TY - JOUR
AU - Spitzberg, Brian, H.
AB - Abstract An approach to modeling meme diffusion is proposed, drawing on insights from evolutionary theory, information theory, meme theory, frame analysis, general systems theory, social identity theory, communicative competence theory, narrative rationality theory, social network analysis, and diffusion of innovation theory. The model framework proposes that memes compete at multiple levels to occupy information niches. The purpose of this synthesis is to provide a heuristic framework for organizing manifold investigations into the roles that new media are playing in the diffusion of ideas in cyberspace and real space. The result is an outline of a multilevel model of meme diffusion (M3D) that seeks to integrate these theories and to stimulate new theory development in the fields of big data and new media. Communication theories may not be evolving as rapidly as the technologies of communication, although efforts are progressing in understanding the role of mediated communication in society (e.g., Couldry & Hepp, 2013; Hampton & Ling, 2013). One of the more challenging, and promising, prospects is that with the advent of new media there are new sources of “big data” through which theories of mediated communication can be tested (Zhou, Ding, & Finin, 2011). There are promising developments in the application of online data mining in the understanding of political affiliations (e.g., González-Bailón, Banchs, & Kaltenbrunner, 2012), social groupings (e.g., Papacharissi, 2009), disease vectors and epidemics (e.g., Nagel et al., 2013), disaster response (e.g., Comfort, 2010), social movements (e.g., Earl & Kimport, 2008), and terrorist networks (e.g., Chen, Reid, Sinai, Silke, & Ganor, 2008), to name a few. There is a need for theories and models that can accommodate such a diverse set of social dynamics and such a broad and nested span of informational and interactional complexity. In light of this need, an effort toward a multilevel model of meme diffusion is presented, consistent with Turner’s (1990) recommendations that the most productive uses of metatheory are in extracting and synthesizing propositions across existing theories into new analytic models capable of empirical test. Also consistent with Turner’s assumptions and the need to include collective concepts in theories (Morgeson & Hofmann, 1999), a diverse set of theoretical concepts will be scavenged from existing studies and perspectives, but not all of the assumptions associated with those concepts in their original theoretical literatures will be considered applicable (Morgeson & Hofmann, 1999). The proposed model framework indicates that memes with more adaptive fitness for a given information ecology, and a more altruistic set of features in its initial social context(s), the more competitive the meme will be. The successful diffusion of the meme, however, will then depend on the severity of the competition by counter-memes in the larger contexts to which the meme might diffuse. Factors at the meme level, the individual level, the social network level, the societal and mass media level, and the geotechnical level are identified in relation to the meme’s chances for successful diffusion. The framework begins with a relatively basic and primitive theoretical unit: the meme. Memes Dawkins (1976) characterized the primary replicator of biological evolution as the (selfish) gene (rather than as Darwin’s selfish individual). He also hypothesized an analogous process of social and cultural evolution through a replicator he referred to as a “meme.” The concept has since been widely discussed and debated (e.g., Aunger, 2002; Blackmore, 1999; Brodie, 1996; Lynch, 1996; Shifman, 2014). A meme is an act or meaning structure that is capable of replication, which means imitation. Imitation is, in essence, a process of communication, in which one social organism, group, or system engages in activity that represents an informational duplicate or derivative version of another act or meaning. Thus, a meme is a version of what semiotics refers to as a sign, and what rhetorical scholars consider a text or an ideograph (Johnson, 2007; Tyrkkö, 2007) and could take on more macrofunctional forms such as a rhetorical trope (Hoeken, Swanepoel, Saal, & Jansen, 2009) or speech act. Memes are replicable forms of signs that are any objects, actions, texts, or symbols manipulated to create an intended mental representation or meaning (Kilpinen, 2008). A given text can be copied, altered or remixed, repackaged, or mimicked (Shifman, 2014). Ongoing efforts by scholars seek to formulate particular conceptual definitions, dimensions, and typologies of memes (e.g., Brodie, 1996; Shifman, 2014). Within any domain of text, broadly defined, certain words and word linkages may reveal the spread, dominance, or emergence of memetic influences on social or institutional networks (e.g., González-Bailón et al., 2012). Writ large, then, memes are much more than the mere “inside jokes or pieces of hip underground knowledge” spread through social media (Bauckhage, 2011), and are instead, the fundamental feature of socially and technologically propagated knowledge (Heylighen & Chielens, 2009). Communication messages such as tweets, e-mails, and digital images are by definition memes, because they are replicable transmitters of cultural meanings. Memes were originally analogized to the role genes play in the process of evolution. Three processes are necessary but not sufficient (Henrich, Boyd, & Richerson, 2008) for evolution to occur: Variation, selection, and retention (Blackmore, 1999). Variation in memes is inherent in human communication; the potential for the construction of novel memes is infinite, despite a finite symbol system (Chomsky, 1957). Even direct quotations experience substantial rates of mutation in social media propagation (Simmons, Adamic & Adar, 2011). There are variable environmental pressures that present selection influences on which memes are replicated and which are not. Finally, retention processes for memes would involve digital memory, collective memory, redundancy, duration, learning, education, and communication processes that reinforce cultural diffusion and traditions once a meme has been innovated. Some aspects of memes make them more likely to replicate (Chielens & Heylighen, 2005; Heylighen, 1993, 1998) and aspects of environments or systems influence the diffusion and selection of memes within and across these contexts (Watts & Dodds, 2007). Dawkins (1976) speculates that the “survival value” of a meme “does not mean value for a gene in a gene pool, but value of a meme in a meme pool” representing its “psychological appeal” (p. 207). He posits that self-replicating entities must possess fidelity (ability to make accurate copies), fecundity (ability to make multiple copies), and longevity (ability to survive long enough to replicate). Debate continues on whether biological evolution occurs at the gene level, individual level, or group level. Arguing for the importance of group-level selection, Wilson and Wilson (2007, p. 345) concluded that “selfishness beats altruism within groups. Altruistic groups beat selfish groups. Everything else is commentary.” Competition is adaptive for individuals within groups, until such time as the group as a collective is competing against other groups, in which case intragroup cooperation and altruism are more adaptive if the group is to survive and thrive. In continuing the analogy, this axiom suggests that memes are likely to compete for prominence within parts of the social network, but when a particular social network segment faces competition against rival social network components, the meme is more likely to survive and thrive to the extent it is ensconced within a cohesive and coherent frame of reference. Within the group, “a given meme becomes more prevalent when it brings higher average fitness to its group members than do alternative memes. Such monotone dynamics are consistent with many specific mechanisms of meme preservation and transmission, which can include various kinds of communication and reinforcement behavior within the group” (Friedman & Singh, 2004, p. 161). Extending this analogy to an information economy-driven logic (Shifman, 2014), memes seek to find a foothold in an ecological information space in which there is limited attention capacity (Leskovec, Backstrom, & Kleinberg, 2009). In many instances, memes will seek a relative information niche to which they are particularly well adapted, and in other instances, memes will survive and thrive only through displacement of other extant memes in that information ecology (Weng, Flammini, Vespignani, & Menczer, 2012). Features adaptive within a group (i.e., within-group competition) are not necessarily the same features adaptive at the intergroup level (i.e., within-group cooperation and intergroup competition). This suggests a fundamental asymmetry in the forces of meme diffusion. It also seems to presage the nature of the polarization of ideas that are diffused in cyberspace, especially in regard to ideas associated with the adaptive survival value of status, power, and resources (whether tangible or symbolic). Furthermore, social identity and intergroup theory anticipate that individuals select group memberships owing in part to the value that such groups have for the individual’s identity. Moreover, how a group competes with other groups establishes potential moral dilemmas between individual and group identities (Gupta, 2001). This tension between individual and collective identities has implications for how communicators adjust their language, rituals (Buck, 2011), and their media choices (Reid, Giles, & Abrams, 2004). Although there are many reasonable objections to the analogy between genes and means of cultural evolution (e.g., Henrich et al., 2008), the analogy does not have to be perfectly parallel for it to be theoretically heuristic (Johnson, 2007). Furthermore, “the yawning gap between popular and academic uses of memes may serve as a fertile site for an improved meme theory” (Shifman, 2013, p. 364). Nevertheless, the notion that memes and individuals, such as genes, strive to thrive at the cost of affiliated competitors, whereas collective groups and societies tend to thrive to the extent that memes and individuals that comprise the group or society cooperate, seems germane to aspects of group competition (e.g., Betts & Hinsz, 2013). Frames Memes bear numerous parallels with the concept of frames (Bateson, 1972; Goffman, 1974; Lakoff, 2004). Somewhat analogous to actual picture frames, as well as the concept of mathematical sets, frames can be understood broadly as analytical frames of reference and emphasis (Goffman, 1974; Scheufele, 2004). Frames focus attention through the punctuation or specification of what is, or is not, relevant (Snow, 2004). They delimit “a class or set of messages (or meaningful actions)” such that frames are both inclusive and exclusive, and metacommunicative (Bateson, 1972, p. 186). Frames can also be understood as cognitive schemata or meaning structures that organize meaning (Lakoff, 2004). In this cognitive sense, they are “the patterns of interpretation through which people classify information in order to handle it efficiently” (Scheufele, 2004, p. 402). Frames therefore refer to a perceiver stance (Shifman, 2013), or “to the process by which people develop a particular conceptualization of an issue or reorient their thinking about an issue” (Chong & Druckman, 2007b, p. 104). Frames reflect complex interrelations among symbols and memes, which can often be operationalized as a linguistic ontology (Kwan, 2006). There are at least three important differences between memes and frames. First, memes tend to be singular messages or message units (e.g., a phrase, an e-mail, a tweet, a video, a visual sign—such as the Obama “Hope” poster-http://obamapostermaker.com/; a YouTube video, a song, a political gaff, etc.), whereas frames generally imply coherent groups of memes and metaphors woven together in more narrative or collective form (Blackmore, 1999; Dodge, 2008). Second, frames are often conceptualized as interpretive schemata, whereas memes are by definition a message behavior. Third, frames are also metamessages that may or may not be contained in the replicable content of the meme itself. For example, a message that smoking shortens one’s life might be loss-framed (i.e., an image showing children at a funeral) or gain-framed (i.e., an image of a person playing with grandchildren). The meme (the verbal message) can be framed visually, but not all media would be capable of replicating that visual frame along with the verbal message. Thus, memes are generally framed and frames often get replicated as part of the meme, but the frame is also a metamessage that may or may not be replicable as part of the individual meme itself. Frames do not exist in a vacuum. Like memes, frames compete in an environment in which rival frames seek survival. Clearly, “wherever there is power, there is counterpower” (Castells, 2011, p. 773), so where there are memes and frames, there are likely to be countermemes and counterframes, some of which might be strategic suppression efforts (e.g., Wei et al., 2013). Counterframes represent systemic interpretive perspectives or narratives that provide an alternative, incompatible, or contrary view vis-à-vis a given frame (Lakoff, 2004). “In competitive contexts, the strength of the opposition frame determines the distance one is pulled away from his or her values even when the frame that is congruent with those values is represented in the debate” (Chong & Druckman, 2007b, p. 113). Frames intersect with, or are systemically ensconced within, narratives (Fisher, 1984, Fisher, 1985, 1989). Narratives are broad outlines and symbolic sequences through which the facts and experiences of the world can be interpreted. Narratives operate by standards of narrative rationality rather than traditional logical rationality. Narratives “hang together” through a sense of coherence and “good reasons” (Fisher, 1984). The rationality of an argument or persuasive appeal depends on its resonance with the narrative frames a person or group uses to make sense of the world. The term resonance is introduced here to represent collectively the criteria of narrative rationality identified by theorists such as Fisher (1984, Fisher, 1994), Lakoff (2004), and Chong and Druckman (2007b). In the study by Fisher (1994), narratives are evaluated in terms of their coherence (i.e., consistency, completeness, and character) and their fidelity (i.e., the legitimacy of the reasons and the values implied), whereas in the work by Lakoff (2004), narratives are evaluated in terms of their compatibility with existing narrative cognitive frames of the interpreter. Narratives also involve interconnected, or linked, concepts, and ideas that are evaluated in their degree of integrated cohesion. In this sense, as memes interconnect in certain coherent ways, they often accumulate in the form of more expansive or inclusive narratives. As Dawkins (1976, p. 210) suggests, “if almost everybody who believes in A also believes in B—if the memes are closely ‘linked’ to the genetic term—then it is convenient to link them together as one meme.” Memes are likely to vary in the degree to which they resonate with, contradict, or evoke, extant frames in the minds of interpreters, and frames are likely to vary in the degree to which they accommodate new memes or other frames. As with evolution, different environments are more or less accommodating to any given memetic variations that migrate to, or arise within, those environments. An understanding of such environmental effects requires further analysis of the factors affecting such diffusion processes. Diffusion of innovations The replication of memes throughout a system or population is typically a form of innovation diffusion. An innovation is “an idea, practice, or object that is perceived as new by an individual or other unit of adoption” (Rogers, 2003, p. 12). Given that “an ‘idea-meme’ might be defined as an entity which is capable of being transmitted from one brain to another” (Dawkins, 1976, p. 210), an innovation includes any meme that is novel to an interpreter of a message transmitting the meme. A diffusion “is a special type of communication in which the messages are about a new idea” (Rogers, 2003, p. 6). The diffusion process, therefore, involves communicating an idea “through certain channels over time among the members of a social system” (Rogers, 2003, p. 5). Most diffusion research has been about products, and most of that research indicates that word-of-mouth and social norms are likely to be more influential than the innovativeness of consumers per se (Sultan, Farley, & Lehmann, 1990, 1996; Van den Bulte & Stremersch, 2004). As a simple example regarding memes, considering songs as memes, Garg, Smit, and Telang (2011) found that homophily and online activity substantially increased the discovery of new songs in an online music community. An idea is generated by an individual. This idea is put into words or images, and communicated via various media (natural and technological) to others. The degree to which this idea is replicated by these others indicates its status as a meme and reflects a process of innovation diffusion. “By the very act of forwarding a viral message, there is an implicit endorsement of the content and the credibility of the message” (Harvey, Stewart, & Ewing, 2011, p. 365). The adoption of the idea that is, in any way, manifest in behavior of these others, indicates that imitation (i.e., replication or diffusion) has occurred. This new meme then competes in the information environment into which it is communicated. Diffusion of innovations theory posits that features such as ease of use, observability, trialability, and relative advantage will moderate the degree to which innovations, or memes, diffuse throughout a potential market, group, or population (e.g., Compeau, Meisher, & Higgins, 2007; Rogers, 2003). As memes diffuse throughout a given group or system, that is, as meme replication or imitation occurs, features of the group or system begin to emerge (Sawyer, 2005). This emergence reflects systemic structures and functions that are manifest as rituals, traditions, narratives, norms, roles, and institutions, and again may reflect the success of the memes and their diffusion. The more that media amplify the availability and diffusion of memes, by making the meme seem easier to “try on” (i.e., trialability and ease of use), more accessible (i.e., observability and normative), and more reasonable or resonant (i.e., relative advantage) than existing or competing memes, such memes will continue replicating throughout the systems to, between, and beyond which they are communicated. A way toward a multilevel model of memetic diffusion One of the most vexing problems of formulating theory in the realm of memes is bridging the micro to the macro features of society. The challenge is how to develop theory that adequately explains how the micro structures of individual-to-individual message exchange influence or translate to macrosocietal structures such as status, social movements, institutions, policy formation, and culture. Social network segments and systems reveal emergent structures at both micro- and macro levels of organization (Assenza, Gutiérrez, Gómez-Gardeñes, Latora, & Boccaletti, 2011). It appears reasonable to presume that variables at each of these levels will affect the fitness of memes, and the ability of individual memes to not only replicate, but to be both transformed and transformative (Wisdom, Brian Chor, Hoagwood, & Horwitz, 2013). Several concepts provide insight into how such translations and transformations are likely to occur. To incorporate a flexible, scalable, and potentially predictive model of memetic fitness, a framework for a Multilevel Model of Meme Diffusion (M3D) is proffered (Figure 1). It envisions communication acts (i.e., memes) that are enacted as innovations by individuals, groups, or institutions. These memes become available for replication in a diffusion process. In general, the framework assumes that “the ‘cultural fitness’ of a mental representation can be inferred from its successful transmission through the population” (Henrich et al., 2008, p. 120). Figure 1 Open in new tabDownload slide The multilevel model of meme diffusion (M3D). Figure 1 Open in new tabDownload slide The multilevel model of meme diffusion (M3D). Figure 1 elaborates six major nested levels of variables likely to influence or reflect meme diffusion: (a) meme(s), (b) the individual sources’ competence, (c) the various social network structures through which the meme is diffused, (d) the societal contextual factors, (e) geospatial/technical factors, and (f) the criteria of meme fitness and the conceptual criteria indicating the practical outcomes of meme diffusion, which feed back into the original system levels. Any given meme may be extraordinarily important to a particular person in a particular context and relationship. This would be the realm of dyadic or everyday interaction. To the extent that communicators seek a broader audience for their memes, the success of the diffusion process is likely to depend on a variety of factors outlined in Figure 1. Each of these nested levels of analysis is elaborated as follows. Meme level Given the origins of the concept of the meme in the theory of evolution, it is not surprising that the concept of fitness is applied to memes. Fitness is the adaptive capability of a meme, where adaptation is the potential of the meme to adjust to the constraints and demands of the networks through which the meme is propagated and replicated. Fitness represents a meme’s ability to replicate by adapting successfully to environments. “Fitter memes will be more successful in being communicated” (Heylighen & Chielens, 2009, p. 2). A variety of meme features is likely to facilitate meme replication. Memes are intrinsically selfish, in the sense that they compete for replication, whether by pure repetition or mere topic uptake. Fitness, however, is always dependent on aspects of the design of the meme, the meme’s source, and the environment into which the meme is diffused. Some memes are generally more likely to replicate than others. In a general sense, “a meme’s memetic fitness (vs. genetic fitness) will depend jointly on how attractive its content is to human brains and how it affects an individual’s likelihood of being selected as a cultural model by other individuals” (Henrich et al., 2008, p. 126). Extensive research has examined the features of messages that make them more persuasive (e.g., Allen & Preiss, 1997; Banas & Rains, 2010; Gallagher & Updegraff, 2012; O’Keefe, 1999; O’Keefe & Nan, 2012; Zarefsky, 2008). Persuasion, however, is not the same as replication. All innovation adoption is a form of influence, and influence can entail a change of attitude, value, or belief. Meme diffusion in the form of replication, therefore, is a form of influence that occasionally involves persuasion. A meme can afford to be more selfish to the extent it is characterized by a number of characteristics. Several typologies of meme fitness have been suggested (Heylighen, 1993, 1998; Heylighen & Chielens, 2009). To date, most meme theory appears to be based on a priori rational models of cognition. The M3D framework proposes that a meme is considered more fit to the extent it is distinctive, redundant, triable, and media convergent. Distinctiveness refers both to the attributional covariance principle, in which things that covary in occurrence tend to be perceived as causally linked (Manusov & Spitzberg, 2008), and to the precision of the message that permits the identification of boundaries between that meme and other memes. More distinctive messages are richer with novelty (Wu & Huberman, 2007), thereby possessing greater information value (i.e., less message entropy). Entropy is essentially “a measure of the unpredictable character of a set of objects. The more variation and difference there is, the higher the entropy, while the less variation there is, the less entropy there is” (Goldsmith, 2000, p. 86). In more cognitive expression, entropy “expresses quantitatively the amount of uncertainty at the receiver about which message or sign has arrived. The more the receiver knows in advance about what messages (or signs the source will or can produce), the less is the uncertainty about what has actually been transmitted and the less is the information gained from a transmission” (Yates, 2012, p. 187). For example, research indicates that the emotional surprise factor of memes moderates their replication (Dobele, Lindgreen, Beverland, Vanhamme, & van Wijk, 2007). Greater entropy in a message domain can reflect the diversity or dispersion of communities to which the message is being diffused (Godes & Mayzlin, 2004). Redundancy, both within message and across messages and media, enhances message retention and accessibility for subsequent replication. In addition, repetition reduces error in transmission, as information lost in one iteration can be corrected by the retransmission of the message. Replication itself is recursively reinforcing (Shannon & Weaver, 1964). Triability refers to the ability of communicators to attempt, express, rehearse, and incorporate the meme. Research indicates that direct behavioral experience and the accessibility of attitude information about a topic (or meme) substantially increase the linkage between attitudes and behavior (Glasman & Albarracín, 2006). In general, simpler, more parsimonious memes are easier to use and express across a variety of contexts. Convergent memes are those amenable across a variety of media formats. Some memes can only be transmitted through a particular medium, whereas other memes are encoded in a format that has vast transferability across languages, media, contexts, and applications. So, for example, the climate change “hockey stick” is a meme that can be described in purely written ways, but thrives in forums that permit the translation of data, words, and visual diagrams, with animation (Besel, 2011). Thus, it can be easily repeated and can be replicated across a variety of media. In contrast, some memes are likely to depend significantly on their visualization, such as the video of the Challenger explosion, or the riot kiss meme (Hahner, 2013)—these memes may have unique rhetorical force primarily due to their visual nature, which restricts their replicability to media that provide visual formats. Finally, memes that are expressed in formats that permit greater media expressivity or richness of cues are likely to be more viral than more informationally impoverished memes. A headline that discusses a politician’s sexting scandal is more powerful to the extent that it comes with the visual image. The individual level Messages begin with some individual actor somewhere, and the characteristics of the actor still matter to the process of diffusion (Totterdell, Holan, & Hukin, 2008). Popularity offline appears to be closely connected to online popularity (Zywica & Danowski, 2008). Computer-mediated communication competence is the extent to which the use of digital media in sending a message is viewed as appropriate and effective (Spitzberg, 2006). Effectiveness refers to the extent to which a message is perceived as fulfilling relatively preferred goals of the communicators engaged in the replication process, whereas appropriateness refers to the extent to which the message exchange process is viewed as legitimate to the context (Spitzberg & Cupach, 1984, 2011). The most competent communication, therefore, is the one that achieves relatively preferred outcomes in a manner considered legitimate to the social context. The conceptually integrative model of computer-mediated communication (CMC) competence consists of five basic components (Spitzberg, 2006). People are more likely to be competent users of CMC to the extent they are (a) motivated, (b) knowledgeable, and (c) skilled within a given (d) context of usage, which produce (e) relatively favorable or unfavorable outcomes. Specifically, in the context of the spread of influence in the WWW, a communicator who is (a) more motivated to generate, use, and distribute information (Ho & Dempsey, 2010), (b) who is more knowledgeable about the technologies and the topic(s) involved (Iribarren & Moro, 2011), (c) who is more skilled at actually using such technologies in the process of communicating, and (d) has more facile contextual incentives and fewer contextual delimitations, is more likely to (e) succeed (i.e., be appropriate and effective) in influencing the diffusion of information via the internet. Competence is closely associated with the source credibility (i.e., ethos) of a communicator, which is an individual or collective judgment that the source of a message possesses good character. “The degree to which one adheres to the message of any story is always related to the degree to which the narrator is taken to be a character whose word warrants attendance, if not adherence” (Fisher, 1994, p. 24). Furthermore, the more competent the communicator, the more likely others will be inclined to find such memes credible, adaptive, and valuable, and therefore, worthy of replication (Kleinnijenhuis, van den Hooff, Utz, Vermeulen, & Huysman, 2011). Whether considered as a form of status, celebrity, popularity, or authority, credible sources are likely to have an advantage in propagating their memetic offspring. The competence of the actor is moderated in part by the communicator’s selectivity of media. Different types of messages are likely to be better transmitted through different media. Some media provide greater presence (i.e., extent of awareness of the other communicator in the interaction), richness (i.e., extent of representation of verbal and nonverbal information), or naturalness (i.e., extent of replication of the face-to-face form of interaction) (Kock, 2004, 2009). Communicators who are more adaptable in their media and messages are more attentive to the roles they occupy in their communication networks (Brandtzæg, 2010; Ishii, 2006), and to the media that fulfill valued audience affordances (Ku, Chu, & Tseng, 2013; LaRose & Eastin, 2004; Ramirez, Dimmick, Feaster, & Lin, 2008). In general, it appears that communication technologies supplement social network factors such as social capital (Wellman, Haase, Wit, & Hampton, 2001; Zhao, 2006), which facilitates the potential for meme diffusion. Actors, by virtue of both their structural position and their individual capabilities and resources, can still wield substantial influence in the context of their social network neighborhoods (Jenkins-Guarnieri, Wright, & Johnson, 2013). Individuals with greater centrality (interconnectedness) and propinquity (proximity and density) in a given segment of their social network have greater potential to contribute to meme replication (Liben-Nowell, Novak, Kumar, Raghavan, & Tomkins, 2005). The diffusion of memes and knowledge is significantly influenced by individuals who are in a position to control or influence the flow of information throughout a network. “Individuals’ location on an adoption curve situates them in a complex world of multiple and shifting subcultures. What they adopt—the cars they drive, music they listen to, stories they link to—signals their affiliations” (Donath, 2008, p. 242). Opinion leaders, facilitators, champions, linking agents (liaisons), and change agents (Thompson, Estabrooks, & Degner, 2006) are hypothesized to influence the replication and structure of social networks significantly through a variety of gate-keeping functions (Barzilai-Nahon, 2008). Research on over 60 million tweets, for example, found that the number of followers and followees are significant predictors of whether a tweet is retweeted (Suh, Hong, Pirolli, & Chi, 2010), although other research indicates that retweets and mentions are influenced more by the content value of the tweet and the name value of the actor (Cha, Haddadi, Benevenuto, & Gummadi, 2010). The position of a person in a given social network is important. By the same token, the nature and features of that social network contextualize the adaptive potential of a meme. Thus, the nature of the social network is considered next. The social network level Given an adaptively fit meme delivered by a competent source, the subsequent success of the meme’s diffusion is likely to depend on the degree to which the social network is uniquely receptive to the meme. The social networks in and through which a meme is propagated are likely to occasionally mediate, and likely moderate, the replication of memes. Memes may be selfishly inclined, but their successful replication is likely to depend on the degree to which a coherent, coordinated (i.e., altruistic) social network system facilitates such replication. Social networks may provide some memes a better set of affinity paths than other memes (Iribarren & Moro, 2011). Consistent with diffusion of innovations theory, which posits informational convergence in meaning (Rogers, 2003), and meme theory, which proposes social forces seeking convergence and conformity (Heylighen & Chielens, 2009), meme replication is generally enhanced through internal forms of facilitation or adaptive context. In particular, whereas memes seek selfish diffusion, the social network is more inclined to reinforce ingroup altruism, in the sense that the components of the network seek homeostasis and coherent progress (Wilson & Wilson, 2007). In promoting such internal altruism, two sets of factors are likely to influence meme diffusion: objective/structural features and subjective/receptiveness features. Objective features Message transfer patterns often reveal highly predictable and stable distributional parameters. Some of these can be understood as structural features of the social networks that initially receive a meme. For example, the number of past memes that are similar to an innovative meme, provides a strong predictive factor of the likelihood that the new meme will be incorporated, reinforced, and propagated (Weng et al., 2012). The number of nodes in a network, the centrality of the early adopting actor(s), and the number of influential nodes seeking to propagate the meme are likely to provide a sheer opportunity factor in the spread of memes (Kee, Sparks, Struppa, & Mannucci, 2013). Some research also indicates memes diffuse better with a sender who has many friends, each of whom has a few other friends, compared with a few friends who each have many other friends (Liu-Thompkins & Rogerson, 2012). The role of influencers in the initial receiving social network(s) is important to the early adoption of the meme. Basically, the more actors there are, and the more efficient it is to communicate with these actors, the more receptive the social network structure is (Centola, 2010). For example, Liu-Thompkins and Rogerson (2012) found that network density (i.e., network connectivity) predicted the diffusion of YouTube videos. The influencers in a network include at least four forms (Castells, 2011): those who have formal authority in networks (networking power), those who influence the regulation of network norms (network power), those who have established greater social influence through their own interactional skills and history (networked power), and the power to establish network boundaries and alter strategic alliances of networks (network-making power). Such influence is likely to be amplified to the extent that network members are highly interdependent, which increases the likelihood of sharing channels of communication and meme transfer. Some research indicates that the receptiveness of the social network is more predictive of meme diffusion than the number of influential network members in the diffusion process (Watts & Dodds, 2007). Research also indicates that there is a tension between homophily and heterophily—homophily facilitates diffusion due to similarity of interests and denser ties, but heterophily provides links to a broader and more diverse network links. This is suggested by an inverted-U relationship between meme diffusion and network homogeneity (Liu-Thompkins, 2012). Thus, at least moderate structural heterophily of network nodes at the borders of the social network is important due to the diversity of links and weak ties through which the network can promote the meme to other networks (Bisgin, Agarwal, & Xu, 2012; Godes & Mayzlin, 2004; Lee, Lee, & Lee, 2009; Weng et al., 2012). In particular, some structural heterophily is important if the meme is to escape the insular boundaries of its own echo chamber of resonance. Subjective features Network structure is demonstrably important, but so are subjective and intersubjective features of social networks. Memes are likely to be replicated to the extent they are met by a subjectively receptive network. The receptiveness factors in the social network that influence the diffusion of a given meme include frame resonance (i.e., the degree to which the meme is perceived as valid, coherent, and consistent with existing meaning frames), the entrenchment and number of extant or counter-frames competing for a social network’s attention span (Leskovec et al., 2009), the perceived relative advantage of the meme relative to existing frames, a social network members’ perceived cascade thresholds of social comparison for action (Fowler & Christakis, 2010; Watts & Dodds, 2007), and the perceived homophily of relevant interests regarding the meme’s content and relationship content (Iribarren & Moro, 2011). No meme is born into a tabula rasa environment. There are extant memes and frames that are either more or less resonant with or resistant to meme innovation. In general, it is proposed that the greater the number and depth of distinct countermemes and counterframes, the greater the entrenchment of regnant meaning lattices, and the greater the resistance to the adoption of meme innovations. This prediction is implicit in research examining the competition of memes in an information environment constrained by an inherent degree of “limited attention” (Weng et al., 2012). To the extent that a meme innovation overcomes the resistance of rival memes and frames, the meme will be replicated to the extent that social network members are subjectively homophilous with respect to the values and meaning structures consistent with the innovative meme. Similarly, memes do not necessarily arrive and gravitate naturally to sync into the regnant frame—rhetorical work can frame the meme to facilitate its resonance with the dominant frame(s) in the social network. This frame resonance is not merely the opposite of meme and frame resistance. Frame resonance involves features of narrative fidelity and coherence (Baesler, 1995; Fisher, 1984; Lakoff, 2004), although there are multiple other approaches to operationalize frames and their features (e.g., David, Atun, Fille, & Monterola, 2011). Memes are also likely to be propagated to the extent that the messages provide evidence of or potential for relative advantage to existing memes and frames (Rogers, 2003). Finally, memes are likely to be adopted to the extent that normative thresholds have been passed, such that cascades or proportions of social network peers have adopted the meme innovation (Fowler & Christakis, 2010; Watts & Dodds, 2007). Specifically, communicators may only adopt or replicate a meme when a certain proportion of their social network has done so. Just as immediate social networks need to be receptive to an introduced meme, so too does the society in which these networks function. The societal level of meme replication is likely to be influenced prominently by the competitive symbolic environment into which the meme is introduced, and the extent to which more macrolevel institutions seek to facilitate the replication of a meme. The societal level Even if a meme is competently designed, competently delivered, and transmitted to and through a receptive and structurally adapted social network, there are features of the context that are likely to mediate or moderate the diffusion trajectory of the meme. In particular, there are exogenous network context factors and process factors that are likely to impact diffusion. The rival network process factors that would facilitate diffusion include the extent to which alternative, and especially antagonistic, rival social groups are competing for meme attention through the transmission of their own countermemes and the extent to which a variety of alternative countermemes and counterframes are already extant. The exogenous actor factors include the stage of diffusion exhaustion (i.e., early, middle, and late), and the degree to which the meme is accessible across media formats and promoted or mitigated by media interests or institutions. Memes are typically transmitted to a finite number of initial actors and/or social networks; their subsequent spread is likely to depend on certain structural features of the network (Kee et al., 2013). There are various forms of social networks (Shumate et al., 2013), and their different types, structures, and functions are likely to have differential effects on meme diffusion. But just as receivers are contextualized within their own social network neighborhood, these social neighborhoods are also contextualized by a population of potentially rival social networks, media environments, and chronological constraints. These contextual influences reflect two classes of factors—rival competitors and diffusion factors. In the larger societal environment in which memes are innovated, there are rival social networks and rival memes populating the rhetorical fabric in the culture (Dawkins, 1976, p. 211). Memes must compete against this vibrant and adapted environment of adapted memes and social networks. “Memes and genes may often reinforce each other, but they sometimes come into opposition… Selection favors memes which exploit their cultural environment to their own advantage” (Dawkins, 1976, p. 213). Similar to many genetic traits, coadapted meme complexes, in the forms of stable and widely accepted narratives in a given culture can become relatively stable over time, into “which new memes find it hard to invade” (Dawkins, 1976, p. 214). A variety of exogenous factors can affect the diffusion of a meme, including the extent to which media institutions and gatekeepers elect not to publicize (i.e., replicate and propagate) the meme across multiple media outlets and markets that are widely and diversely accessible to the population. For example, González-Bailón et al.’s (2012) study of the arousal, valence, and dominance codes of words used in online political discussions as predictors of political approval rates over time was interpreted in the framework of agenda-setting theory (Hong Nga Nguyen & Gehrau, 2010), in which such memes both reflected, and primed, public agendas and may reflect the framing of media gatekeepers (Scheufele & Tewksbury, 2007). Research on the diffusion of the Arab Spring revolts suggested that despite the wide scale influences of social media and the internet, individual governments responded in various ways and through various media institutions in attempting to mitigate those influences, with varying degrees of effectiveness (Spitzberg, Tsou, An, Gupta, & Gawron, 2013). Research is beginning to identify the sources, for example, of attempts to machine-manipulate social media campaigns by seeding memes (e.g., Tweets) to raise the profile of a particular issue or person (Ratkiewicz et al., 2011). The spiral of silence effect of majority opinion may fit well with the notion that collectives with cooperative memes may silence the minority memes with less cohesion (Glynn, Hayes, & Shanahan, 1997), and at the same time account for how some highly competitive individual communicators or minority groups have sufficient motivation and knowledge to give voice to their memes despite such majority dominance (e.g., Matthes, Rios Morrison, & Schemer, 2010). For example, a study of diffusion of innovations (i.e., Twitter adoption) found that mass media influences (e.g., celebrity endorsements) significantly enhanced prediction of “spikes” in Twitter adoption (Toole, Cha, & González, 2012). Finally, the role of demographic similarity in diffusion can vary from stage to stage of diffusion as well (De Bruyn & Lilien, 2008; Toole et al., 2012). Networks need ties to other networks that are somewhat different if the meme is to influence actors and networks beyond those that already uphold similar memes and memeplexes. The geo-technical level The history of communication is in part a successive set of technological affordances that have collapsed the time and space necessary for memes to replicate across communicators (Cairncross, 1997). Research continues to demonstrate, however, that place, space and communication continue to be highly interdependent (Adams & Jansson, 2012; Mok, Wellman, & Carrasco, 2010), despite widespread access to electronic and mobile communications technologies (Crandall et al., 2010; Onnela, Arbesman, González, Barabási, & Christakis, 2011). For example, geospatial proximity and communicative tie closeness have much in common across forms of communication (Liben-Nowell et al., 2005). In regard to the geospatial structure of social networks, “geography constrains group formation in important ways that nevertheless differ from the way it constrains dyadic interactions” (Onnela et al., 2011, p. 5). For example, research shows that geospatial distance, national boundaries suggesting language commonalities, and even airline routes predict Twitter networks and activity (Takhteyev, Gruzd, & Wellman, 2012). The power of space is suggested by research indicating that using the reflected data of cellphone usage, the vast majority of everyday human mobility patterns are predictable (González, Hidalgo, & Barabási, 2008; Song, Qu, Blumm, & Barabási, 2010). In turn, geographic place often represents functional intersections of human activity that can be mapped by meme production (Crandall et al., 2010; Tillema, Dijst, & Schwanen, 2010). For example, a disproportionate geospatial tweeting about a music event may accurately geolocate a music venue (Andrienko et al., 2013). Several geotechnical features are likely to affect meme replication. Clearly, infrastructure trauma or limitations that impede communication technologies and their hardware networks place real limits on meme replication. Social media play an increasingly important role in mapping response to natural disasters and human crises, but they are limited by the functionality of those media. Furthermore, some cultures, countries, and locale lack full technological penetration and demographic accessibility, or geospatial span, which would constrain the ability to replicate memes efficiently. Population density and proximity, often a proxy for geotechnical features of urban density (Hampton & Ling, 2013), facilitate social ties and geospatial contact, thereby increasing the potential for meme replication. Features of the digital divide, in which certain groups or communities have more or less access to convergent mobile communication technologies, and in which urban areas have denser and wider communication networks, significantly influence technology adoption (Hampton & Ling, 2013; Toole et al., 2012). In regard to the technical structure of society, as the internet spreads, social connectivity and social networking platforms tend to spread as well (Wang & Wellman, 2010). The mapping of time–space geography through GIS-enabled and big data sources is beginning to map the complex intersections between space, time, and communication (Shaw, Yu, & Bombom, 2008; Yin, Shaw, & Yu, 2011). If all levels of a system are well-aligned for a given meme to reproduce, the meme is likely to be successful. It will demonstrate its fitness through its performance. In order to anticipate the analysis of meme success, it is important to consider criteria by which the outcomes of meme production might be operationalized and evaluated. The outcome level To the extent that memes are successful, their competence can be evaluated along a variety of efficacy criteria (see Heylighen, 1993), including: popularity (i.e., number of population members adopting the meme; Ratkiewicz, Fortunato, Flammini, Menczer, & Vespignani, 2010), velocity (i.e., the rapidity or rate with which the meme is diffused along these other dimensions; Liu-Thompkins & Rogerson, 2012; Toole et al., 2012), longevity (i.e., persistence or duration of the meme’s exact and derivative forms and content; Baden & Lecheler, 2012; Shifman, 2014), and fecundity (i.e., the degree to which social network adopters replicate the meme by transmitting the meme to others; José-Cabezudo & Camarero-Izquierdo, 2012; Shifman, 2014). In particular, memes that are more popular are replicated by a larger number of actors or nodes and/or a greater proportion of a given population of potentially receptive actors or nodes. Memes with greater velocity diffuse through a given proportion of a population faster than other memes (Centola, 2010; Cruz & Fill, 2008). Some memes and their derivatives can manifest greater longevity than other memes (Dawkins, 1976). Finally, memes have replication rates, indicating the extent to which they are not just recirculated within a closed social network, but forwarded, repeated, and evolved further and beyond the existing network (Harvey et al., 2011; Macskassy & Michelson, 2011). This is a measure fecundity or throughput modification in which the proportion of actors who not only receive a message but forward the meme in some changed form to others. Such criteria may vary from context to context. So, for example, Cornelissen (2013) proposes eight optimality principles that are expected to constrain the adaptive fitness of theorizing metaphors for social scientists. If success is operationalized by the toppling of a government or the election of a candidate, then these serve as the essential defining features of memetic success, and the empirical question then becomes the extent to which factors and criteria of the M3D framework are significantly associated with such outcomes. Depending on which communicative functions are sought, different criteria such as fidelity of replication or meaning might become most relevant (Chiu, Hsieh, Kao, & Lee, 2007; Shifman, 2014). Future implications The M3D framework is probably too expansive to be tested completely in any one study. Nevertheless, M3D is intended to provide a heuristic deductive for falsifiable propositions. Components within levels are generally positively related, and altruistic factors are generally negatively related to competitive factors across levels. For example, just to illustrate an exemplar of potential propositions: The greater an individual communicator’s digital divide constraints (e.g., relative deprivation of opportunities to participate in digital media environments), the lower that individual’s (a) network centrality of influencers, and the lower the (b) relative advantage of that individual’s memes in a given social network. The lower an individual’s network centrality of influencers and the lower the relative advantage of this individual’s memes in a social network, the lower the (a) popularity, (b) centrality, and (c) longevity of this person’s memes (whether self-generated/tweeted or replicated/retweeted) Therefore, the greater an individual communicator’s digital divide constraints (e.g., relative deprivation of opportunities to participate in digital media environments), the lower the (a) popularity, (b) centrality, and (c) longevity of this person’s memes (whether self-generated/tweeted or replicated/retweeted). The M3D framework has begun to organize interdisciplinary research in three broad senses. First, there are the meme diffusion processes that emerge when memes themselves become contagious and highly replicated, such as the viral spread of YouTube videos, tweets, and e-mails. This process could be considered a form of etymemic diffusion (from etymology: Greek etymon—“true sense” + logia “study of, a speaking of”), in which an original meme generates further directly linked memes resulting in a sort of genetic history of a given textual form over time (e.g., the riot kiss, Hahner, 2013). Second, there are events that elicit or evoke expansive collective common expressions in the forms of memes with common textual elements, such as separate individuals’ tweets about reactions to flu symptoms, political candidate behavior, movie watching, or social protest events. This process could be considered a form of evememic diffusion, (from evenire: Latin ex- “out” and venire “to come out, happen, result”), in which events stimulate similar textual expressions about the experience of an event or set of events (e.g., flu tweets; Nagel et al., 2013). Third, transmemic diffusion involves instances in which events evoke memes that generate new memes, or memes generate new events. The Arab Spring was sparked as a merchant immolating himself in Tunisia, which fanned the memes of the Jasmine Revolution and the Arab Spring that fueled further protests and further memes (Spitzberg et al., 2013). These types of research address the reflexive ways in which events shape memes, and the ways in which memes shape events. The metatheoretical perspective known as reductionism broadly envisions the world as a set of matryoshka dolls, in which each successive higher macro level of observation contains within it a more microscopic homunculus entity. Understanding the “scale politics of spatiality” and the “scale politics of communication” will usher in enormous challenges, both theoretically and methodologically (see Adams & Jansson, 2012; Gazzard, 2011). This conceptual analysis has purposefully emphasized integrative heurism over falsifiable rigor for the sake of seeking interconnections across currently divergent tributaries of theoretical thought (Turner, 1990). Future theory and research will face several challenges. Memes, and their frames, are borne into a competitive communicative environment, both in terms of their content and their media access (Chong & Druckman, 2007a; Ramirez et al., 2008). In general, “a small fraction of memes … account for the great majority of all posts. Likewise, a small fraction of users account for most of the traffic” (Weng et al., 2012, p. 4). “Users with few friends may have low breadth of attention while those with many friends are exposed to many memes and thus may exhibit greater entropy” (Weng et al., 2012, p. 5). Yet, research suggests also that memes spread more when a social network has many friends who each have a few friends, than to have a few friends who in turn have many friends (Liu-Thompkins & Rogerson, 2012). Thus, theory must reconcile that individuals matter, but they matter as a result of their structural position in a social network system. Other research indicates that individuals matter, but not as much as systemic features of the network environment in which the individuals are embedded. For example, Watts and Dodds (2007, p. 454) found that “cascades [of influence] do not succeed because of a few highly influential individuals influencing everyone else but rather on account of a critical mass of easily influenced individuals influencing other easy-to-influence people.” Formulating theoretical predictions and explanations for when individuals add unique variance to meme success over and above more systemic factors will continue to pose a theoretical and analytical challenge. “Whether a particular genetic-fitness-reducing meme can spread, and how far it will spread, depends on the details—the dynamics of which are best understood by formally modeling the social and psychological processes involved” (Henrich et al., 2008, p. 127). Many of the constructs in M3D have already been measured in regard to memes (e.g., entropy, popularity, longevity, etc.). As research progresses, a variety of the structures underlying meme diffusion will likely reveal formal models and algorithms that describe and predict both individual actor-level and system-level dynamics. The expectation is that increasingly parsimonious principles and operationalizations will be refined and validated for representing the primary and moderating variables identified in M3D. M3D is clearly not without its limitations. For example, the analogy with genes has its limits. Like genes, the vast majority of memes produced probably do not replicate, but unlike genes, their variations are not randomly distributed. Most memes are probably not generated by their users in order to be replicated (i.e., to be influential beyond their immediate performance). If most memes are not selfish, theoretical assumptions need to articulate the conditions under which selfishness can be anticipated. Another limitation of the framework is that some levels of the framework will require quite different operationalizations than others—most studies of big data invest relatively little effort examining the individual level of competence, which often may require surveying or interviewing the communicator. Drilling down to this level presents problems ranging from IRB constraints to a lack of ways to integrate individual-level data points into big data sets. Furthermore, much of big data analysis involves point-based data (i.e., individuals), but social network analysis tends to be based more on link-based or relational data. Developing consistent metrics across such forms of data may continue to be a priority for testing broad-based models such as this. 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TI - Toward a Model of Meme Diffusion (M3D)
JF - Communication Theory
DO - 10.1111/comt.12042
DA - 2014-08-01
UR - https://www.deepdyve.com/lp/oxford-university-press/toward-a-model-of-meme-diffusion-m3d-NhDkpjHilw
SP - 311
VL - 24
IS - 3
DP - DeepDyve
ER -