Analyzing Disproportionate Reaction via Comparative Multilingual Targeted Sentiment in Twitter

Global events such as terrorist attacks are commented upon in social media, such as Twitter, in different languages and from different parts of the world. Most prior studies have focused on monolingual sentiment analysis, and therefore excluded an extensive proportion of the Twitter userbase. In this paper, we perform a multilingual comparative sentiment analysis study on the terrorist attack in Paris, during November 2015. In particular, we look at targeted sentiment, investigating opinions on specific entities, not simply the general sentiment of each tweet. Given the potentially inflammatory and polarizing effect that these types of tweets may have on attitudes, we examine the sentiments expressed about different targets and explore whether disproportionate reaction was expressed about such targets across different languages. Specifically, we assess whether the sentiment for French speaking Twitter users during the Paris attack differs from English-speaking ones. We identify disproportionately negative attitudes in the English dataset over the French one towards some entities and, via a crowdsourcing experiment, illustrate that this also extends to forming an annotator bias

Djelovanje kisikove plazme na polietersulfon

Polyether sulphone was found to be a useful material for production of high reliability humidity sensors. In order to obtain best properties of the sensors, the polymer surface should be activated before a thin layer of metal is deposited. A way to do it is exposition of the polymer to oxygen plasma. In order to prevent destructive action of the plasma, a very mild inductively coupled RF oxygen plasma was created in a vacuum system. The plasma density was of the order of 1016 m-3, and the electron temperature 4 eV. Active particles produced in plasma interact with the polymer causing oxidation of the surface layer and a continuous thinning of the polymer foil. In our case the rate of thinning was 25 mm per hour.Polietersulfon je pogodan materijal za osjetljive slojeve vrlo pouzdanih proba za vlažnost. Radi postizanja dobrih svojstava, površina polimera mora se aktivirati prije naparavanja metalne elektrode. Jedna od metoda je izlaganje polimera blagoj, induktivno proizvedenoj kisikovoj plazmi. Aktivirane čestice u plazmi uzrokuju oksidaciju površine polimera i njegovo stanjivanje. Primjenjivali smo stanjivanje brzinom 25 µm na sat

Djelovanje kisikove plazme na polietersulfon

Polyether sulphone was found to be a useful material for production of high reliability humidity sensors. In order to obtain best properties of the sensors, the polymer surface should be activated before a thin layer of metal is deposited. A way to do it is exposition of the polymer to oxygen plasma. In order to prevent destructive action of the plasma, a very mild inductively coupled RF oxygen plasma was created in a vacuum system. The plasma density was of the order of 1016 m-3, and the electron temperature 4 eV. Active particles produced in plasma interact with the polymer causing oxidation of the surface layer and a continuous thinning of the polymer foil. In our case the rate of thinning was 25 mm per hour.Polietersulfon je pogodan materijal za osjetljive slojeve vrlo pouzdanih proba za vlažnost. Radi postizanja dobrih svojstava, površina polimera mora se aktivirati prije naparavanja metalne elektrode. Jedna od metoda je izlaganje polimera blagoj, induktivno proizvedenoj kisikovoj plazmi. Aktivirane čestice u plazmi uzrokuju oksidaciju površine polimera i njegovo stanjivanje. Primjenjivali smo stanjivanje brzinom 25 µm na sat

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

Plasma-wall interaction studies within the EUROfusion consortium: Progress on plasma-facing components development and qualification

This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014-2018 under grant agreement No 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission.The provision of a particle and power exhaust solution which is compatible with first-wall components and edge-plasma conditions is a key area of present-day fusion research and mandatory for a successful operation of ITER and DEMO. The work package plasma-facing components (WP PFC) within the European fusion programme complements with laboratory experiments, i.e. in linear plasma devices, electron and ion beam loading facilities, the studies performed in toroidally confined magnetic devices, such as JET, ASDEX Upgrade, WEST etc. The connection of both groups is done via common physics and engineering studies, including the qualification and specification of plasma-facing components, and by modelling codes that simulate edge-plasma conditions and the plasma-material interaction as well as the study of fundamental processes. WP PFC addresses these critical points in order to ensure reliable and efficient use of conventional, solid PFCs in ITER (Be and W) and DEMO (W and steel) with respect to heat-load capabilities (transient and steady-state heat and particle loads), lifetime estimates (erosion, material mixing and surface morphology), and safety aspects (fuel retention, fuel removal, material migration and dust formation) particularly for quasi-steady-state conditions. Alternative scenarios and concepts (liquid Sn or Li as PFCs) for DEMO are developed and tested in the event that the conventional solution turns out to not be functional. Here, we present an overview of the activities with an emphasis on a few key results: (i) the observed synergistic effects in particle and heat loading of ITER-grade W with the available set of exposition devices on material properties such as roughness, ductility and microstructure; (ii) the progress in understanding of fuel retention, diffusion and outgassing in different W-based materials, including the impact of damage and impurities like N; and (iii), the preferential sputtering of Fe in EUROFER steel providing an in situ W surface and a potential first-wall solution for DEMO.European Commission; Consortium for Ocean Leadership 633053; Institute of Solid State Physics, University of Latvia as the Center of Excellence has received funding from the European Union’s Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART

Debunking in a world of tribes

Social media aggregate people around common interests eliciting collective framing of narratives and worldviews. However, in such a disintermediated environment misinformation is pervasive and attempts to debunk are often undertaken to contrast this trend. In this work, we examine the effectiveness of debunking on Facebook through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users usually consuming proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook US interact with specific debunking posts. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. However, both groups interact similarly with the information within their echo chamber. Then, we measure how users from both echo chambers interacted with 50,220 debunking posts accounting for both users consumption patterns and the sentiment expressed in their comments. Sentiment analysis reveals a dominant negativity in the comments to debunking posts. Furthermore, such posts remain mainly confined to the scientific echo chamber. Only few conspiracy users engage with corrections and their liking and commenting rates on conspiracy posts increases after the interaction

Comparison of AES and EXAFS analysis of a thin CuxAly\mathrm{Cu_x Al_y} layer on Al substrate

A layer of copper with the thickness of 0.5 μm was sputter deposited on a commercially available aluminum foil with the thickness of 30 μm. The composition of elements within the coating was determined with Auger Electron Spectroscopy (AES) depth profiling, while the structure was determined with Extended X-Ray Absorption Fine Structure (EXAFS) and X-Ray Diffraction (XRD). Samples were exposed to a low pressure weakly ionized hydrogen plasma with a high H density. Due to extensive recombination of hydrogen atoms on copper surface the samples were heated to 300°C so that diffusion of elements within the coating took place. After the plasma treatment, the samples were analyzed with AES and EXAFS again. The AES depth profiles showed that a rather uniform coating consisting of 66 at.% of Al and 33 at% of Cu was formed. Comparison of the shape of the main Auger LMM peak of pure Cu and that of Cux Aly coating showed a substantial difference. The XRD analysis showed the presence of crystalline CuAl2 phase. However, the EXAFS analysis showed that the coating was not a stoichiometric CuAl2, but rather the Cu\Al solid solution rich in CuAl2

How to evaluate sentiment classifiers for Twitter time-ordered data?

Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so. Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data. The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters. We collected a large set of 1.5 million tweets in 13 European languages. We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding 138 in-sample datasets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set). We find no significant difference between the best cross-validation and sequential validation. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios