Electron-electron interaction effects on optical excitations in semiconducting single-walled carbon nanotubes

We report correlated-electron calculations of optically excited states in ten semiconducting single-walled carbon nanotubes with a wide range of diameters. Optical excitation occurs to excitons whose binding energies decrease with the increasing nanotube diameter, and are smaller than the binding energy of an isolated strand of poly-(paraphenylene vinylene). The ratio of the energy of the second optical exciton polarized along the nanotube axis to that of the lowest exciton is smaller than the value predicted within single-particle theory. The experimentally observed weak photoluminescence is an intrinsic feature of semiconducting nanotubes, and is consequence of dipole-forbidden excitons occurring below the optical exciton.Comment: 5 pages, 3 figures, To appear in PR

Evidence for Excimer Photoexcitations in an Ordered {\pi}-Conjugated Polymer Film

We report pressure-dependent transient picosecond and continuous-wave photomodulation studies of disordered and ordered films of 2-methoxy-5-(2-ethylhexyloxy) poly(para-phenylenevinylene). Photoinduced absorption (PA) bands in the disordered film exhibit very weak pressure dependence and are assigned to intrachain excitons and polarons. In contrast, the ordered film exhibits two additional transient PA bands in the midinfrared that blueshift dramatically with pressure. Based on high-order configuration interaction calculations we ascribe the PA bands in the ordered film to excimers. Our work brings insight to the exciton binding energy in ordered films versus disordered films and solutions. The reduced exciton binding energy in ordered films is due to new energy states appearing below the continuum band threshold of the single strand.Comment: 5.5 pages, 5 figure

Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201

The Politics of Social Filtering

Social filtering – the selective engagement with people, communication and other information as a result of the recommendations of others – has always taken place. However, the possibilities of the Internet combined with the growth of online social networking activities have enabled this process to become rapidly more extensive, easier and potentially problematic. This paper focuses on the analysis of the politics of social filtering through social network sites. It argues that what is needed is both a closer examination and evaluation of these processes and also the development of a framework through which to begin such an evaluation. There is also a second intent: to (re)assert the argument that any analysis necessarily needs to take into account and critique the development, implementation and use of technologies (this includes the software, algorithms and code)themselves as well as the people that build and use them

Providing awareness, explanation and control of personalized filtering in a social networking site

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user’s usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the “filter bubble” problem and increase the users’ trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users’ trust in the system

Reducing Controversy by Connecting Opposing Views

Peer reviewe

Interaction energy functional for lattice density functional theory: Applications to one-, two- and three-dimensional Hubbard models

The Hubbard model is investigated in the framework of lattice density functional theory (LDFT). The single-particle density matrix $\gamma_{ij}$ with respect the lattice sites is considered as the basic variable of the many-body problem. A new approximation to the interaction-energy functional $W[\gamma]$ is proposed which is based on its scaling properties and which recovers exactly the limit of strong electron correlations at half-band filling. In this way, a more accurate description of $W$ is obtained throughout the domain of representability of $\gamma_{ij}$, including the crossover from weak to strong correlations. As examples of applications results are given for the ground-state energy, charge-excitation gap, and charge susceptibility of the Hubbard model in one-, two-, and three-dimensional lattices. The performance of the method is demonstrated by comparison with available exact solutions, with numerical calculations, and with LDFT using a simpler dimer ansatz for $W$. Goals and limitations of the different approximations are discussed.Comment: 25 pages and 8 figures, submitted to Phys. Rev.

Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections

"This is an Accepted Manuscript of an article published by Taylor & Francis in The Communication Review on 2019, available online: https://www.tandfonline.com/doi/full/10.1080/10714421.2019.1599642"[EN] During election campaigns, candidates, parties, and media share their relevance on Twitter with a group of especially active users, aligned with a particular party. This paper introduces the profile of ¿party evangelists,¿ and explores the activity and effects these users had on the general political conversation during the 2015 Spanish general election. On that occasion, the electoral expectations were uncertain for the two major parties (PP and PSOE) because of the rise of two emerging parties that were disrupting the political status quo (Podemos and Ciudadanos). This was an ideal situation to assess the differences between the evangelists of established and emerging parties. The paper evaluates two aspects of the political conversation based on a corpus of 8.9 million tweets: the retweet- ing effectiveness, and the sentiment analysis of the overall conver- sation. We found that one of the emerging party¿s evangelists dominated message dissemination to a much greater extent.The present research was supported by the Ministerio de Economia y Competitividad [CSO2013-43960-R] [CSO2016-77331-C2-1-R]. The present research was supported by the Ministerio de Economia y Competitividad, Spain, under Grants CSO2013-43960-R ("2015-2016 Spanish political parties' online campaign strategies") and CSO2016-77331-C2-1-R ("Strategies, agendas and discourse in electoral cybercampaigns: media and citizens"). This work was possible thanks to help received from Emilio Giner in his task of extracting the corpus of tweets and from assistance provided by Mike Thelwall and David Vilares in the use of the SentiStrength application. We have benefited from valuable comments on drafts of this article from professors Joaquín Aldás, Amparo Baviera-Puig, Guillermo López-García, and especially Lidia Valera-Ordaz.Baviera, T.; Sampietro, A.; García-Ull, FJ. (2019). Political conversations on Twitter in a disruptive scenario: The role of "party evangelists" during the 2015 Spanish general elections. The Communication Review. 22(2):117-138. https://doi.org/10.1080/10714421.2019.1599642S117138222Alvarez, R., Garcia, D., Moreno, Y., & Schweitzer, F. (2015). Sentiment cascades in the 15M movement. EPJ Data Science, 4(1). doi:10.1140/epjds/s13688-015-0042-4Anduiza, E., Cristancho, C., & Sabucedo, J. M. (2013). Mobilization through online social networks: the political protest of theindignadosin Spain. Information, Communication & Society, 17(6), 750-764. doi:10.1080/1369118x.2013.808360Anstead, N., & O’Loughlin, B. (2011). The Emerging Viewertariat and BBC Question Time. The International Journal of Press/Politics, 16(4), 440-462. doi:10.1177/1940161211415519Barabási, A.-L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509-512. doi:10.1126/science.286.5439.509Barberá, P. (2015). Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data. Political Analysis, 23(1), 76-91. doi:10.1093/pan/mpu011Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting From Left to Right. Psychological Science, 26(10), 1531-1542. doi:10.1177/0956797615594620Barberá, P., & Rivero, G. (2014). Understanding the Political Representativeness of Twitter Users. Social Science Computer Review, 33(6), 712-729. doi:10.1177/0894439314558836Berger, J., & Milkman, K. L. (2012). What Makes Online Content Viral? Journal of Marketing Research, 49(2), 192-205. doi:10.1509/jmr.10.0353Bigonha, C., Cardoso, T. N. C., Moro, M. M., Gonçalves, M. A., & Almeida, V. A. F. (2011). Sentiment-based influence detection on Twitter. Journal of the Brazilian Computer Society, 18(3), 169-183. doi:10.1007/s13173-011-0051-5Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008Bravo-Marquez, F., Mendoza, M., & Poblete, B. (2014). Meta-level sentiment models for big social data analysis. Knowledge-Based Systems, 69, 86-99. doi:10.1016/j.knosys.2014.05.016Casero-Ripollés, A., Feenstra, R. A., & Tormey, S. (2016). Old and New Media Logics in an Electoral Campaign. The International Journal of Press/Politics, 21(3), 378-397. doi:10.1177/1940161216645340Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2013). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340-358. doi:10.1177/1461444813480466Meeyoung Cha, Benevenuto, F., Haddadi, H., & Gummadi, K. (2012). The World of Connections and Information Flow in Twitter. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 42(4), 991-998. doi:10.1109/tsmca.2012.2183359Chadwick, A. (2013). The Hybrid Media System. doi:10.1093/acprof:oso/9780199759477.001.0001Cogburn, D. L., & Espinoza-Vasquez, F. K. (2011). From Networked Nominee to Networked Nation: Examining the Impact of Web 2.0 and Social Media on Political Participation and Civic Engagement in the 2008 Obama Campaign. Journal of Political Marketing, 10(1-2), 189-213. doi:10.1080/15377857.2011.540224(2014). Journal of Communication, 64(2). doi:10.1111/jcom.2014.64.issue-2Conover, M. D., Gonçalves, B., Flammini, A., & Menczer, F. (2012). Partisan asymmetries in online political activity. EPJ Data Science, 1(1). doi:10.1140/epjds6Coviello, L., Sohn, Y., Kramer, A. D. I., Marlow, C., Franceschetti, M., Christakis, N. A., & Fowler, J. H. (2014). Detecting Emotional Contagion in Massive Social Networks. PLoS ONE, 9(3), e90315. doi:10.1371/journal.pone.0090315D’heer, E., & Verdegem, P. (2014). Conversations about the elections on Twitter: Towards a structural understanding of Twitter’s relation with the political and the media field. European Journal of Communication, 29(6), 720-734. doi:10.1177/0267323114544866Dang-Xuan, L., Stieglitz, S., Wladarsch, J., & Neuberger, C. (2013). AN INVESTIGATION OF INFLUENTIALS AND THE ROLE OF SENTIMENT IN POLITICAL COMMUNICATION ON TWITTER DURING ELECTION PERIODS. Information, Communication & Society, 16(5), 795-825. doi:10.1080/1369118x.2013.783608Díaz-Parra, I., & Jover-Báez, J. (2016). Social movements in crisis? From the 15-M movement to the electoral shift in Spain. International Journal of Sociology and Social Policy, 36(9/10), 680-694. doi:10.1108/ijssp-09-2015-0101Dubois, E., & Gaffney, D. (2014). The Multiple Facets of Influence. American Behavioral Scientist, 58(10), 1260-1277. doi:10.1177/0002764214527088Enli, G. (2017). Twitter as arena for the authentic outsider: exploring the social media campaigns of Trump and Clinton in the 2016 US presidential election. European Journal of Communication, 32(1), 50-61. doi:10.1177/0267323116682802Felt, M. (2016). Social media and the social sciences: How researchers employ Big Data analytics. Big Data & Society, 3(1), 205395171664582. doi:10.1177/2053951716645828Ferrara, E., & Yang, Z. (2015). Measuring Emotional Contagion in Social Media. PLOS ONE, 10(11), e0142390. doi:10.1371/journal.pone.0142390(2015). Journal of Communication, 65(5). doi:10.1111/jcom.2015.65.issue-5Guerrero-Solé, F. (2018). Interactive Behavior in Political Discussions on Twitter: Politicians, Media, and Citizens’ Patterns of Interaction in the 2015 and 2016 Electoral Campaigns in Spain. Social Media + Society, 4(4), 205630511880877. doi:10.1177/2056305118808776Guo, L., & Vargo, C. (2015). The Power of Message Networks: A Big-Data Analysis of the Network Agenda Setting Model and Issue Ownership. Mass Communication and Society, 18(5), 557-576. doi:10.1080/15205436.2015.1045300Himelboim, I., McCreery, S., & Smith, M. (2013). Birds of a Feather Tweet Together: Integrating Network and Content Analyses to Examine Cross-Ideology Exposure on Twitter. Journal of Computer-Mediated Communication, 18(2), 40-60. doi:10.1111/jcc4.12001Huckfeldt, R., Johnson, P. E., & Sprague, J. (2004). Political Disagreement. doi:10.1017/cbo9780511617102Brundidge, J. (2010). Encountering «Difference» in the Contemporary Public Sphere: The Contribution of the Internet to the Heterogeneity of Political Discussion Networks. Journal of Communication, 60(4), 680-700. doi:10.1111/j.1460-2466.2010.01509.xJungherr, A. (2015). Analyzing Political Communication with Digital Trace Data. Contributions to Political Science. doi:10.1007/978-3-319-20319-5Jungherr, A., Jürgens, P., & Schoen, H. (2011). Why the Pirate Party Won the German Election of 2009 or The Trouble With Predictions: A Response to Tumasjan, A., Sprenger, T. O., Sander, P. G., & Welpe, I. M. «Predicting Elections With Twitter: What 140 Characters Reveal About Political Sentiment». Social Science Computer Review, 30(2), 229-234. doi:10.1177/0894439311404119Kaiser, H. F. (1960). The Application of Electronic Computers to Factor Analysis. Educational and Psychological Measurement, 20(1), 141-151. doi:10.1177/001316446002000116Klinger, U., & Svensson, J. (2014). The emergence of network media logic in political communication: A theoretical approach. New Media & Society, 17(8), 1241-1257. doi:10.1177/1461444814522952Lavezzolo, S., & Ramiro, L. (2017). Stealth democracy and the support for new and challenger parties. European Political Science Review, 10(2), 267-289. doi:10.1017/s1755773917000108McGregor, S. C., Mourão, R. R., & Molyneux, L. (2017). Twitter as a tool for and object of political and electoral activity: Considering electoral context and variance among actors. Journal of Information Technology & Politics, 14(2), 154-167. doi:10.1080/19331681.2017.1308289McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27(1), 415-444. doi:10.1146/annurev.soc.27.1.415Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113. doi:10.1016/j.asej.2014.04.011Min, Y. (2004). News Coverage of Negative Political Campaigns. Harvard International Journal of Press/Politics, 9(4), 95-111. doi:10.1177/1081180x04271861Newman, M. (2010). Networks. doi:10.1093/acprof:oso/9780199206650.001.0001Orriols, L., & Cordero, G. (2016). The Breakdown of the Spanish Two-Party System: The Upsurge of Podemos and Ciudadanos in the 2015 General Election. South European Society and Politics, 21(4), 469-492. doi:10.1080/13608746.2016.1198454Park, C. S. (2013). Does Twitter motivate involvement in politics? Tweeting, opinion leadership, and political engagement. Computers in Human Behavior, 29(4), 1641-1648. doi:10.1016/j.chb.2013.01.044Riquelme, F., & González-Cantergiani, P. (2016). Measuring user influence on Twitter: A survey. Information Processing & Management, 52(5), 949-975. doi:10.1016/j.ipm.2016.04.003Robinson, J. P. (1976). Interpersonal Influence in Election Campaigns: Two Step-Flow Hypotheses. Public Opinion Quarterly, 40(3), 304. doi:10.1086/268307Robles, J. M., Díez, R., R. Castromil, A., Rodríguez, A., & Cruz, M. (2015). El movimiento 15-M en los medios y en las redes. Un análisis de sus estrategias comunicativas. Empiria. Revista de metodología de ciencias sociales, 0(32), 37. doi:10.5944/empiria.32.2015.15308Recerca. Revista de pensament i anàlisi. (s. f.). doi:10.6035/recercaSunstein, C. R. (2017). #Republic. doi:10.1515/9781400884711Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558. doi:10.1002/asi.21416Vaccari, C., Chadwick, A., & O’Loughlin, B. (2015). Dual Screening the Political: Media Events, Social Media, and Citizen Engagement. Journal of Communication, 65(6), 1041-1061. doi:10.1111/jcom.12187Vergeer, M., & Hermans, L. (2013). Campaigning on Twitter: Microblogging and Online Social Networking as Campaign Tools in the 2010 General Elections in the Netherlands. Journal of Computer-Mediated Communication, 18(4), 399-419. doi:10.1111/jcc4.12023Vilares, D., Thelwall, M., & Alonso, M. A. (2015). The megaphone of the people? Spanish SentiStrength for real-time analysis of political tweets. Journal of Information Science, 41(6), 799-813. doi:10.1177/0165551515598926Weimann, G. (1991). The Influentials: Back to the Concept of Opinion Leaders? Public Opinion Quarterly, 55(2), 267. doi:10.1086/269257Wu, S., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Who says what to whom on twitter. Proceedings of the 20th international conference on World wide web - WWW ’11. doi:10.1145/1963405.1963504Xu, W. W., Sang, Y., Blasiola, S., & Park, H. W. (2014). Predicting Opinion Leaders in Twitter Activism Networks. American Behavioral Scientist, 58(10), 1278-1293. doi:10.1177/0002764214527091Zollo, F., Novak, P. K., Del Vicario, M., Bessi, A., Mozetič, I., Scala, A., … Quattrociocchi, W. (2015). Emotional Dynamics in the Age of Misinformation. PLOS ONE, 10(9), e0138740. doi:10.1371/journal.pone.013874

Network segregation in a model of misinformation and fact checking

Misinformation under the form of rumor, hoaxes, and conspiracy theories spreads on social media at alarming rates. One hypothesis is that, since social media are shaped by homophily, belief in misinformation may be more likely to thrive on those social circles that are segregated from the rest of the network. One possible antidote is fact checking which, in some cases, is known to stop rumors from spreading further. However, fact checking may also backfire and reinforce the belief in a hoax. Here we take into account the combination of network segregation, finite memory and attention, and fact-checking efforts. We consider a compartmental model of two interacting epidemic processes over a network that is segregated between gullible and skeptic users. Extensive simulation and mean-field analysis show that a more segregated network facilitates the spread of a hoax only at low forgetting rates, but has no effect when agents forget at faster rates. This finding may inform the development of mitigation techniques and overall inform on the risks of uncontrolled misinformation online

The radical cation of bacteriochlorophyll b. A liquid-phase endor and triple resonance study

The previous termradical cationnext term of bacterioehlorophyll b (BChl b) is investigated by ENDOR and TRIPLE resonance in liquid solution. The experimental hyperfine coupling constants, ten proton and three nitrogen couplings, are compared with the predictions from advanced molecular-orbital calculations (RHF INDO/SP). The detailed picture obtained of the spin density distribution is a prerequisite for the investigation of the primary electron donor previous termradical cationnext term in BChl b containing photosynthetic bacteria