Clustering Memes in Social Media

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'13), 201

Transfer Learning for Multi-language Twitter Election Classification

Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure

Magnetic Effects at the Edge of the Solar System: MHD Instabilities, the de Laval nozzle Effect and an Extended Jet

To model the interaction between the solar wind and the interstellar wind, magnetic fields must be included. Recently Opher et al. 2003 found that, by including the solar magnetic field in a 3D high resolution simulation using the University of Michigan BATS-R-US code, a jet-sheet structure forms beyond the solar wind Termination Shock. Here we present an even higher resolution three-dimensional case where the jet extends for $150AU$ beyond the Termination Shock. We discuss the formation of the jet due to a de Laval nozzle effect and it's su bsequent large period oscillation due to magnetohydrodynamic instabilities. To verify the source of the instability, we also perform a simplified two dimensional-geometry magnetohydrodynamic calculation of a plane fluid jet embedded in a neutral sheet with the profiles taken from our 3D simulation. We find remarkable agreement with the full three-dimensional evolution. We compare both simulations and the temporal evolution of the jet showing that the sinuous mode is the dominant mode that develops into a velocity-shear-instability with a growth rate of $5 \times 10^{-9} sec^{-1}=0.027 years^{-1}$. As a result, the outer edge of the heliosphere presents remarkable dynamics, such as turbulent flows caused by the motion of the jet. Further study, e.g., including neutrals and the tilt of the solar rotation from the magnetic axis, is required before we can definitively address how this outer boundary behaves. Already, however, we can say that the magnetic field effects are a major player in this region changing our previous notion of how the solar system ends.Comment: 24 pages, 13 figures, accepted for publication in Astrophysical Journal (2004

Persistence and Uncertainty in the Academic Career

Understanding how institutional changes within academia may affect the overall potential of science requires a better quantitative representation of how careers evolve over time. Since knowledge spillovers, cumulative advantage, competition, and collaboration are distinctive features of the academic profession, both the employment relationship and the procedures for assigning recognition and allocating funding should be designed to account for these factors. We study the annual production n_{i}(t) of a given scientist i by analyzing longitudinal career data for 200 leading scientists and 100 assistant professors from the physics community. We compare our results with 21,156 sports careers. Our empirical analysis of individual productivity dynamics shows that (i) there are increasing returns for the top individuals within the competitive cohort, and that (ii) the distribution of production growth is a leptokurtic "tent-shaped" distribution that is remarkably symmetric. Our methodology is general, and we speculate that similar features appear in other disciplines where academic publication is essential and collaboration is a key feature. We introduce a model of proportional growth which reproduces these two observations, and additionally accounts for the significantly right-skewed distributions of career longevity and achievement in science. Using this theoretical model, we show that short-term contracts can amplify the effects of competition and uncertainty making careers more vulnerable to early termination, not necessarily due to lack of individual talent and persistence, but because of random negative production shocks. We show that fluctuations in scientific production are quantitatively related to a scientist's collaboration radius and team efficiency.Comment: 29 pages total: 8 main manuscript + 4 figs, 21 SI text + fig

Characterizing and modeling the dynamics of online popularity

Online popularity has enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems, the Wikipedia and an entire country's Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and inter-event time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics.Comment: 5 pages, 4 figures. Modeling part detailed. Final version published in Physical Review Letter

Beating the news using social media: the case study of American Idol

We present a contribution to the debate on the predictability of social events using big data analytics. We focus on the elimination of contestants in the American Idol TV shows as an example of a well defined electoral phenomenon that each week draws millions of votes in the USA. This event can be considered as basic test in a simplified environment to assess the predictive power of Twitter signals. We provide evidence that Twitter activity during the time span defined by the TV show airing and the voting period following it correlates with the contestants ranking and allows the anticipation of the voting outcome. Twitter data from the show and the voting period of the season finale have been analyzed to attempt the winner prediction ahead of the airing of the official result. We also show that the fraction of tweets that contain geolocation information allows us to map the fanbase of each contestant, both within the US and abroad, showing that strong regional polarizations occur. The geolocalized data are crucial for the correct prediction of the final outcome of the show, pointing out the importance of considering information beyond the aggregated Twitter signal. Although American Idol voting is just a minimal and simplified version of complex societal phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators that may be able to anticipate the future unfolding of opinion formation events

In-beam gamma-ray spectroscopy of 35Mg and 33Na

Excited states in the very neutron-rich nuclei 35Mg and 33Na were populated in the fragmentation of a 38Si projectile beam on a Be target at 83 MeV/u beam energy. We report on the first observation of gamma-ray transitions in 35Mg, the odd-N neighbor of 34Mg and 36Mg, which are known to be part of the "Island of Inversion" around N = 20. The results are discussed in the framework of large- scale shell-model calculations. For the A = 3Z nucleus 33Na, a new gamma-ray transition was observed that is suggested to complete the gamma-ray cascade 7/2+ --> 5/2+ --> 3/2+ gs connecting three neutron 2p-2h intruder states that are predicted to form a close-to-ideal K = 3/2 rotational band in the strong-coupling limit.Comment: Accepted for publication Phys. Rev. C. March 16, 2011: Replaced figures 3 and 5. We thank Alfredo Poves for pointing out a problem with the two figure