Contraction of online response to major events

Quantifying regularities in behavioral dynamics is of crucial interest for understanding collective social events such as panics or political revolutions. With the widespread use of digital communication media it has become possible to study massive data streams of user-created content in which individuals express their sentiments, often towards a specific topic. Here we investigate messages from various online media created in response to major, collectively followed events such as sport tournaments, presidential elections or a large snow storm. We relate content length and message rate, and find a systematic correlation during events which can be described by a power law relation - the higher the excitation the shorter the messages. We show that on the one hand this effect can be observed in the behavior of most regular users, and on the other hand is accentuated by the engagement of additional user demographics who only post during phases of high collective activity. Further, we identify the distributions of content lengths as lognormals in line with statistical linguistics, and suggest a phenomenological law for the systematic dependence of the message rate to the lognormal mean parameter. Our measurements have practical implications for the design of micro-blogging and messaging services. In the case of the existing service Twitter, we show that the imposed limit of 140 characters per message currently leads to a substantial fraction of possibly dissatisfying to compose tweets that need to be truncated by their users.Comment: project page: http://senseable.mit.edu/tweetbursts

DropCompute: simple and more robust distributed synchronous training via compute variance reduction

Background: Distributed training is essential for large scale training of deep neural networks (DNNs). The dominant methods for large scale DNN training are synchronous (e.g. All-Reduce), but these require waiting for all workers in each step. Thus, these methods are limited by the delays caused by straggling workers. Results: We study a typical scenario in which workers are straggling due to variability in compute time. We find an analytical relation between compute time properties and scalability limitations, caused by such straggling workers. With these findings, we propose a simple yet effective decentralized method to reduce the variation among workers and thus improve the robustness of synchronous training. This method can be integrated with the widely used All-Reduce. Our findings are validated on large-scale training tasks using 200 Gaudi Accelerators.Comment: https://github.com/paper-submissions/dropcomput

Cities, information, and the epigraphic habit: re-evaluating the links between the numbers of inscriptions and the sizes of sites

Among classical scholars there is a widespread assumption that there is no relationship between the sizes of communities and their epigraphic output. In this article, I offer a new model, which suggests two hypotheses for how inscriptions increase with population, depending on whether they can be regarded as a form of infrastructure or a measure of wealth or disposable income. I show that, despite the variation between sites, there is nonetheless a consistent relationship between the numbers of inscriptions and the estimated populations of sites. The numbers of inscriptions increase slower than the estimated populations of sites, however, suggesting that they acted as a form of information infrastructure. This has important implications for our understanding of the mechanisms for transmitting information in ancient contexts, suggesting several avenues for future research

Emergent Forms of Online Sociality in Disasters Arising from Natural Hazards

Disasters arising from natural hazards are associated with breakdown of existing structures, but they also result in creation of new social ties in the process of self-organization and problem solving by those affected. This dissertation focuses on emergent forms of sociality that arise in the context of crisis. Specifically, it considers collaborative work practices, social network structures, and organizational forms that emerge on social media during disasters arising from natural hazards. Social media platforms support highly-distributed social environments, and the forms of sociality that emerge in these contexts are affected by the affordances of their technical features, especially those that more or less successfully facilitate the creation of a shared information space. Thus, this dissertation is organized around two important aspects of social media spaces: the availability of an explicitly-shared site of work and the availability of a visible, legible record of activity.This dissertation investigates the forms of sociality that emerge during disasters in three social media activities: retweeting, crisis mapping in OpenStreetMap (OSM), and Twitter reply conversations. These three social media activities highlight various availability of an explicitly-shared site of work and visible record of activity. The studies of retweeting and reply conversations investigate the Twitter activity in response to the 2012 Hurricane Sandy—the second costliest hurricane in US history and the most tweeted about event to date at the time. Analysis of crisis mapping in OpenStreetMap—an open, editable, volunteer-based map of the world—focuses on the OSM activity after the 2010 Haiti earthquake, which was the first major disaster event supported by OpenStreetMap. For these investigations, the dissertation elaborates and develops human-centered data science methods—a set of methodological approaches that both harness the power of computational techniques and account for the highly-situated nature of the social activity in crisis. Finally, the dissertation positions the findings from the three studies within the larger context of high-tempo, high-volume social media activity and highlights how the framework of the two intersecting dimensions of the shared information space reveals larger patterns within the emergent forms of sociality across contexts

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression

Characterising the Social Media Temporal Response to External Events

In recent years social media has become a crucial component of online information propagation. It is one of the fastest responding mediums to offline events, significantly faster than traditional news services. Popular social media posts can spread rapidly through the internet, potentially spreading misinformation and affecting human beliefs and behaviour. The nature of how social media responds allows inference about events themselves and provides insight into human behavioural characteristics. However, despite its importance, researchers don’t have a strong understanding of the temporal dynamics of this information flow. This thesis aims to improve understanding of the temporal relationship between events, news and associated social media activity. We do this by examining the temporal Twitter response to stimuli for various case studies, primarily based around politics and sporting events. The first part of the thesis focuses on the relationships between Twitter and news media. Using Granger causality, we provide evidence that the social media reaction to events is faster than the traditional news reaction. We also consider how accurately tweet and news volumes can be predicted, given other variables. The second part of the thesis examines information cascades. We show that the decay of retweet rates is well-modelled as a power law with exponential cutoff, providing a better model than the widely used power law. This finding, explained using human prioritisation of tasks, then allows the development of a method to estimate the size of a retweet cascade. The third major part of the thesis concerns tweet clustering methods in response to events. We examine how the likelihood that two tweets are related varies, given the time difference between them, and use this finding to create a clustering method using both textual and temporal information. We also develop a method to estimate the time of the event that caused the corresponding social media reaction.Thesis (Ph.D.) -- University of Adelaide, School of Mathematical Sciences, 201

Networks, Epidemics and Collective Behavior: from Physics to Data Science

In the final quarter of the XX century the classical reductionist approach that had been driving the development of physics was questioned. Instead, it was proposed that systems were arranged in hierarchies so that the upper level had to convey to the rules of the lower level, but at the same time it could also exhibit its own laws that could not be inferred from the ones of its fundamental constituents. This observation led to the creation of a new field known as complex systems. This novel view was, however, not restricted to purely physical systems. It was soon noticed that very different systems covering a huge array of fields, from ecology to sociology or economics, could also be analyzed as complex systems. Furthermore, it allowed physicists to contribute with their knowledge and methods in the development of research in those areas. In this thesis we tackle problems covering three areas of complex systems: networks, which are one of the main mathematical tools used to study complex systems; epidemic spreading, which is one of the fields in which the application of a complex systems perspective has been more successful; and the study of collective behavior, which has attracted a lot of attention since data from human behavior in huge amounts has been made available thanks to social networks. In fact, data is also the main driver of our discussion of the other two areas. In particular, we use novel sources of data to challenge some of the classical assumptions that have been made in the study of networks as well as in the development of models of epidemic spreading. In the case of networks, the problem of null models is addressed using tools coming from statistical physics. We show that anomalies in networks can be just a consequence of model oversimplification. Then, we extend the framework to generate contact networks for the spreading of diseases in populations in which both the contact structure and the age distribution of the population are important. Next, we follow the historical development of mathematical epidemiology and revisit the assumptions that were made when there was no data about the real behavior of this kind of systems. We show that one of the most important quantities used in this kind of studies, the basic reproduction number, is not properly defined for real systems. Similarly, we extend the theoretical framework of epidemic spreading on directed networks to multilayer systems. Furthermore, we show that the challenge of incorporating data to models is not only restricted to the problem of obtaining it, but that it is also really important to be aware of its characteristics to do it properly.Lastly, we conclude the thesis studying two examples of collective behavior using data extracted from online systems. We do so using techniques that were originally developed for other purposes, such as earthquake prediction. Yet, we demonstrate that they can also be used to study this new type of systems. Furthermore, we show that, despite their unique characteristics, they possess properties similar to the ones that have been observed in the offline world. This not only means that modern societies are intertwined with the online world, but it also signals that if we aim to understand socio-technical systems a holistic approach, as the one proposed by complex systems, is indispensable.<br /