The advent of social media has provided data and insights about how people relate to information and culture. While information is composed by bits and its fundamental building bricks are relatively well understood, the same cannot be said for culture. The fundamental cultural unit has been defined as a "meme". Memes are defined in literature as specific fundamental cultural traits, that are floating in their environment together. Just like genes carried by bodies, memes are carried by cultural manifestations like songs, buildings or pictures. Memes are studied in their competition for being successfully passed from one generation of minds to another, in different ways. In this paper we choose an empirical approach to the study of memes. We downloaded data about memes from a well-known website hosting hundreds of different memes and thousands of their implementations. From this data, we empirically describe the behavior of these memes. We statistically describe meme occurrences in our dataset and we delineate their fundamental traits, along with those traits that make them more or less apt to be successful.
Finally, there are in literature also some publications about memetics, the proposed branch of science that should study memes. Some of the pioneering works are @cite_12 , @cite_1 and @cite_5 . Our work is different from these examples in literature as we are focused particularly on internet memes. Our approach is more data driven, as internet memes are more easily traceable as they leave a measurable footprint, this makes our paper a further contribution w.r.t these works.
@cite_5 @cite_1 @cite_12
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The advent of social media has brought Internet memes, a unique social phenomenon, to the front stage of the Web. Embodied in the form of images with text descriptions, little do we know about the “language of memes”. In this paper, we statistically study the correlations among popular memes and their wordings, and generate meme descriptions from raw images. To do this, we take a multimodal approach—we propose a robust nonparanormal model to learn the stochastic dependencies among the image, the candidate descriptions, and the popular votes. In experiments, we show that combining text and vision helps identifying popular meme descriptions; that our nonparanormal model is able to learn dense and continuous vision features jointly with sparse and discrete text features in a principled manner, outperforming various competitive baselines; that our system can generate meme descriptions using a simple pipeline. We are surrounded by information in the 21st Century: we are bombarded by advertising, attitudes, celebrities, news, wars, fashion, the latest fads...the sheer amount of information we have access to appears untameable, unworkable, and too much to gain sense from unless we pick and choose very carefully. However, our choices are very often made for us as the result of advertising, media companies, the government and popular culture. The results of these choices are called memes, and their impact is shaping not just society but us individually, and on a core level, beyond psychology, personal free will, and even genetics. The very first book on the subject of memes, and how they behave just like viruses, this is an incredible study into the power of communication and 'going along with everyone else'. "Virus of the Mind" explains just how we are 'infected' by the deliberate shaping of society's attitudes and behaviours - and how we can cure ourselves. Background: Intricate maps of science have been created from citation data to visualize the structure of scientific activity. However, most scientific publications are now accessed online. Scholarly web portals record detailed log data at a scale that exceeds the number of all existing citations combined. Such log data is recorded immediately upon publication and keeps track of the sequences of user requests (clickstreams) that are issued by a variety of users across many different domains. Given these advantages of log datasets over citation data, we investigate whether they can produce high-resolution, more current maps of science. Methodology: Over the course of 2007 and 2008, we collected nearly 1 billion user interactions recorded by the scholarly web portals of some of the most significant publishers, aggregators and institutional consortia. The resulting reference data set covers a significant part of world-wide use of scholarly web portals in 2006, and provides a balanced coverage of the humanities, social sciences, and natural sciences. A journal clickstream model, i.e. a first-order Markov chain, was extracted from the sequences of user interactions in the logs. The clickstream model was validated by comparing it to the Getty Research Institute’s Architecture and Art Thesaurus. The resulting model was visualized as a journal network that outlines the relationships between various scientific domains and clarifies the connection of the social sciences and humanities to the natural sciences. Conclusions: Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the underrepresentation of the social sciences and humanities that is commonly found in citation data. Divide and conquer—the strategy that science uses to cope with the mountains of printed matter it produces—appears on the surface to serve us well. Science organizes itself into manageable units—scientific specialties—and so its literature is created and assimilated in manageable chunks or units. But a few clouds on the horizon ought not to go unexamined. First, most of the units are no doubt logically related to other units. Second, there are far more combinations of units, therefore far more potential relationships among the units, than there are units. Third, the system is not organized to cope with combinations. I suggest that important relationships might be escaping our notice. Individual units of literature are created to some degree independently of one another, and, insofar as that is so, the logical connections among the units, though inevitable, may be unintended by and even unknown to their creators. Until those fragments, like scattered pieces of a puzzle, are brought together, the relationships among them may remain undiscovered—even though the isolated pieces might long have been public knowledge. My purpose in this essay is to show, by means of an example, how this might happen. I shall identify two units of literature that are logically connected but noninteractive; neither seems to acknowledge the other to any substantial degree. Yet the logical connections, once apparent, lead to a potentially useful and possibly new hypothesis.