Arquivo por meses: Maio 2015

Die galicische Dialektologie: Die geografische Varietäten der galicischen Sprache

Publicado o por en Docencia,Galicisch I,Galicisch II,Galicisch III,Semestre de verán 2015

Quelle:  http://linguadenegueiros.blogspot.com.es/2015/04/materiais-de-dialectoloxia-galega.html

Prema na imaxe

 Mehr information:

César Domínguez (@cesarpabl): Was tragen die minorisierten Literaturen zur Herausbildung der Weltliteratur bei?

Publicado o por en Docencia,Proseminar: Der Jakobsweg als transkultureller Raum. Galicien am Ende des Pilgerweges,Semestre de verán 2015

Imaxe tirada de https://simplificad.wordpress.com/about/

Quelle: http://www.routledge.com/authors/i7931-cesar-dominguez

I’m associate professor of comparative literature at the University of Santiago de Compostela (USC), Spain, where I hold the Jean Monnet Chair “The Culture of European Integration”.  My four most recent books are World Literature: A Reader (Routledge, 2013), Literatura europea comparada (Arco/Libros, 2013), Contemporary Developments in Emergent Literatures and the New Europe (USC, 2014), and Introducing Comparative Literature: New Trends and Applications (Routledge, 2015). As for professional services, I‘m vice-president of the Spanish Society of General and Comparative Literature, chair of the ICLA Research Committee, secretary of the ICLA Coordinating Committee, member of the Academia Europaea, general coordinator of the European Network of Comparative Literary Studies, and Fellow of the Stockholm Collegium of World Literary History.
Education
PhD Comparative Literature, USC, 2001
MA Graduate Student in Residence, Brown U, 1994-95
BA Spanish Literature, UV, 1993
Areas of Research / Professional Expertise
My teaching and research focus upon theory of comparative literature, European literature, translation, cosmopolitanism, trans-Atlantic cultural flows (with special attention to Argentina and Galicia), and world literature.
Websites
Personal website

 

Dúas reflexións sobre as relacións entre a externalización da memoria colectiva e individual e as nosas burbullas mediáticas

Publicado o por en Docencia,Proseminar: Der Jakobsweg als transkultureller Raum. Galicien am Ende des Pilgerweges,Semestre de verán 2015

Embed from Getty Images

A raís dos problemas que se formularon en clase sobre a problemática da interacción entre a evolución dos fenómenos culturais e internet con nós mesmos, déixovos dous pequenos artigos publicados en El País (09.05.2015).

Eternidad remasterizada

Por Manuel Cruz

Lo bueno de no tener memoria es que todo viene de nuevas. Le sucedía, por poner un caso extremo, al protagonista de la películaMemento, incapaz de tener el menor recuerdo, pero en su caso ello representaba un problema, porque cuando él volvía a empezar desde cero (lo que sucedía a cada instante), el resto del mundo ya estaba de vuelta. Pero el asunto adquiere una nueva dimensión cuando los desmemoriados empiezan a ser legión, y vagan por el mundo como zombis asombrados ante todo cuanto existe, perplejos ante tanta novedad, convencidos de que fundan lo real a cada instante con su mirada, de que cada nueva cosa que descubren son ellos quienes la acaban de inventar. Ya no es necesario hoy hacer memoria —significativa expresión, ciertamente, que explicita la condición de práctica que tiene el recuerdo— porque la memoria nos viene hecha. O, mejor dicho, la totalidad de nuestros recuerdos se encuentra a nuestra disposición y podemos convocarla a voluntad. Para los jóvenes eso se ha generalizado y, en cierto sentido, pueden llevar permanentemente todo su pasado a cuestas si lo desean. Basta con un golpe de tecla, con pulsar el intro sobre la página adecuada. Eso puede tener un lado bueno, un lado malo y, finalmente, otro peor.

El bueno es que ya no cabe la coartada de la mitificación del pasado como un territorio perdido a nuestro pesar, porque ahora solo se pierde lo que uno de verdad desea abandonar, sin que mantenga su validez el viejo argumento de que era la vida o el mundo los que acaban dispersándonos a todos o haciéndonos olvidar nuestras mejores experiencias. Hoy, cualquiera puede quedar conectado con todos los viejos compañeros del instituto: basta con que lo desee. El que se desentiende de su pasado ha de argumentar, por ejemplo, que ha cambiado mucho y ya no tiene nada que ver con quien era en esa primera juventud. El lado malo es que, con esta absoluta disponibilidad —que va mucho más allá de la reproductibilidad técnica benjaminiana para ser reproductibilidad a voluntad—, el pasado pierde los pequeños restos de aura que pudieran quedarle.

Pero el lado peor de todo esto viene constituido por el enorme malentendido que dicha disponibilidad genera. Porque volver a ver aquellas imágenes del pasado no garantiza que nos vuelvan a conmover o a aterrar como lo hicieron entonces. Dichas sensaciones eran las que conservaba, como oro en paño, la memoria. Lo que hoy recuperamos merced a la tecnología es el referente material, pero no la experiencia que tuvimos porque quien se relaciona con dicho referente ya no es aquel adolescente o aquel niño, sino alguien radicalmente distinto en todos los sentidos. Si hoy las mismas imágenes nos dejan fríos o nos hacen sentir raros no es por causa de la memoria misma, sino porque en algún momento tuvimos la desafortunada idea de crecer, y nuestra mirada y nuestro corazón han cambiado. Aquellas sensaciones eran verdad. La mentira es intentar que sigan teniendo realidad tal cual en nuestro presente. Moraleja: no intente servirse de Internet para volver a vivir lo que ya vivió una vez. Acabará arruinando hasta sus propios recuerdos.

Manuel Cruz es catedrático de Filosofía en la Universidad de Barcelona.

Mercado de recuerdos

Por Alicia García Ruiz

Al principio fueron los objetos. Luego le tocó el turno a los recuerdos. El primer marxismo entendió como cosificación el proceso por el que el sistema de objetos, en lugar de acompañarnos, se presenta bajo la máscara amable de la propiedad: cercanos, míos por fin. Pero en realidad hay en ellos algo raro, la extrañeza inquietante de lo que retorna cambiado. Son objetos ajenos, disfrazados de naturalidad, pero pertenecientes a una cadena de procesos y significados mecánicos en la que acabamos como eslabones. El futuro de nuestros recuerdos parece sellado por un destino similar: colocados frente a nosotros, en dispositivos externos de memoria, nos miran desde pantallas en flujo perpetuo, haciéndonos sentir incómodos ante ellos. “¿Soy yo el de aquella fotografía?”. “¿De veras escribí aquello?”.

Es irónico que a principios del siglo pasado una de las primeras casas discográficas adoptase como logotipo la figura del “ángel registrador”, que diariamente graba en piedra las acciones, palabras y pensamientos de los hombres para ser ponderadas en el Juicio Final. El custodio de Angel Records, nombre de la discográfica, esculpía en un disco de pizarra. Las redes sociales se asemejan a una versión profana de ese mito: de la pizarra al vinilo, del vinilo al bit. Todo queda registrado irreversiblemente y a la vez todo retorna como un extraño visitante. La fantasmagoría de la mercancía ha alcanzado también a los recuerdos, a la memoria en general. No son evocaciones hechas para perdurar, sino para circular interminablemente y ser exhibidas, escudriñadas, juzgadas. Confiamos a estos dispositivos la gestión de nuestros recuerdos y nos desligamos de una capacidad activa de memoria. Como última novedad, Facebook ofrece ahora efemérides sin que el usuario las solicite (“¿Qué hacías tal día como hoy?”).

La resistencia a estos procesos no pasará por el retorno a un mundo sin flujo de imágenes y memorias, sino a una relación distinta con él. No sólo hará falta abstenerse de regalar información, sino construir algo diferente con su abundancia. Es lo que Benjamin o Kracauer intuyeron en la figura del coleccionista o del historiador como personajes que vagan espectralmente por la antesala de la historia, el presente. Entre el naufragio de objetos e imágenes, el tráfago de experiencias y recuerdos, los coleccionistas recogen pequeños restos, aquellos que nadie quiere y los refuncionalizan, rescatándolos para otro fin. Al liberarlos de su esclavitud funcional, cuenta Benjamin, son despertados para un nuevo uso, abren una nueva época. No se trata de una creación ex nihilo, sino de lo que Kracauer llamó una “utopía preservadora”, que rescata del olvido fragmentos de vida en espera de redención. ¿Qué salvar del olvido y qué olvidar? Probablemente Rilke tenía razón en que todo ángel es terrible. El registro es un severo daimon que nos fuerza a decidir incluso cuando parece que ya deciden todo por nosotros. Pero también hay heraldos portadores de utopías realizables. Ángel González no prometía amor eterno, sino sobrevivirse a sí mismo: ser yo cuando ya no sea yo.

Alicia García Ruiz es investigadora de Filosofía Contemporánea en la Universidad de Barcelona.

E a outra pregunta que xorde do titulo é: crea Facebook unha burbulla ideolóxica no noso muro por mor dos seus algoritmos para que non entre nada alleo ao noso xeito de pensar. Na revista Science (07.05.2015) apareceu este artigo. Que pensades?

POLITICAL SCIENCE

Exposure to ideologically diverse news and opinion on Facebook

  1. Eytan Bakshy1,*,†,
  2. Solomon Messing1,†,
  3. Lada Adamic1,2

+Author Affiliations


  1. 1Facebook, Menlo Park, CA 94025, USA.

  2. 2School of Information, University of Michigan, Ann Arbor, MI, USA.
  1. ↵*Corresponding author. E-mail: ebakshy@fb.com

  1. ↵† These authors contributed equally to this work.

Exposure to news, opinion and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using de-identified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks, and examine the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed, and further studied users’ choices to click through to ideologically discordant content. Compared to algorithmic ranking, individuals’ choices about what to consume had a stronger effect limiting exposure to cross-cutting content.

Exposure to news and civic information is increasingly mediated by online social networks and personalization (1). Information abundance provides individuals with an unprecedented number of options, shifting the function of curating content from newsroom editorial boards to individuals, their social networks, and manual or algorithmic information sorting (2–4). While these technologies have the potential to expose individuals to more diverse viewpoints (4, 5), they also have the potential to limit exposure to attitude-challenging information (2, 3, 6), which is associated with adopting more extreme attitudes over time (7) and misperceiving facts about current events (8). This changing environment has led to speculation around the creation of “echo chambers” (in which individuals are exposed only to information from like-minded individuals) and “filter bubbles”(in which content is selected by algorithms based on a viewer’s previous behaviors), which are devoid of attitude-challenging content (3, 9). Empirical attempts to examine these questions have been limited by difficulties in measuring news stories’ ideological leanings (10) and measuring exposure—relying on either error-laden, retrospective self-reports or behavioral data with limited generalizability—and have yielded mixed results (4, 9, 11–15).

We utilize a large, comprehensive dataset from Facebook that allows us to (i) compare the ideological diversity of the broad set of news and opinion shared on Facebook with that shared by individuals’ friend networks, (ii) compare this to the subset of stories that appear in individuals’ algorithmically-ranked News Feeds, and (iii) observe what information individuals choose to consume, given exposure on News Feed. To examine how each of the three mechanisms affect exposure (i.e., homophily, algorithmic ranking, selective avoidance of attitude-challenging items), we constructed a de-identified dataset that includes 10.1 million active U.S. users who self-report their ideological affiliation and 7 million distinct Web links (URLs) shared by U.S. users over a 6-month period between July 7, 2014 and January 7, 2015. We classified stories as either “hard” (e.g., national news, politics, world affairs), or “soft” content (e.g., sports, entertainment, travel) by training a support vector machine on unigram, bigram, and trigram text features (see Sec S1.4.1 for details). Approximately 13% of these URLs were classified as hard content. We further limit the set of hard news URLs to the 226 thousand distinct hard content URLs shared by at least 20 users who volunteer their ideological affiliation in their profile, so that we can accurately measure alignment, as detailed below. This dataset included approximately 3.8 billion unique potential exposures (i.e., cases in which an individual’s friend shared hard content, regardless of whether it appeared in her News Feed), 903 million unique exposures (i.e., cases in which a link to the content appears on screen in an individual’s News Feed), and 59 million unique clicks, among users in our study.

We then obtain a measure of content alignment (A) for each hard story by averaging the ideological affiliation of each user who shared the article. Alignment is not a measure of media slant, rather it captures differences in the kind of content shared among a set of partisans, which can include topic matter, framing, and slant. These scores, averaged over websites, capture key differences in well-known ideologically-aligned media sources: FoxNews.com is aligned with conservatives (As = +.80), while the HuffingtonPost.com is aligned with liberals (As = -0.65, for additional detail and validation, see Sec S1.4.2). We observed substantial polarization among hard content shared by users, with the most frequently shared links clearly aligned with largely liberal or conservative populations (Fig. 1).

Fig. 1
View larger version:
Fig. 1Distribution of ideological alignment of content shared on Facebook as measured by the average affiliation of sharers, weighted by the total number of shares.

Content was delineated as liberal, conservative, or neutral based on the distribution of alignment scores (see SOM for details).

The flow of information on Facebook is structured by how individuals are connected in the network. The interpersonal networks on Facebook are different from the segregated structure of political blogs (16): while there is clustering according to political affiliation on Facebook, there are also many friendships that cut across ideological affiliations. Among friendships with individuals who report their ideological affiliation in their profile, the median proportion of friendships liberals maintain with conservatives is 0.20, interquartile range (IQR) [0.09, 0.36]. Similarly, the median proportion of friendships that conservatives maintain with liberals is 0.18, IQR [0.09, 0.30] (Fig. 2).

Fig. 2

View larger version:

Fig. 2Homophily in self-reported ideological affiliation.

Proportion of links to friends of different ideological affiliations for liberal, moderate, and conservative users. Points indicate medians, thick lines indicate interquartile ranges, and thin lines represent 90% interval.

How much cross-cutting content individuals encounter depends on who their friends are and what information those friends share. If individuals acquired information from random others, approximately 45% of the hard content liberals would be exposed to would be cross cutting, compared to 40% for conservatives (Fig. 3B). Of course, individuals do not encounter information at random in offline environments (14) nor on the Internet (9). Despite the slightly higher volume of conservatively aligned articles shared (Fig. 1), liberals tend to be connected to fewer friends who share information from the other side, compared to their conservative counterparts: 24% of the hard content shared by liberals’ friends are cross-cutting, compared to 35% for conservatives (Fig. 3B).

Fig. 3

View larger version:

Fig. 3

(A) Illustration of how algorithmic ranking and individual choice affects the proportion of ideologically cross-cutting content that individuals encounter. Gray circles illustrate the content present at each stage in the media exposure process. (B) Average ideological diversity of content (i) shared by random others (random), (ii) shared by friends (potential from network), (iii) actually appeared in users’ News Feeds (exposed), (iv) users clicked on (selected).

The media that individuals consume on Facebook depends not only on what their friends share, but also on how the News Feed ranking algorithm sorts these articles, and what individuals choose to read (Fig. 3A). The order in which users see stories in the News Feed depends on many factors, including how often the viewer visits Facebook, how much they interact with certain friends, and how often users have clicked on links to certain websites in News Feed in the past. We find that after ranking, there is on average slightly less cross-cutting content: conservatives see approximately 5% less cross-cutting content compared to what friends share, while liberals see about 8% less ideologically diverse content.

Individual choice has a larger role in limiting exposure to ideologically cross cutting content: after adjusting for the effect of position (the click rate on a link is negatively correlated with its position in the News Feed; see fig. S5), we estimate the factor decrease in the likelihood that an individual clicks on a cross-cutting article relative to the proportion available in News Feed to be 17% for conservatives and 6% for liberals, a pattern consistent with prior research (4, 17). Despite these tendencies, there is substantial room for individuals to consume more media from the other side: on average, viewers clicked on 7% of hard content available in their feeds.

Our analysis has limitations. Though the vast majority of U.S. social media users are on Facebook (18), our study is limited to this social media platform. Facebook’s users tend to be younger, more educated, and female, compared to the U.S. population as a whole (18). Other forms of social media, such as blogs or Twitter, have been shown to exhibit different patterns of homophily among politically interested users, largely because ties tend to form based primarily upon common topical interests and/or specific content (16, 19), whereas Facebook ties primarily reflect many different offline social contexts: school, family, social activities, and work, which have been found to be fertile ground for fostering cross-cutting social ties (20). In addition, our distinction between exposure and consumption is imperfect: individuals may read the summaries of articles that appear in the News Feed, and therefore be exposed to some of the articles’ content without clicking through.

This work informs longstanding questions about how media exposure is shaped by our social networks. While partisans tend to maintain relationships with like-minded contacts (consistent with (21)), on average more than 20 percent of an individual’s Facebook friends who report an ideological affiliation are from the opposing party, leaving substantial room for exposure to opposing viewpoints (22, 23). Furthermore, in contrast to concerns that people might “listen and speak only to the like-minded” while online (6), we find exposure to cross-cutting content (Fig. 3B) along a hypothesized route—traditional media shared in social media (4). Perhaps unsurprisingly, we show that the composition of our social networks is the most important factor limiting the mix of content encountered in social media. The way that sharing occurs within these networks is not symmetric—liberals tend to be connected to fewer friends who share conservative content than conservatives (who tend to be linked to more friends who share liberal content).

Finally, we conclusively establish that on average in the context of Facebook, individual choices (2, 13, 15, 17) more than algorithms (3, 9) limit exposure to attitude-challenging content. Despite the differences in what individuals consume across ideological lines, our work suggests that individuals are exposed to more cross-cutting discourse in social media they would be under the digital reality envisioned by some (2, 6). Rather than people browsing only ideologically aligned news sources or opting out of hard news altogether, our work shows that social media exposes individuals to at least some ideologically cross-cutting viewpoints (4). Of course, we do not pass judgment on the normative value of cross-cutting exposure—though normative scholars often argue that exposure to a diverse “marketplace of ideas” is key to a healthy democracy (25), a number of studies find that exposure to cross-cutting viewpoints is associated with lower levels of political participation (22, 26, 27). Regardless, our work suggests that the power to expose oneself to perspectives from the other side in social media lies first and foremost with individuals.

Supplementary Materials

www.sciencemag.org/cgi/content/full/science.aaa1160/DC1

Materials and Methods

Supplementary Text

Figs. S1 to S10

Tables S1 to S6

References (28–35)

  • Received for publication 20 October 2014.
  • Accepted for publication 27 April 2015.

References and Notes

  1. ↵
    K. Olmstead, A. Mitchell, T. Rosenstiel, Navigating news online. Pew Research Center. Online at: http://www. journalism. org/analysis_report/navigating_news_online (2011).
  2. ↵
    1. W. L. Bennett,
    2. S. Iyengar

    , A New Era of Minimal Effects? The Changing Foundations of Political Communication. J. Commun. 58, 707–731 (2008).

    CrossRefWeb of ScienceGoogle Scholar
  3. ↵
    E. Pariser, The Filter Bubble: What the Internet Is Hiding from You (Penguin Press, London, 2011).
  4. ↵
    1. S. Messing,
    2. S. J. Westwood

    , Communic. Res. (2012).

    Google Scholar
  5. ↵
    E. Bakshy, I. Rosenn, C. Marlow, L. Adamic, Proceedings of the 21st international conference on World Wide Web Pages 1201.4145 (2012).
  6. ↵
    C. R. Sunstein, Republic.com 2.0 (Princeton University Press, 2007).
  7. ↵
    1. N. Stroud

    , Media Use and Political Predispositions: Revisiting the Concept of Selective Exposure. Polit. Behav. 30, 341–366 (2008).

    CrossRefWeb of ScienceGoogle Scholar
  8. ↵
    1. S. Kull,
    2. C. Ramsay,
    3. E. Lewis

    , Misperceptions, the Media, and the Iraq War. Polit. Sci. Q. 118, 569–598 (2003).

    CrossRefWeb of ScienceGoogle Scholar
  9. ↵
    S. Flaxman, S. Goel, J. M. Rao, Ideological segregation and the effects of social media on news consumption, SSRN Scholarly Paper ID 2363701, Social Science Research Network, Rochester, NY (2013).
  10. ↵
    1. T. Groeling

    , Media Bias by the Numbers: Challenges and Opportunities in the Empirical Study of Partisan News. Annu. Rev. Polit. Sci. 16, 129–151 (2013).

    CrossRefGoogle Scholar
  11. ↵
    1. M. Gentzkow,
    2. J. M. Shapiro

    , Ideological Segregation Online and Offline. Q. J. Econ. 126, 1799–1839 (2011).

    Abstract/FREE Full Text
  12. M. J. LaCour, A balanced information diet, not echo chambers: Evidence from a direct measure of media exposure, SSRN Scholarly Paper ID 2303138, Social Science Research Network, Rochester, NY (2013).
  13. ↵
    1. E. Lawrence,
    2. J. Sides,
    3. H. Farrell

    , Self-Segregation or Deliberation? Blog Readership, Participation, and Polarization in American Politics. Perspect. Polit. 8, 141 (2010).

    CrossRefGoogle Scholar
  14. ↵
    1. D. O. Sears,
    2. J. L. Freedman

    , Selective Exposure to Information: A Critical Review. Public Opin. Q. 31, 194 (1967).

    Abstract/FREE Full Text
  15. ↵
    1. N. A. Valentino,
    2. A. J. Banks,
    3. V. L. Hutchings,
    4. A. K. Davis

    , Selective Exposure in the Internet Age: The Interaction between Anxiety and Information Utility. Polit. Psychol. 30, 591–613 (2009).

    CrossRefGoogle Scholar
  16. ↵
    L. A. Adamic, N. Glance, Proceedings of the 3rd International Workshop on Link Discovery(ACM, New York, 2005), pp. 36–43.
  17. ↵
    1. S. Iyengar,
    2. K. S. Hahn

    , Red Media, Blue Media: Evidence of Ideological Selectivity in Media Use. J. Commun. 59, 19–39 (2009).

    CrossRefWeb of ScienceGoogle Scholar
  18. ↵
    M. Duggan, A. Smith, Social media update 2013. Pew Research Center. Online at: http://www.pewinternet.org/2013/12/30/social-media-update-2013/ (2013). (2013).
  19. ↵
    M. D. Conover et al., Fifth International AAAI Conference on Weblogs and Social Media (2011).
  20. ↵
    1. D. C. Mutz,
    2. J. J. Mondak

    , The Workplace as a Context for Cross-Cutting Political Discourse. J. Polit. 68, 140 (2006).

    CrossRefWeb of ScienceGoogle Scholar
  21. ↵
    1. S. Goel,
    2. W. Mason,
    3. D. J. Watts

    , Real and perceived attitude agreement in social networks. J. Pers. Soc. Psychol. 99, 611–621 (2010).

    CrossRefMedlineGoogle Scholar
  22. ↵
    1. D. C. Mutz

    , Cross-cutting Social Networks: Testing Democratic Theory in Practice. Am. Polit. Sci. Rev. 96, 112 (2002).

    CrossRefGoogle Scholar
  23. ↵
    B. Bishop, The Big Sort: Why the Clustering of Like-Minded America is Tearing Us Apart(Houghton Mifflin Harcourt, New York, 2008).
    1. D. C. Mutz,
    2. P. S. Martin

    , Am. Polit. Sci. Rev. 95, 97 (2001).

    Web of ScienceGoogle Scholar
  25. ↵
    1. T. Mendelberg

    , Political Decision Making. Deliber. Particip. 6, 151–193 (2002).

    Google Scholar
  26. ↵
    1. R. Huckfeldt,
    2. J. M. Mendez,
    3. T. Osborn

    , Disagreement, Ambivalence, and Engagement: The Political Consequences of Heterogeneous Networks. Polit. Psychol. 25, 65–95 (2004).

    CrossRefGoogle Scholar
  27. ↵
    1. R. Bond,
    2. S. Messing

    , Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook. Am. Polit. Sci. Rev. 109, 62–78 (2015).

    CrossRefGoogle Scholar
  28. ↵
    E. Riloff, Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI Press, Portland, Oregon, 1996), vol. 2 of AAAI’96, pp. 1044–1049.
  29. E. Riloff, Case-Based Reasoning Research and Development, K. D. Ashley, D. G. Bridge, eds., no. 2689 in Lecture Notes in Computer Science (Springer Berlin Heidelberg, 2003), pp. 4–4.
  30. S. Gupta, C. D. Manning, Proceedings of the SIGNLL Conference on Computational Natural Language Learning (SIGNLL, Baltimore, MD, 2014), vol. 98.
  31. S. Gupta, C. D. Manning, Proceedings of the ACL 2014 Workshop on Interactive Language Learning, Visualization, and Interfaces (ACL-ILLVI) (Baltimore, MD, 2014), p. 38.
  32. C. Budak, S. Goel, J. M. Rao, Fair and Balanced? Quantifying Media Bias through Crowdsourced Content Analysis (2014); available at http://ssrn.com/abstract=2526461.
    1. T. Groseclose,
    2. J. Milyo

    , A Measure of Media Bias. Q. J. Econ. 120, 1191–1237 (2005).

    Abstract/FREE Full Text
    1. M. Gentzkow,
    2. J. M. Shapiro

    , What Drives Media Slant? Evidence From U.S. Daily Newspapers. Econometrica 78, 35–71 (2010).

    CrossRefWeb of ScienceGoogle Scholar
  35. ↵
    1. S. K. Whitbourne

    , Openness to experience, identity flexibility, and life change in adults. J. Pers. Soc. Psychol. 50, 163–168 (1986).

    CrossRefMedlineGoogle Scholar
  36. Acknowledgments: We thank Jeremy Bailenson, Dean Eckles, Annie Franco, Justin Grimmer, Shanto Iyengar, Brian Karrer, Cliff Nass, Alex Peysakhovich, Rebecca Weiss, Sean Westwood and anonymous reviewers for their valuable feedback. The following code and data are archived in the Harvard Dataverse Network, http://dx.doi.org/10.7910/DVN/LDJ7MS, “Replication Data for: Exposure to Ideologically Diverse News and Opinion on Facebook”; R analysis code and aggregate data for deriving the main results (e.g., Table S5, S6); Python code and dictionaries for training and testing the hard-soft news classifier; Aggregate summary statistics of the distribution of ideological homophily in networks; Aggregate summary statistics of the distribution of ideological alignment for hard content;shared by the top 500 most shared websites. The authors of this work are employed and funded by Facebook. Facebook did not place any restrictions on the design and publication of this observational study, beyond the requirement that this work was to be done in compliance with Facebook’s Data Policy and research ethics review process (https://m.facebook.com/policy.php).