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

Long-time behaviour of discretizations of the simple pendulum equation

We compare the performance of several discretizations of the simple pendulum equation in a series of numerical experiments. The stress is put on the long-time behaviour. We choose for the comparison numerical schemes which preserve the qualitative features of solutions (like periodicity). All these schemes are either symplectic maps or integrable (preserving the energy integral) maps, or both. We describe and explain systematic errors (produced by any method) in numerical computations of the period and the amplitude of oscillations. We propose a new numerical scheme which is a modification of the discrete gradient method. This discretization preserves (almost exactly) the period of small oscillations for any time step.Comment: 41 pages, including 18 figures and 4 table

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

Comment on `conservative discretizations of the Kepler motion'

We show that the exact integrator for the classical Kepler motion, recently found by Kozlov ({\it J. Phys. A: Math. Theor.\} {\bf 40} (2007) 4529-4539), can be derived in a simple natural way (using well known exact discretization of the harmonic oscillator). We also turn attention on important earlier references, where the exact discretization of the 4-dimensional isotropic harmonic oscillator has been applied to the perturbed Kepler problem.Comment: 6 page

Dynamics of conflicts in Wikipedia

In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.Comment: Supporting information adde

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

Inverse-kinematics one-neutron pickup with fast rare-isotope beams

New measurements and reaction model calculations are reported for single neutron pickup reactions onto a fast \nuc{22}{Mg} secondary beam at 84 MeV per nucleon. Measurements were made on both carbon and beryllium targets, having very different structures, allowing a first investigation of the likely nature of the pickup reaction mechanism. The measurements involve thick reaction targets and $\gamma$-ray spectroscopy of the projectile-like reaction residue for final-state resolution, that permit experiments with low incident beam rates compared to traditional low-energy transfer reactions. From measured longitudinal momentum distributions we show that the \nuc{12}{C} (\nuc{22}{Mg},\nuc{23}{Mg}+\gamma)X reaction largely proceeds as a direct two-body reaction, the neutron transfer producing bound \nuc{11}{C} target residues. The corresponding reaction on the \nuc{9}{Be} target seems to largely leave the \nuc{8}{Be} residual nucleus unbound at excitation energies high in the continuum. We discuss the possible use of such fast-beam one-neutron pickup reactions to track single-particle strength in exotic nuclei, and also their expected sensitivity to neutron high-$\ell$ (intruder) states which are often direct indicators of shell evolution and the disappearance of magic numbers in the exotic regime.Comment: 8 pages, 5 figure