Early Warning Analysis for Social Diffusion Events

There is considerable interest in developing predictive capabilities for social diffusion processes, for instance to permit early identification of emerging contentious situations, rapid detection of disease outbreaks, or accurate forecasting of the ultimate reach of potentially viral ideas or behaviors. This paper proposes a new approach to this predictive analytics problem, in which analysis of meso-scale network dynamics is leveraged to generate useful predictions for complex social phenomena. We begin by deriving a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes taking place over social networks with realistic topologies; this modeling approach is inspired by recent work in biology demonstrating that S-HDS offer a useful mathematical formalism with which to represent complex, multi-scale biological network dynamics. We then perform formal stochastic reachability analysis with this S-HDS model and conclude that the outcomes of social diffusion processes may depend crucially upon the way the early dynamics of the process interacts with the underlying network's community structure and core-periphery structure. This theoretical finding provides the foundations for developing a machine learning algorithm that enables accurate early warning analysis for social diffusion events. The utility of the warning algorithm, and the power of network-based predictive metrics, are demonstrated through an empirical investigation of the propagation of political memes over social media networks. Additionally, we illustrate the potential of the approach for security informatics applications through case studies involving early warning analysis of large-scale protests events and politically-motivated cyber attacks

Hipsters on Networks: How a Small Group of Individuals Can Lead to an Anti-Establishment Majority

The spread of opinions, memes, diseases, and "alternative facts" in a population depends both on the details of the spreading process and on the structure of the social and communication networks on which they spread. In this paper, we explore how \textit{anti-establishment} nodes (e.g., \textit{hipsters}) influence the spreading dynamics of two competing products. We consider a model in which spreading follows a deterministic rule for updating node states (which describe which product has been adopted) in which an adjustable fraction $p_{\rm Hip}$ of the nodes in a network are hipsters, who choose to adopt the product that they believe is the less popular of the two. The remaining nodes are conformists, who choose which product to adopt by considering which products their immediate neighbors have adopted. We simulate our model on both synthetic and real networks, and we show that the hipsters have a major effect on the final fraction of people who adopt each product: even when only one of the two products exists at the beginning of the simulations, a very small fraction of hipsters in a network can still cause the other product to eventually become the more popular one. To account for this behavior, we construct an approximation for the steady-state adoption fraction on $k$-regular trees in the limit of few hipsters. Additionally, our simulations demonstrate that a time delay $\tau$ in the knowledge of the product distribution in a population, as compared to immediate knowledge of product adoption among nearest neighbors, can have a large effect on the final distribution of product adoptions. Our simple model and analysis may help shed light on the road to success for anti-establishment choices in elections, as such success can arise rather generically in our model from a small number of anti-establishment individuals and ordinary processes of social influence on normal individuals.Comment: Extensively revised, with much new analysis and numerics The abstract on arXiv is a shortened version of the full abstract because of space limit

‘The uses of ethnography in the science of cultural evolution’. Commentary on Mesoudi, A., Whiten, A. and K. Laland ‘Toward a unified science of cultural evolution’

There is considerable scope for developing a more explicit role for ethnography within the research program proposed in the article. Ethnographic studies of cultural micro-evolution would complement experimental approaches by providing insights into the “natural” settings in which cultural behaviours occur. Ethnography can also contribute to the study of cultural macro-evolution by shedding light on the conditions that generate and maintain cultural lineages

Modeling the adoption of innovations in the presence of geographic and media influences

While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reflecting real geography or influence from mass media forces. In this article, we show these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homopholy both amongst individuals with similar propensities to adopt a technology and geographic location are critical to reproduce features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves

Tensor Learning for Recovering Missing Information: Algorithms and Applications on Social Media

Real-time social systems like Facebook, Twitter, and Snapchat have been growing rapidly, producing exabytes of data in different views or aspects. Coupled with more and more GPS-enabled sharing of videos, images, blogs, and tweets that provide valuable information regarding “who”, “where”, “when” and “what”, these real-time human sensor data promise new research opportunities to uncover models of user behavior, mobility, and information sharing. These real-time dynamics in social systems usually come in multiple aspects, which are able to help better understand the social interactions of the underlying network. However, these multi-aspect datasets are often raw and incomplete owing to various unpredictable or unavoidable reasons; for instance, API limitations and data sampling policies can lead to an incomplete (and often biased) perspective on these multi-aspect datasets. This missing data could raise serious concerns such as biased estimations on structural properties of the network and properties of information cascades in social networks. In order to recover missing values or information in social systems, we identify “4S” challenges: extreme sparsity of the observed multi-aspect datasets, adoption of rich side information that is able to describe the similarities of entities, generation of robust models rather than limiting them on specific applications, and scalability of models to handle real large-scale datasets (billions of observed entries). With these challenges in mind, this dissertation aims to develop scalable and interpretable tensor-based frameworks, algorithms and methods for recovering missing information on social media. In particular, this dissertation research makes four unique contributions: _ The first research contribution of this dissertation research is to propose a scalable framework based on low-rank tensor learning in the presence of incomplete information. Concretely, we formally define the problem of recovering the spatio-temporal dynamics of online memes and tackle this problem by proposing a novel tensor-based factorization approach based on the alternative direction method of multipliers (ADMM) with the integration of the latent relationships derived from contextual information among locations, memes, and times. _ The second research contribution of this dissertation research is to evaluate the generalization of the proposed tensor learning framework and extend it to the recommendation problem. In particular, we develop a novel tensor-based approach to solve the personalized expert recommendation by integrating both the latent relationships between homogeneous entities (e.g., users and users, experts and experts) and the relationships between heterogeneous entities (e.g., users and experts, topics and experts) from the geo-spatial, topical, and social contexts. _ The third research contribution of this dissertation research is to extend the proposed tensor learning framework to the user topical profiling problem. Specifically, we propose a tensor-based contextual regularization model embedded into a matrix factorization framework, which leverages the social, textual, and behavioral contexts across users, in order to overcome identified challenges. _ The fourth research contribution of this dissertation research is to scale up the proposed tensor learning framework to be capable of handling real large-scale datasets that are too big to fit in the main memory of a single machine. Particularly, we propose a novel distributed tensor completion algorithm with the trace-based regularization of the auxiliary information based on ADMM under the proposed tensor learning framework, which is designed to scale up to real large-scale tensors (e.g., billions of entries) by efficiently computing auxiliary variables, minimizing intermediate data, and reducing the workload of updating new tensors

Memetic moments : the speed of twitter memes

This paper examines how speed shapes internet culture. To do so, it analyses ‘memetic moments’ on Twitter, short-lived and rapidly circulated memes that quickly reach saturation. The paper examines two ‘memetic moments’ on Twitter in 2018 and 2019 to assess how they develop over time. Each case study comprises a week’s worth of relevant tweets that were analysed for temporal patterns. We analyse these ‘memetic moments’ through Lefebvre’s (2004) work on rhythmanalysis, arguing that the temporal patterns of memes on Twitter can be understood through his concepts of repetition, presence and dialogue. While seemingly trivial, memetic moments underscore the didactic relationship between social media and news media while also providing a way to approach complex social issues

Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics

With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations