meme can be acquired from another agent only if the other agent has “accessed” the meme, i.e. if an application of the Lamarckian operator has read or modified the meme. Experiments have been performed on the η-queen problem and on a problem of digital circuit diagnosis. In the case of the η-queen problem, the addition of the Lamarckian operator in the single agent case improves the fitness of the best solution. In both cases the experiments show that the distribution of constraints, even if it may lead to a reduction of the fitness of the best solution, does not produce a significant reduction. Evelina Lamma, Ph.D.: She is Full Professor at the University of Ferrara. She got her degree in Electrical Engineering at the University of Bologna in 1985, and her Ph.D. in Computer Science in 1990. Her research activity centers on extensions of logic programming languages and artificial intelligence. She was coorganizers of the 3rd International Workshop on Extensions of Logic Programming ELP92, held in Bologna in February 1992, and of the 6th Italian Congress on Artificial Intelligence, held in Bologna in September 1999. Currently, she teaches Artificial Intelligence and Fondations of Computer Science. Fabrizio Riguzzi, Ph.D.: He is Assistant Professor at the Department of Engineering of the University of Ferrara, Italy. He received his Laurea from the University of Bologna in 1995 and his Ph.D. from the University of Bologna in 1999. He joined the Department of Engineering of the University of Ferrara in 1999. He has been a visiting researcher at the University of Cyprus and at the New University of Lisbon. His research interests include: data mining (and in particular methods for learning from multirelational data), machine learning, belief revision, genetic algorithms and software engineering. Luís Moniz Pereira, Ph.D.: He is Full Professor of Computer Science at Departamento de Informática, Universidade Nova de Lisboa, Portugal. He received his Ph.D. in Artificial Intelligence from Brunel University in 1974. He is the director of the Artificial Intelligence Centre (CENTRIA) at Universidade Nova de Lisboa. He has been elected Fellow of the European Coordinating Committee for Artificial Intelligence in 2001. He has been a visiting Professor at the U. California at Riverside, USA, the State U. NY at Stony Brook, USA and the U. Bologna, Italy. His research interests include: knowledge representation, reasoning, learning, rational agents and logic programming.     

半开放平方衰减的立方自催化反应扩散系统中化学波的研究

建立半开放带平方衰减反应,立方自催化反应扩散系统的数学模型,分析系统的初边值问题和化学波的局部行为,得化学波的类型及产生的条件.研究行波解所具有的性质,编制有限差分法的计算程序,通过计算以验证理论分析的结果.     

Racial microaggressions and perceptions of Internet memes

Although more blatant forms of discrimination have declined, racial prejudice continues to manifest itself in subtle ways. For example, People of Color experience racial microaggressions (i.e., subtle slights or ‘put downs’) in their face-to-face interactions (Nadal, 2011) and in online contexts (Clark et al., 2011). This study investigates whether experiencing subtle racial discrimination offline can influence perceptions of online content, specifically racial themed Internet memes. Results indicate that although both People of Color and Whites viewed racial themed memes to be more offensive than non-racial themed memes (control images), for People of Color the ratings of racial themed memes were predicted by previous discrimination; those who reported experiencing more racial microaggressions in everyday settings rated racial themed memes as more offensive. The same pattern of results did not emerge for ratings of non-racial themed memes or for White participants. These results provide initial evidence that experiencing racial microaggressions in offline interactions may lead individuals from racial minority groups to be more likely to perceive racial discrimination in online settings.     

电子邮件病毒传播网络的建模与分析

为了深入了解电子邮件病毒传播网络的演化规律,通过分析电子邮件病毒传播网络的生长过程的特点及方式,把电子邮件病毒的传播过程抽象成一个网络的生长,建立了基于局部优先连接机制的电子邮件病毒传播网络的动态演化模型,该模型在考虑网络动态增长的同时,兼顾了网络内部边的动态演化,更细致、真实地刻画了电子邮件病毒传播网络的动态演化特性,仿真结果表明,该模型所生成的网络具有无标度特性、小世界以及高聚集性。     

A system for extracting and comparing memes in online forums

From their origins in the sociological field, memes have recently become of interest in the context of `viral' transmission of basic information units (memes) in online social networks. However, much work still needs to be done in terms of metrics and practical data processing issues. In this paper we define a theoretical basis and processing system for extracting and matching memes from free format text. The system facilitates the work of a text analyst in extracting this type of data structures from online text corpuses and n performing empirical experiments in a controlled manner. The general aspects related to the solution are the automatic processing of unstructured text without need for preprocessing (such as labelling and tagging), identification of co-occurences of concepts and corresponding relations, construction of semantic networks and selecting the top memes. The system integrates these processes which are generally separate in other state of the art systems. The proposed system is important because unstructured online text content is growing at a greater rate than other content (e.g. semi-structured, structured) and integrated and automated systems for knowledge extraction from this content will be increasingly important in the future. To illustrate the method and metrics we process several real online discussion forums, extracting the principal concepts and relations, building the memes and then identifying the key memes for each document corpus using a sophisticated matching process. The results show that our method can automatically extract coherent key knowledge from free text, which is corroborated by benchmarking with a set of other text analysis approaches, as well as a user study evaluation.     

Who creates Trends in Online Social Media: The Crowd or Opinion Leaders?

Internet slang words can very quickly become ubiquitous because of social memes and viral online content. Weibo, a Twitter‐like service in China, demonstrates that the adoption of popular Internet slang undergoes 2 distinct peaks in its temporal evolution, in which the former is relatively much lower than the latter. An in‐depth comparison of the diffusion of these different peaks suggests that popular attention in the early stage of propagation results in large‐scale coverage, while the participation of opinion leaders at the early stage only leads to minor popularity. Our empirical results question the conventional influentials hypothesis and provide some insights for marketing practice and influence maximization in social networks.     

基于微博的事件传播分析

事件的传播分析是社交网络分析中一个重要的研究点.网络热点事件的爆发通过社交网络迅速传播,从而在短时间内造成很大的影响.而在社交网络中制造舆论热点进行传播的代价相对于传统媒介较低,因此很容易被不法分子利用,对社会安全以及人们财产造成损失.传统的影响传播分析仅能对单条博文进行影响传播分析,这使社交网络中的事件传播分析受到限制.在已有的独立级联模型的基础上,提出了一种结合用户去重、垃圾用户滤除和概率阅读的传播模型,其基本思想是对多条热点博文构成的事件进行用户去重,构建事件传播网络拓扑图,然后对其中的垃圾用户节点进行滤除,最后利用概率阅读模型进行影响传播分析.这为事件传播分析提供了思路.通过一系列实验来验证方法及模型,通过与传统的博文分析进行对比,验证了方法的正确性与有效性.     

改进的键基正交各向异性近场动力学模型

针对经典的键基近场动力学(bond-based peridynamic,BPD)模型受固定泊松比限制的问题,提出一种改进BPD模型。该模型可解除泊松比限制,并可用于分析正交各向异性单向板的变形和裂纹扩展问题。在改进BPD模型中,每根键受到轴向和横向成对力的作用,额外增加的节点转动可消除由横向力引起的附加弯矩,从而确保该模型满足角动量守恒条件。仿真结果验证所提出的改进BPD模型的精度,并展示其预测碳纤维复合材料变形和裂纹扩展的能力。     

Threshold conditions for arbitrary cascade models on arbitrary networks

Given a network of who-contacts-whom or who-links-to-whom, will a contagious virus/product/meme spread and ‘take over’ (cause an epidemic) or die out quickly? What will change if nodes have partial, temporary or permanent immunity? The epidemic threshold is the minimum level of virulence to prevent a viral contagion from dying out quickly and determining it is a fundamental question in epidemiology and related areas. Most earlier work focuses either on special types of graphs or on specific epidemiological/cascade models. We are the first to show the G2-threshold (twice generalized) theorem, which nicely de-couples the effect of the topology and the virus model. Our result unifies and includes as special case older results and shows that the threshold depends on the first eigenvalue of the connectivity matrix, (a) for any graph and (b) for all propagation models in standard literature (more than 25, including H.I.V.). Our discovery has broad implications for the vulnerability of real, complex networks and numerous applications, including viral marketing, blog dynamics, influence propagation, easy answers to ‘what-if’ questions, and simplified design and evaluation of immunization policies. We also demonstrate our result using extensive simulations on real networks, including on one of the biggest available social-contact graphs containing more than 31?million interactions among more than 1?million people representing the city of Portland, Oregon, USA.     

求解k条最短路径问题的混合蛙跳算法

提出了一种求解k条最短路径问题的混合蛙跳算法.采用自然路径的形式对青蛙个体编码,设计了一种能够使模因信息在青蛙个体间传递的蛙跳方法.在各青蛙族群内部,通过较差个体向优秀个体的跳跃进行局部搜索,从而优化模因信息.在族群之间,通过混合与排序使各族群的模因信息得以交流与重组,从而获取新的寻优方向.数值实验表明,本文算法搜索k条最短路径的能力强、收敛速度快、稳定性好,可应用于求解大规模网络中的多条最优路径问题.     

基于ANFIS的非线性电机系统的建模

将一种神经—模糊结构—自适应神经模糊推理系统 (简称ANFIS)用于非线性电机系统的建模 ,获得了一个良好的大范围的全局非线性模型 ,同时 ,通过与反向传播网络建模结果的性能对比 ,说明ANFIS在参数收敛速度及建模精度上的优越性。显示出ANFIS是非线性系统的建模、辨识的有力工具