The topology of a discussion: the #occupy case

We analyse a large sample of the Twitter activity developed around the social movement 'Occupy Wall Street' to study the complex interactions between the human communication activity and the semantic content of a discussion. We use a network approach based on the analysis of the bipartite graph @Users-#Hashtags and of its projections: the 'semantic network', whose nodes are hashtags, and the 'users interest network', whose nodes are users In the first instance, we find out that discussion topics (#hashtags) present a high heterogeneity, with the distinct role of the communication hubs where most the 'opinion traffic' passes through. In the second case, the self-organization process of users activity leads to the emergence of two classes of communicators: the 'professionals' and the 'amateurs'. Moreover the network presents a strong community structure, based on the differentiation of the semantic topics, and a high level of structural robustness when a certain set of topics are censored and/or accounts are removed. Analysing the characteristics the @Users-#Hashtags network we can distinguish three phases of the discussion about the movement. Each phase corresponds to specific moment of the movement: from declaration of intent, organisation and development and the final phase of political reactions. Each phase is characterised by the presence of specific #hashtags in the discussion. Keywords: Twitter, Network analysisComment: 13 pages, 9 figure

High connectivity among locally adapted populations of a marine fish (Menidia menidia)

Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 91 (2010): 3526–3537, doi:10.1890/09-0548.1.Patterns of connectivity are important in understanding the geographic scale of local adaptation in marine populations. While natural selection can lead to local adaptation, high connectivity can diminish the potential for such adaptation to occur. Connectivity, defined as the exchange of individuals among subpopulations, is presumed to be significant in most marine species due to life histories that include widely dispersive stages. However, evidence of local adaptation in marine species, such the Atlantic silverside, Menidia menidia, raises questions concerning the degree of connectivity. We examined geochemical signatures in the otoliths, or ear bones, of adult Atlantic silversides collected in 11 locations along the northeastern coast of the United States from New Jersey to Maine in 2004 and eight locations in 2005 using laser ablation inductively coupled plasma mass spectrometry (ICP-MS) and isotope ratio monitoring mass spectrometry (irm-MS). These signatures were then compared to baseline signatures of juvenile fish of known origin to determine natal origin of these adult fish. We then estimated migration distances and the degree of mixing from these data. In both years, fish generally had the highest probability of originating from the same location in which they were captured (0.01–0.80), but evidence of mixing throughout the sample area was present. Furthermore, adult M. menidia exhibit highly dispersive behavior with some fish migrating over 700 km. The probability of adult fish returning to natal areas differed between years, with the probability being, on average, 0.2 higher in the second year. These findings demonstrate that marine species with largely open populations are capable of local adaptation despite apparently high gene flow.This work was funded by the National Science Foundation (grant OCE-0425830 to D. O. Conover and grant OCE- 0134998 to S. R. Thorrold) and the New York State Department of Environmental Conservation

Partisan Asymmetries in Online Political Activity

We examine partisan differences in the behavior, communication patterns and social interactions of more than 18,000 politically-active Twitter users to produce evidence that points to changing levels of partisan engagement with the American online political landscape. Analysis of a network defined by the communication activity of these users in proximity to the 2010 midterm congressional elections reveals a highly segregated, well clustered partisan community structure. Using cluster membership as a high-fidelity (87% accuracy) proxy for political affiliation, we characterize a wide range of differences in the behavior, communication and social connectivity of left- and right-leaning Twitter users. We find that in contrast to the online political dynamics of the 2008 campaign, right-leaning Twitter users exhibit greater levels of political activity, a more tightly interconnected social structure, and a communication network topology that facilitates the rapid and broad dissemination of political information.Comment: 17 pages, 10 figures, 6 table

A meta-analysis of state-of-the-art electoral prediction from Twitter data

Electoral prediction from Twitter data is an appealing research topic. It seems relatively straightforward and the prevailing view is overly optimistic. This is problematic because while simple approaches are assumed to be good enough, core problems are not addressed. Thus, this paper aims to (1) provide a balanced and critical review of the state of the art; (2) cast light on the presume predictive power of Twitter data; and (3) depict a roadmap to push forward the field. Hence, a scheme to characterize Twitter prediction methods is proposed. It covers every aspect from data collection to performance evaluation, through data processing and vote inference. Using that scheme, prior research is analyzed and organized to explain the main approaches taken up to date but also their weaknesses. This is the first meta-analysis of the whole body of research regarding electoral prediction from Twitter data. It reveals that its presumed predictive power regarding electoral prediction has been rather exaggerated: although social media may provide a glimpse on electoral outcomes current research does not provide strong evidence to support it can replace traditional polls. Finally, future lines of research along with a set of requirements they must fulfill are provided.Comment: 19 pages, 3 table

Using Multiple Signatures to Improve Accuracy of Substorm Identification

We have developed a new procedure for combining lists of substorm onset times from multiple sources. We apply this procedure to observational data and to magnetohydrodynamic (MHD) model output from 1–31 January 2005. We show that this procedure is capable of rejecting false positive identifications and filling data gaps that appear in individual lists. The resulting combined onset lists produce a waiting time distribution that is comparable to previously published results, and superposed epoch analyses of the solar wind driving conditions and magnetospheric response during the resulting onset times are also comparable to previous results. Comparison of the substorm onset list from the MHD model to that obtained from observational data reveals that the MHD model reproduces many of the characteristic features of the observed substorms, in terms of solar wind driving, magnetospheric response, and waiting time distribution. Heidke skill scores show that the MHD model has statistically significant skill in predicting substorm onset times.Plain Language SummaryMagnetospheric substorms are a process of explosive energy release from the plasma environment on the nightside of the Earth. We have developed a procedure to identify substorms that uses multiple forms of observational data in combination. Our procedure produces a list of onset times for substorms, where each onset time has been independently confirmed by two or more observational data sets. We also apply our procedure to output from a physical model of the plasma environment surrounding the Earth and show that this model can predict a significant fraction of the substorm onset times.Key PointsCombining substorm onsets from multiple types of observations can produce a more accurate list of onset times than any single listThe resulting onset list exhibits expected behavior for substorms in terms of magnetospheric driving and responseSWMF has a weak but consistent and statistically significant skill in predicting substormsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154913/1/jgra55605_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154913/2/jgra55605-sup-0002-2019JA027559-Text_SI-S01.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154913/3/jgra55605.pd

Twitter-based analysis of the dynamics of collective attention to political parties

Large-scale data from social media have a significant potential to describe complex phenomena in real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the elections outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media.Comment: 16 pages, 7 figures, 3 tables. Published in PLoS ON

Spin measurements for 147Sm+n resonances: Further evidence for non-statistical effects

We have determined the spins J of resonances in the 147Sm(n,gamma) reaction by measuring multiplicities of gamma-ray cascades following neutron capture. Using this technique, we were able to determine J values for all but 14 of the 140 known resonances below En = 1 keV, including 41 firm J assignments for resonances whose spins previously were either unknown or tentative. These new spin assignments, together with previously determined resonance parameters, allowed us to extract separate level spacings and neutron strength functions for J = 3 and 4 resonances. Furthermore, several statistical test of the data indicate that very few resonances of either spin have been missed below En = 700eV. Because a non-statistical effect recently was reported near En = 350 eV from an analysis of 147Sm(n,alpha) data, we divided the data into two regions; 0 < En < 350 eV and 350 < En < 700 eV. Using neutron widths from a previous measurement and published techniques for correcting for missed resonances and for testing whether data are consistent with a Porter-Thomas distribution, we found that the reduced-neutron-width distribution for resonances below 350 eV is consistent with the expected Porter-Thomas distribution. On the other hand, we found that reduced-neutron-width data in the 350 < En < 700 eV region are inconsistent with a Porter-Thomas distribution, but in good agreement with a chi-squared distribution having two or more degrees of freedom. We discuss possible explanations for these observed non-statistical effects and their possible relation to similar effects previously observed in other nuclides.Comment: 40 pages, 13 figures, accepted by Phys. Rev.

Structural and Electronic Properties of Small Neutral (MgO)n Clusters

Ab initio Perturbed Ion (PI) calculations are reported for neutral stoichiometric (MgO)n clusters (n<14). An extensive number of isomer structures was identified and studied. For the isomers of (MgO)n (n<8) clusters, a full geometrical relaxation was considered. Correlation corrections were included for all cluster sizes using the Coulomb-Hartree-Fock (CHF) model proposed by Clementi. The results obtained compare favorably to the experimental data and other previous theoretical studies. Inclusion of correlaiotn is crucial in order to achieve a good description of these systems. We find an important number of new isomers which allows us to interpret the experimental magic numbers without the assumption of structures based on (MgO)3 subunits. Finally, as an electronic property, the variations in the cluster ionization potential with the cluster size were studied and related to the structural isomer properties.Comment: 24 pages, LaTeX, 7 figures in GIF format. Accepted for publication in Phys. Rev.