Meme Popularity Research Articles

Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena

Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

The origin of the criticality in meme popularity distribution on complex networks.

Previous studies showed that the meme popularity distribution is described by a heavy-tailed distribution or a power-law, which is a characteristic feature of the criticality. Here, we study the origin of the criticality on non-growing and growing networks based on the competition induced criticality model. From the direct Mote Carlo simulations and the exact mapping into the position dependent biased random walk (PDBRW), we find that the meme popularity distribution satisfies a very robust power- law with exponent α = 3/2 if there is an innovation process. On the other hand, if there is no innovation, then we find that the meme popularity distribution is bounded and highly skewed for early transient time periods, while it satisfies a power-law with exponent α ≠ 3/2 for intermediate time periods. The exact mapping into PDBRW clearly shows that the balance between the creation of new memes by the innovation process and the extinction of old memes is the key factor for the criticality. We confirm that the balance for the criticality sustains for relatively small innovation rate. Therefore, the innovation processes with significantly influential memes should be the simple and fundamental processes which cause the critical distribution of the meme popularity in real social networks.

A model-free scheme for meme ranking in social media

The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, and tags). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve this problem well. They are either daunted by unpractical assumptions or incapability of characterizing dynamic information. As such, in this paper, we elaborate a model-free scheme to rank online memes in the context of social media. This scheme is capable to characterize the nonlinear interactions of online users, which mark the process of meme diffusion. Empirical studies on two large-scale, real-world datasets (one in English and one in Chinese) demonstrate the effectiveness and robustness of the proposed scheme. In addition, due to its fine-grained modeling of user dynamics, this ranking scheme can also be utilized to explain meme popularity through the lens of social influence.

Average is boring: how similarity kills a meme's success.

Every day we are exposed to different ideas, or memes, competing with each other for our attention. Previous research explained popularity and persistence heterogeneity of memes by assuming them in competition for limited attention resources, distributed in a heterogeneous social network. Little has been said about what characteristics make a specific meme more likely to be successful. We propose a similarity-based explanation: memes with higher similarity to other memes have a significant disadvantage in their potential popularity. We employ a meme similarity measure based on semantic text analysis and computer vision to prove that a meme is more likely to be successful and to thrive if its characteristics make it unique. Our results show that indeed successful memes are located in the periphery of the meme similarity space and that our similarity measure is a promising predictor of a meme success.

Competition-Induced Criticality in a Model of Meme Popularity

Heavy-tailed distributions of meme popularity occur naturally in a model of meme diffusion on social networks. Competition between multiple memes for the limited resource of user attention is identified as the mechanism that poises the system at criticality. The popularity growth of each meme is described by a critical branching process, and asymptotic analysis predicts power-law distributions of popularity with very heavy tails (exponent α<2, unlike preferential-attachment models), similar to those seen in empirical data.

Internet Memes as an Information Dissemination Tool for Libraries: The Ateneo De Manila University Experience

An increasing popularity of Internet memes can be observed particularly among college students. Websites such as 9gag, Reddit, and 4chan provide a venue for these memes to be visited by the general public. Having observed this trend, the Rizal Library of the Ateneo de Manila University embarked on a strategy to use Internet memes as an information dissemination tool. The initial phase involved creating social media accounts for the Rizal Library to serve as the initial source of information. After which, various marketing strategies were implemented to amplify the followership of the aforementioned accounts, therefore increasing the figures of the targeted audience. The final phase involved creation of the memes and the evaluation of results. After the completion of the project, statistics reflected an exponential growth in the reach of the advertisements posted by the Rizal Library, with the audience themselves contributing to the information dissemination process. This can be supported by the enhanced usage of the services and collections which were promoted using memes.

Tracking Large-Scale Video Remix in Real-World Events

Content sharing networks, such as YouTube, contain traces of both explicit online interactions (such as likes, comments, or subscriptions), as well as latent interactions (such as quoting, or remixing, parts of a video). We propose visual memes, or frequently re-posted short video segments, for detecting and monitoring such latent video interactions at scale. Visual memes are extracted by scalable detection algorithms that we develop, with high accuracy. We further augment visual memes with text, via a statistical model of latent topics. We model content interactions on YouTube with visual memes, defining several measures of influence and building predictive models for meme popularity. Experiments are carried out with over 2 million video shots from more than 40,000 videos on two prominent news events in 2009: the election in Iran and the swine flu epidemic. In these two events, a high percentage of videos contain remixed content, and it is apparent that traditional news media and citizen journalists have different roles in disseminating remixed content. We perform two quantitative evaluations for annotating visual memes and predicting their popularity. The proposed joint statistical model of visual memes and words outperforms an alternative concurrence model, with an average error of 2% for predicting meme volume and 17% for predicting meme lifespan.

Erratum: Competition among memes in a world with limited attention

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.

Competition among memes in a world with limited attention.

The wide adoption of social media has increased the competition among ideas for our finite attention. We employ a parsimonious agent-based model to study whether such a competition may affect the popularity of different memes, the diversity of information we are exposed to, and the fading of our collective interests for specific topics. Agents share messages on a social network but can only pay attention to a portion of the information they receive. In the emerging dynamics of information diffusion, a few memes go viral while most do not. The predictions of our model are consistent with empirical data from Twitter, a popular microblogging platform. Surprisingly, we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas.