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.
In the Introduction we stated that there are several works studying internet memes. For example, @cite_15 studies the dynamics of competitions of memes spreading in two distinct social networks on different social media (Facebook and Twitter). Also @cite_22 addresses the problem of competition among memes, assuming that each user can follow only a handful of memes at the same time. Meme and information spread is also a problem definition addressed in computer science with different methodological approaches: works like @cite_0 , @cite_6 and @cite_16 provide algorithms and frameworks to analyze how information spreads in a network of people. While those papers examine different cascade information spreads as independent, in @cite_8 different spread events on the same network are analyzed at the same time, as different memes influence each other while trying to span on the same set of minds. Other computer science tools help us tracking memes over the Web @cite_11 . As an application, @cite_10 deals with cooperative behavior.
@cite_22 @cite_8 @cite_6 @cite_0 @cite_15 @cite_16 @cite_10 @cite_11
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How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications. If a false rumor propagates via Twitter, while the truth propagates between friends in Facebook, which one will prevail? This question captures the essence of the problem we address here. We study the intertwined propagation of two competing "memes" (or viruses, rumors, products etc.) in a composite network. A key novelty is the use of a composite network, which in its simplest model is defined as a single set of nodes with two distinct types of edges interconnecting them. Each meme spreads across the composite network in accordance to an SIS-like propagation model (a flu-like infection-recovery). To study the epidemic behavior of our system, we formulate it as a non-linear dynamic system (NLDS). We develop a metric for each meme that is based on the eigenvalue of an appropriately constructed matrix and argue that this metric plays a key role in determining the "winning" meme. First, we prove that our metric determines the tipping point at which both memes become extinct eventually. Second, we conjecture that the meme with the strongest metric will most likely prevail over the other, and we show evidence of that via simulations in both real and synthetic composite networks. Our work is among the first to study the interplay between two competing memes in composite networks. We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes. The real-time information on news sites, blogs and social networking sites changes dynamically and spreads rapidly through the Web. Developing methods for handling such information at a massive scale requires that we think about how information content varies over time, how it is transmitted, and how it mutates as it spreads. We describe the News Information Flow Tracking, Yay! (NIFTY) system for large scale real-time tracking of "memes" - short textual phrases that travel and mutate through the Web. NIFTY is based on a novel highly-scalable incremental meme-clustering algorithm that efficiently extracts and identifies mutational variants of a single meme. NIFTY runs orders of magnitude faster than our previous Memetracker system, while also maintaining better consistency and quality of extracted memes. We demonstrate the effectiveness of our approach by processing a 20 terabyte dataset of 6.1 billion blog posts and news articles that we have been continuously collecting for the last four years. NIFTY extracted 2.9 billion unique textual phrases and identified more than 9 million memes. Our meme-tracking algorithm was able to process the entire dataset in less than five days using a single machine. Furthermore, we also provide a live deployment of the NIFTY system that allows users to explore the dynamics of online news in near real-time. Tracking new topics, ideas, and "memes" across the Web has been an issue of considerable interest. Recent work has developed methods for tracking topic shifts over long time scales, as well as abrupt spikes in the appearance of particular named entities. However, these approaches are less well suited to the identification of content that spreads widely and then fades over time scales on the order of days - the time scale at which we perceive news and events. We develop a framework for tracking short, distinctive phrases that travel relatively intact through on-line text; developing scalable algorithms for clustering textual variants of such phrases, we identify a broad class of memes that exhibit wide spread and rich variation on a daily basis. As our principal domain of study, we show how such a meme-tracking approach can provide a coherent representation of the news cycle - the daily rhythms in the news media that have long been the subject of qualitative interpretation but have never been captured accurately enough to permit actual quantitative analysis. We tracked 1.6 million mainstream media sites and blogs over a period of three months with the total of 90 million articles and we find a set of novel and persistent temporal patterns in the news cycle. In particular, we observe a typical lag of 2.5 hours between the peaks of attention to a phrase in the news media and in blogs respectively, with divergent behavior around the overall peak and a "heartbeat"-like pattern in the handoff between news and blogs. We also develop and analyze a mathematical model for the kinds of temporal variation that the system exhibits. 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. Internet memes are phenomena that rapidly gain popularity or notoriety on the Internet. Often, modifications or spoofs add to the profile of the original idea thus turning it into a phenomenon that transgresses social and cultural boundaries. It is commonly assumed that Internet memes spread virally but scientific evidence as to this assumption is scarce. In this paper, we address this issue and investigate the epidemic dynamics of 150 famous Internet memes. Our analysis is based on time series data that were collected from Google Insights, Delicious, Digg, and StumbleUpon. We find that differential equation models from mathematical epidemiology as well as simple log-normal distributions give a good account of the growth and decline of memes. We discuss the role of log-normal distributions in modeling Internet phenomena and touch on practical implications of our findings. 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. 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. A rumor is commonly defined as a statement whose true value is unverifiable. Rumors may spread misinformation (false information) or disinformation (deliberately false information) on a network of people. Identifying rumors is crucial in online social media where large amounts of information are easily spread across a large network by sources with unverified authority. In this paper, we address the problem of rumor detection in microblogs and explore the effectiveness of 3 categories of features: content-based, network-based, and microblog-specific memes for correctly identifying rumors. Moreover, we show how these features are also effective in identifying disinformers, users who endorse a rumor and further help it to spread. We perform our experiments on more than 10,000 manually annotated tweets collected from Twitter and show how our retrieval model achieves more than 0.95 in Mean Average Precision (MAP). Finally, we believe that our dataset is the first large-scale dataset on rumor detection. It can open new dimensions in analyzing online misinformation and other aspects of microblog conversations. A new wave of growing antisemitism, driven by fringe Web communities, is an increasingly worrying presence in the socio-political realm. The ubiquitous and global nature of the Web has provided tools used by these groups to spread their ideology to the rest of the Internet. Although the study of antisemitism and hate is not new, the scale and rate of change of online data has impacted the efficacy of traditional approaches to measure and understand this worrying trend. In this paper, we present a large-scale, quantitative study of online antisemitism. We collect hundreds of million comments and images from alt-right Web communities like 4chan's Politically Incorrect board ( pol ) and the Twitter clone, Gab. Using scientifically grounded methods, we quantify the escalation and spread of antisemitic memes and rhetoric across the Web. We find the frequency of antisemitic content greatly increases (in some cases more than doubling) after major political events such as the 2016 US Presidential Election and the "Unite the Right" rally in Charlottesville. Furthermore, this antisemitism appears in tandem with sharp increases in white ethnic nationalist content on the same communities. We extract semantic embeddings from our corpus of posts and demonstrate how automated techniques can discover and categorize the use of antisemitic terminology. We additionally examine the prevalence and spread of the antisemitic "Happy Merchant" meme, and in particular how these fringe communities influence its propagation to more mainstream services like Twitter and Reddit. Taken together, our results provide a data-driven, quantitative framework for understanding online antisemitism. Our open and scientifically grounded methods serve as a framework to augment current qualitative efforts by anti-hate groups, providing new insights into the growth and spread of antisemitism online. How do blogs cite and influence each other? How do such links evolve? Does the popularity of old blog posts drop exponentially with time? These are some of the questions that we address in this work. Our goal is to build a model that generates realistic cascades, so that it can help us with link prediction and outlier detection. Blogs (weblogs) have become an important medium of information because of their timely publication, ease of use, and wide availability. In fact, they often make headlines, by discussing and discovering evidence about political events and facts. Often blogs link to one another, creating a publicly available record of how information and influence spreads through an underlying social network. Aggregating links from several blog posts creates a directed graph which we analyze to discover the patterns of information propagation in blogspace, and thereby understand the underlying social network. Not only are blogs interesting on their own merit, but our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks. Here we report some surprising findings of the blog linking and information propagation structure, after we analyzed one of the largest available datasets, with 45,000 blogs and 2.2 million blog-postings. Our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks. We also present a simple model that mimics the spread of information on the blogosphere, and produces information cascades very similar to those found in real life. Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation of memes across multiple Web communities, using a processing pipeline based on perceptual hashing and clustering techniques, and a dataset of 160M images from 2.6B posts gathered from Twitter, Reddit, 4chan's Politically Incorrect board ( pol ), and Gab, over the course of 13 months. We group the images posted on fringe Web communities ( pol , Gab, and The_Donald subreddit) into clusters, annotate them using meme metadata obtained from Know Your Meme, and also map images from mainstream communities (Twitter and Reddit) to the clusters. Our analysis provides an assessment of the popularity and diversity of memes in the context of each community, showing, e.g., that racist memes are extremely common in fringe Web communities. We also find a substantial number of politics-related memes on both mainstream and fringe Web communities, supporting media reports that memes might be used to enhance or harm politicians. Finally, we use Hawkes processes to model the interplay between Web communities and quantify their reciprocal influence, finding that pol substantially influences the meme ecosystem with the number of memes it produces, while has a higher success rate in pushing them to other communities. Analyzing the spreading patterns of memes with respect to their topic distributions and the underlying diffusion network structures is an important task in social network analysis. This task in many cases becomes very challenging since the underlying diffusion networks are often hidden, and the topic specific transmission rates are unknown either. In this paper, we propose a continuous time model, TOPICCASCADE, for topicsensitive information diffusion networks, and infer the hidden diffusion networks and the topic dependent transmission rates from the observed time stamps and contents of cascades. One attractive property of the model is that its parameters can be estimated via a convex optimization which we solve with an efficient proximal gradient based block coordinate descent (BCD) algorithm. In both synthetic and real-world data, we show that our method significantly improves over the previous state-of-the-art models in terms of both recovering the hidden diffusion networks and predicting the transmission times of memes. Online social media are complementing and in some cases replacing person-to-person social interaction and redefining the diffusion of information. In particular, microblogs have become crucial grounds on which public relations, marketing, and political battles are fought. We demonstrate a web service that tracks political memes in Twitter and helps detect astroturfing, smear campaigns, and other misinformation in the context of U.S. political elections. We also present some cases of abusive behaviors uncovered by our service. Our web service is based on an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events. Twitter summarizes the great deal of messages posted by users in the form of trending topics that reflect the top conversations being discussed at a given moment. These trending topics tend to be connected to current affairs. Different happenings can give rise to the emergence of these trending topics. For instance, a sports event broadcasted on TV, or a viral meme introduced by a community of users. Detecting the type of origin can facilitate information filtering, enhance real-time data processing, and improve user experience. In this paper, we introduce a typology to categorize the triggers that leverage trending topics: news, current events, memes, and commemoratives. We define a set of straightforward language-independent features that rely on the social spread of the trends to discriminate among those types of trending topics. Our method provides an efficient way to immediately and accurately categorize trending topics without need of external data, outperforming a content-based approach. Microblogging is a communication paradigm in which users post bits of information (brief text updates or micro media such as photos, video or audio clips) that are visible by their communities. When a user finds a “meme” of another user interesting, she can eventually repost it, thus allowing memes to propagate virally trough a social network. In this paper we introduce the meme ranking problem, as the problem of selecting which k memes (among the ones posted 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, and with parameters learnt from real logs of meme propagations. Our experimentation demonstrates the effectiveness and feasibility of these methods. How do blogs cite and influence each other? How do such links evolve? Does the popularity of old blog posts drop exponentially with time? These are some of the questions that we address in this work. Blogs (weblogs) have become an important medium of information because of their timely publication, ease of use, and wide availability. In fact, they often make headlines, by discussing and discovering evidence about political events and facts. Often blogs link to one another, creating a publicly available record of how information and influence spreads through an underlying social network. Aggregating links from several blog posts creates a directed graph which we analyze to discover the patterns of information propagation in blogspace, and thereby understand the underlying social network. Here we report some surprising findings of the blog linking and information propagation structure, after we analyzed one of the largest available datasets, with 45, 000 blogs and ≈ 2.2 million blog-postings. Our analysis also sheds light on how rumors, viruses, and ideas propagate over social and computer networks. There has been a rapidly spreading meme in U.S. pundit and academic circles since 2010 that describes China's recent diplomacy as "newly assertive." This "new assertiveness" meme suffers from two problems. First, it underestimates the complexity of key episodes in Chinese diplomacy in 2010 and overestimates the amount of change. Second, the explanations for the new assertiveness claim suffer from unclear causal mechanisms and lack comparative rigor that would better contextualize China's diplomacy in 2010. An examination of seven cases in Chinese diplomacy at the heart of the new assertiveness meme finds that, in some instances, China's policy has not changed; in others, it is actually more moderate; and in still others, it is a predictable reaction to changed external conditions. In only one case—maritime disputes—does one see more assertive Chinese rhetoric and behavior. The speed and extent with which the newly assertive meme has emerged point to an understudied issue in international relations—namely, the role that online media and the blogosphere play in the creation of conventional wisdoms that might, in turn, constrain policy debates. The assertive China discourse may be a harbinger of this effect as a Sino-U.S. security dilemma emerges. Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hastag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hastag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hastag popularity, which is mostly driven by exogenous factors.