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Social network services are used, among other things, to express political opinion. The present study is an effort to analyze the timing of media postings related to candidates Clinton and Trump on the platform Instagram before and after the 2016 US presidential election. A selected set of hashtags is used to determine whether a posting was intended to support or oppose either candidate. We thus obtain four time series of hourly readings of the number of Instagram postings: Clinton vs. Trump, supporters vs. opponents. We use cross-wavelet analysis to reveal the periodic structure of these series. It turns out that all four time series have significant 12- and 24-hourperiods. Among our findings is that, at the 12-hour period, the time series of Trump supporters was leading Trump opponents as well as Clinton supporters the days before the election, while the series of Clinton opponents was often leading Clinton supporters: Trump supporters and Clinton opponents were eager to post media, while Trump opponents and Clinton supporters were sluggish. Considering the forecasts for this election,these results come as a surprise.
Resumo O objetivo do artigo é analisar as estratégias de uso dos stories do Instagram postados pelos candidatos à Presidência da República do Brasil em 2018. As nossas hipóteses de pesquisa são que os candidatos usaram os stories para enaltecer sua imagem pessoal e anunciar agenda de campanha (H1), e houve alto uso dos stories para divulgar bastidores e baixo uso para propostas de campanha (H2). Os stories foram classificados em seis variáveis elaboradas: tema geral, tema específico, tema secundário, tipo de imagem, menção a partido e humor. A coleta (N=2032) foi realizada entre 05 de setembro e 07 de outubro de 2018, compreendendo o primeiro turno das eleições. Procedemos a uma análise de conteúdo do tipo quantitativa e qualitativa por meio de um livro de códigos. Os resultados sugerem que os candidatos usaram a plataforma para gerenciar imagem pública e anúncio de agenda. Palavras-chave: Instagram. Stories. Presidente.
Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. However, manual image annotation, even for creating training sets for machine learning algorithms, requires hard effort and contains human judgment errors and subjectivity. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are pursued. In this work, we investigate whether tags accompanying photos in the Instagram can be considered as image annotation metadata. If such a claim is proved then Instagram could be used as a very rich, easy to collect automatically, source of training data for the development of AIA techniques. Our hypothesis is that Instagram hashtags, and especially those provided by the photo owner / creator, express more accurately the content of a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose 1000 Instagram images were collected and one to four hashtags, considered as the most descriptive ones for the image in question, were chosen among the hashtags used by the photo owner. An online database was constructed to generate online questionnaires containing 20 images each, which were distributed to experiment participants so they can choose the best suitable hashtag for every image according to their interpretation. Results show that an average of 66% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed.
The automated parsing of 130,213 news articles about the 2012 US presidential elections produces a network formed by the key political actors and issues, which were linked by relations of support and opposition. The nodes are formed by noun phrases and links by verbs, directly expressing the action of one node upon the other. This network is studied by applying insights from several theories and techniques, and by combining existing tools in an innovative way, including:graph partitioning, centrality, assortativity, hierarchy and structural balance. The analysis yields various patterns. First, weobserve that the fundamental split between the Republican and Democrat camps can be easily detected by network partitioning, which provides a strong validation check of the approach adopted, as well as a sound way to assign actors and topics to one of the two camps. Second, we identify the most central nodes of the political camps. We also learnt that Clinton played a more central role than Biden in the Democrat camp; the overall campaign was much focused on economy and rights; the Republican Party (Grand Old Party or GOP) is the most divisive subject in the campaign, and is portrayed more negatively than the Democrats; and, overall, the media reported positive statements more frequently forthe Democrats than the Republicans. This is the first study in which political positions are automatically extracted and derived froma very large corpus of online news, generating a network that goes well beyond traditional word-association networks by means of richer linguistic analysis of texts.
The third-person perception is the tendency for people to believe others are more affected by media messages than they are themselves. Analysis of a probability sample telephone survey revealed a significant relationship between having voted in the 2004 presidential election and third-person perception level. This empirical evidence supports a link between past voting behavior and the third-person perception, which expands third-person perception and behavioral domains beyond censorship issues. Republicans were also found to have a significantly greater third-person perception level than Democrats.
This study looks at student Facebook groups supporting the 2008 presidential candidates, John McCain and Barack Obama, from largest land-grant universities in seven battleground states. The findings of a content analysis of wall posts show that students are using Facebook to facilitate dialog and civic political involvement. In opposition to pro-McCain groups, pro-Obama groups have wider time frame coverage and demonstrate substantively higher site activity. Political discussions related to the political civic process, policy issues, campaign information, candidate issues, and acquisition of campaign products dominate across groups and election seasons. An examination of the content of wall posts based on the four categories of the Michigan Model of voting behavior (partisanship, group affiliation, candidate image, and political/campaign issues) reveals that in the primary season, pro-Obama groups focus mostly on short-term topics (candidate image and campaign issues), whereas pro-McCain groups focus mostly on long-term topics (partisanship and group affiliation). The overall findings of this study suggest that youth online communities actively follow campaigns and post comments that foster the political dialog and civic engagement.
The cross wavelet transform (XWT) is a powerful tool for testing the proposed connections between two time series. Because of XWT’s skeletal structure, which is based on the wavelet transform, it is suitable for the analysis of non-stationary periodic signals. Recent work has shown that the power spectrum based on the wavelet transform can produce a deviation, which can be corrected by choosing a proper rectification scale. In this study, we show that the standard application of the XWT can also lead to a biased result. A corrected version of the standard XWT was constructed using the scale of each series as normalizing factors. This correction was first tested in an artificial example involving two series build from combinations of two harmonic series of different amplitudes and frequencies. The standard XWT applied to this example produce a biased result, whereas the correct result is obtained with used of the proposed normalization. This analysis was then applied to a real geophysical situation with important implications to climate modulation on the northwestern Brazilian coast. The linkage between the relative humidity and the shortwave radiation measurements, obtained from the 8°S 30°W ATLAS buoy of the Southwestern Extension of the Prediction and Research moored Array in the Tropical Atlantic Project (PIRATA-SWE), was explored. The analysis revealed the importance of including the correction in order to not overlook any possible connections. The requirements of incorporating this correction in the XWT calculations are emphasized.
Using social media for political discourse is increasingly becoming common practice, especially around election time. Arguably, one of the most interesting aspects of this trend is the possibility of "pulsing" the public's opinion in near real-time and, thus, it has attracted the interest of many researchers as well as news organizations. Recently, it has been reported that predicting electoral outcomes from social media data is feasible, in fact it is quite simple to compute. Positive results have been reported in a few occasions, but without an analysis on what principle enables them. This, however, should be surprising given the significant differences in the demographics between likely voters and users of online social networks. This work aims to test the predictive power of social media metrics against several Senate races of the two recent US Congressional elections. We review the findings of other researchers and we try to duplicate their findings both in terms of data volume and sentiment analysis. Our research aim is to shed light on why predictions of electoral (or other social events) using social media might or might not be feasible. In this paper, we offer two conclusions and a proposal: First, we find that electoral predictions using the published research methods on Twitter data are not better than chance. Second, we reveal some major challenges that limit the predictability of election results through data from social media. We propose a set of standards that any theory aiming to predict elections (or other social events) using social media should follow.
Morlet et al (1982, this issue) showed the advantages of using complex values for both waves and characteristics of the media. We simulated the theoretical tools we present here, using the Goupillaud-Kunetz algorithm. Now we present sampling methods for complex signals or traces corresponding to received waves, and sampling methods for complex characterization of multilayered or heterogeneous media. Regarding the complex signals, we present a two-dimensional (2-D) method of sampling in the time-frequency domain using a special or "extended" Gabor expansion on a set of basic wavelets adapted to phase preservation. Such a 2-D expansion permits us to handle in a proper manner instantaneous frequency spectra. We show the differences between "wavelet resolution" and "sampling grid resolution." We also show the importance of phase preservation in high-resolution seismic. Regarding the media, we show how analytical studies of wave propagation in periodic structured layers could help when trying to characterize the physical properties of the layers and their large scale granularity as a result of complex deconvolution. Analytical studies of wave propagation in periodic structures are well known in solid state physics, and lead to the so-called "Bloch waves." The introduction of complex waves leads to replacing the classical wave equation by a Schrödinger equation. Finally, we show that complex wave equations, Gabor expansion, and Bloch waves are three different ways of introducing the tools of quantum mechanics in high-resolution seismic (Gabor, 1946; Kittel, 1976; Morlet, 1975). And conversely, the Goupillaud-Kunetz algorithm and an extended Gabor expansion may be of some use in solid state physics.
Wavelet analysis is becoming more popular in the Economics discipline. Until recently, most works have made use of tools associated with the Discrete Wavelet Transform. However, after 2005, there has been a growing body of work in Economics and Finance that makes use of the Continuous Wavelet Transform tools. In this article, we give a self-contained summary on the most relevant theoretical results associated with the Continuous Wavelet Transform, the Cross-Wavelet Transform, the Wavelet Coherency and the Wavelet Phase-Difference. We describe how the transforms are usually implemented in practice and provide some examples. We also introduce the Economists to a new class of analytic wavelets, the Generalized Morse Wavelets, which have some desirable properties and provide an alternative to the Morlet Wavelet. Finally, we provide a user friendly toolbox which will allow any researcher to replicate our results and to use it in his/her own research.
Instagram is the fastest growing social network site globally. This study investigates motives for its use, and its relationship to contextual age and narcissism. A survey of 239 college students revealed that the main reasons for Instagram use are "Surveillance/Knowledge about others," "Documentation," "Coolness," and "Creativity." The next significant finding was a positive relationship between those who scored high in interpersonal interaction and using Instagram for coolness, creative purposes, and surveillance. Another interesting finding shows that there is a positive relationship between high levels of social activity (traveling, going to sporting events, visiting friends, etc.) and being motivated to use Instagram as a means of documentation. In reference to narcissism, there was a positive relationship between using Instagram to be cool and for surveillance. Theoretical contributions of this study relate to our understanding of uses and gratifications theory. This study uncovers new motives for social media use not identified in previous literature.
When defined in terms of social identity and affect toward copartisans and opposing partisans, the polarization of the American electorate has dramatically increased. We document the scope and consequences of affective polarization of partisans using implicit, explicit, and behavioral indicators. Our evidence demonstrates that hostile feelings for the opposing party are ingrained or automatic in voters' minds, and that affective polarization based on party is just as strong as polarization based on race. We further show that party cues exert powerful effects on nonpolitical judgments and behaviors. Partisans discriminate against opposing partisans, doing so to a degree that exceeds discrimination based on race. We note that the willingness of partisans to display open animus for opposing partisans can be attributed to the absence of norms governing the expression of negative sentiment and that increased partisan affect provides an incentive for elites to engage in confrontation rather than cooperation.
A practical step-by-step guide to wavelet analysis is given, with examples taken from time series of the El NiñoSouthem Oscillation (ENSO). The guide includes a comparison to the windowed Fourier transform, the choice of an appropriate wavelet basis function, edge effects due to finite-length time series, and the relationship between wavelet scale and Fourier frequency. New statistical significance tests for wavelet power spectra are developed by deriving theoretical wavelet spectra for white and red noise processes and using these to establish significance levels and confidence intervals. It is shown that smoothing in time or scale can be used to increase the confidence of the wavelet spectrum. Empirical formulas are given for the effect of smoothing on significance levels and confidence intervals. Extensions to wavelet analysis such as filtering, the power Hovmöller, cross-wavelet spectra, and coherence are described. The statistical significance tests are used to give a quantitative measure of changes in ENSO variance on interdecadal timescales. Using new datasets that extend back to 1871, the Niño3 sea surface temperature and the Southern Oscillation index show significantly higher power during 1880-1920 and 1960-90, and lower power during 1920-60, as well as a possible 15-yr modulation of variance. The power Hovmöller of sea level pressure shows significant variations in 2-8-yr wavelet power in both longitude and time.
Twitter as a new form of social media potentially contains useful information
that opens new opportunities for content analysis on tweets. This paper
examines the predictive power of Twitter regarding the US presidential election
of 2012. For this study, we analyzed 32 million tweets regarding the US
presidential election by employing a combination of machine learning
techniques. We devised an advanced classifier for sentiment analysis in order
to increase the accuracy of Twitter content analysis. We carried out our
analysis by comparing Twitter results with traditional opinion polls. In
addition, we used the Latent Dirichlet Allocation model to extract the
underlying topical structure from the selected tweets. Our results show that we
can determine the popularity of candidates by running sentiment analysis. We
can also uncover candidates popularities in the US states by running the
sentiment analysis algorithm on geo-tagged tweets. To the best of our
knowledge, no previous work in the field has presented a systematic analysis of
a considerable number of tweets employing a combination of analysis techniques
by which we conducted this study. Thus, our results aptly suggest that Twitter
as a well-known social medium is a valid source in predicting future events
such as elections. This implies that understanding public opinions and trends
via social media in turn allows us to propose a cost- and time-effective way
not only for spreading and sharing information, but also for predicting future
This study examines the ways in which online media influenced political disaffection among young adults during the 2008 presidential election campaign. The effects of social media attention, online expression, and traditional Internet sources on political cynicism, apathy, and skepticism were evaluated using data from an online survey of college students. Results show that attention to social media for campaign information is positively related to cynicism and apathy and negatively related to skepticism. Online expression has a positive effect on skepticism. Implications are discussed for the role of social media in bringing a historically disengaged demographic into the political process.
Despite research to the contrary, the general public and a significant number of politicians are convinced the U.S. news media have a liberal and pro-Democratic bias. To understand why many people believe the media ha ve such biases, this study tested whether such a perception is related to an observer's own partisan and ideological positions. Findings based on two large national surveys suggest that audiences' ideologies and partisanships affect how they view the media. Strong conservatives and Republicans are more likely to distrust the news media, whereas the best predictor of a media bias perception is political cynicism.
This study examined college students' use of online media for political purposes in the 2008 election. Social media attention, online expression, and traditional Internet attention were assessed in relation to political self-efficacy and situational political involvement. Data from a Web survey of college students showed significant positive relationships between attention to traditional Internet sources and political self-efficacy and situational political involvement. Attention to social media was not significantly related to political self-efficacy or involvement. Online expression was significantly related to situational political involvement but not political self-efficacy. Implications are discussed for political use of online media for young adults.
Although Facebook is primarily known for building and maintaining relationships, the 2008 presidential election highlighted this social networking website as a viable tool for political communication. In fact, during primary season until Election Day in 2008, Facebook users created more than 1,000 Facebook group pages that focused on Barack Obama and John McCain. Using quantitative content analysis, the primary purpose of this study was to assess how both John McCain and Barack Obama were portrayed across these Facebook groups. Results indicated that group membership and activity levels were higher for Barack Obama than for John McCain. Overall, Barack Obama was portrayed more positively across Facebook groups than John McCain. In addition, profanity, racial, religious, and age-related language were also coded for and varied with regard to how each candidate was portrayed. Theoretical and practical implications are discussed.
I propose to consider the question, “Can machines think?”♣ This should begin with definitions of the meaning of the terms “machine” and “think”. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words “machine” and “think” are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, “Can machines think?” is to be sought in a statistical survey such as a Gallup poll.
Central banks have different objectives in the short and long run. Governments operate simultaneously at different timescales. Many economic processes are the result of the actions of several agents, who have different term objectives. Therefore, a macroeconomic time series is a combination of components operating on different frequencies. Several questions about economic time series are connected to the understanding of the behavior of key variables at different frequencies over time, but this type of information is difficult to uncover using pure time-domain or pure frequency-domain methods.To our knowledge, for the first time in an economic setup, we use cross-wavelet tools to show that the relation between monetary policy variables and macroeconomic variables has changed and evolved with time. These changes are not homogeneous across the different frequencies.
From experimental studies in digital processing of seismic reflection data, geophysicists know that a seismic signal does vary in amplitude, shape, frequency and phase, versus propagation time. To enhance the resolution of the seismic reflection method, we must investigate these variations in more detail. We present quantitative results of theoretical studies on propagation of plane waves for normal incidence, through perfectly elastic multilayered media. As wavelet shapes, we use zero-phase cosine wavelets modulated by a Gaussian envelope and the corresponding complex wavelets. A finite set of such wavelets, for an appropriate sampling of the frequency domain, may be taken as the basic wavelets for a Gabor expansion of any signal or trace in a two-dimensional (2-D) domain (time and frequency). We can then compute the wave propagation using complex functions and thereby obtain quantitative results including energy and phase of the propagating signals. These results appear as complex 2-D functions of time and frequency, i.e., as "instantaneous frequency spectra." Choosing a constant sampling rate on the logarithmic scale in the frequency domain leads to an appropriate sampling method for phase preservation of the complex signals or traces. For this purpose, we developed a Gabor expansion involving basic wavelets with a constant time duration/mean period ratio. For layered media, as found in sedimentary basins, we can distinguish two main types of series: (1) progressive series, and (2) cyclic or quasi-cyclic series. The second type is of high interest in hydrocarbon exploration. Progressive series do not involve noticeable distortions of the seismic signal. We studied, therefore, the wave propagation in cyclic series and, first, simple models made up of two components (binary media). Such periodic structures have a spatial period. We present synthetic traces computed in the time domain using the Goupillaud-Kunetz model of propagation for one-dimensional (1-D) synthetic seismograms. Three different cases appear for signal scattering, depending upon the value of the ratio wavelength of the signal/spatial period of the medium. (1) Large wavelengths The composite medium is fully transparent, but phase delaying. It acts like an homogeneous medium, with an "effective velocity" and an "effective impedance." (2) Short wavelengths For wavelengths close to twice the spatial period of the medium, the composite medium strongly attenuates the transmission, and superreflectivity occurs as counterpart. (3) Intermediate wavelengths For intermediate values of the frequency, velocity dispersion versus frequency appears. All these phenomena are studied in the frequency domain, by analytic formulation of the transfer functions of the composite media for transmission and reflection. Such phenomena are similar to Bloch waves in crystal lattices as studied in solid state physics, with only a difference in scale, and we checked their conformity with laboratory measurements. Such models give us an easy way to introduce the use of effective velocities and impedances which are frequency dependent, i.e., complex. They will be helpful for further developments of "complex deconvolution." The above results can be extended to quasi-cyclic media made up of a random distribution of double layers. For signal transmission, quasi-cyclic series act as a high cut filter with possible time delay, velocity dispersion, and "constant Q" type of law for attenuation. For signal reflection they act as a low cut filter, with possible superreflections. These studies could be extended to three-dimensional (3-D) binary models (grains and pores in a porous reservoir), in agreement with well-known acoustic properties of gas reservoirs (theory of bright spots). We present some applications to real well data.
This paper addresses a bias problem in the estimate of wavelet power spectra for atmospheric and oceanic datasets. For a time series comprised of sine waves with the same amplitude at different frequencies the conventionally adopted wavelet method does not produce a spectrum with identical peaks, in contrast to a Fourier analysis. The wavelet power spectrum in this definition, that is, the transform coefficient squared (to within a constant factor), is equivalent to the integration of energy (in physical space) over the influence period (time scale) the series spans. Thus, a physically consistent definition of energy for the wavelet power spectrum should be the transform coefficient squared divided by the scale it associates. Such adjusted wavelet power spectrum results in a substantial improvement in the spectral estimate, allowing for a comparison of the spectral peaks across scales. The improvement is validated with an artificial time series and a real coastal sea level record. Also examined is the previous example of the wavelet analysis of the Niño-3 SST data.
Predicting election trends with Twitter: Hillary Clinton versus Donald Trump
H A Makse
Bovet A., Morone F., and Makse H.A., 2016. Predicting election trends with Twitter:
Hillary Clinton versus Donald Trump. Working Paper, City College of New York, New
York, USA. URL https://arxiv.org/abs/1610.01587.
Fake accounts still plague Instagram despite purge, study finds
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Not all fair probabilistic schedulers are equivalent
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