Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>News and social media play an important role in public political discourse. It is not clear what quantifiable relationships public discussions of politics have with official discourse within legislative bodies. In this study we present an analysis of how language used by Members of Parliament (MPs) in the United Kingdom (UK) changes after social media posts and online reactions to those posts. We consider three domains: news articles posted on Facebook in the UK, speeches in the questions-debates in the UK House of Commons, and Tweets by UK MPs. Our method works by quantifying how the words used in one domain become more common in another domain after an event such as a social media post. Our results show that words used in one domain later appear more commonly in other domains. For instance after each article on Facebook, we estimate that on average 4 in 100,000 words in Commons speeches had changed, becoming more similar to the language in the article. We also find that the extent of this language change positively correlates with the number of comments and emotional interactions on Facebook. The observed language change differs between political parties; in particular, changes in word use by Labour MPs are more strongly related to social media content than that of Conservative MPs. We argue that the magnitude of this word flow is quite substantial given the large volume of news articles shared on Facebook. Our method and results quantify how parliamentary attention follows public interest as expressed on Facebook and also indicate how this effect may be stronger for posts which evoke reactions on Facebook associated with laughter or anger.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Temporal social networks of human interactions are preponderant in understanding the fundamental patterns of human behavior. In these networks, interactions occur locally between individuals (i.e., nodes) who connect with each other at different times, culminating into a complex system-wide web that has a dynamic composition. Dynamic behavior in networks occurs not only locally but also at the global level, as systems expand or shrink due either to: changes in the size of node population or variations in the chance of a connection between two nodes. Here, we propose a numerical maximum-likelihood method to estimate population size and the probability of two nodes connecting at any given point in time. An advantage of the method is that it relies only on aggregate quantities, which are easy to access and free from privacy issues. Our approach enables us to identify the simultaneous (rather than the asynchronous) contribution of each mechanism in the densification and sparsification of human contacts, providing a better understanding of how humans collectively construct and deconstruct social networks.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The ubiquity of digital music consumption has made it possible to extract information about modern music that allows us to perform large scale analysis of stylistic change over time. In order to uncover underlying patterns in cultural evolution, we examine the relationship between the established characteristics of different genres and styles, and the introduction of novel ideas that fuel this ongoing creative evolution. To understand how this dynamic plays out and shapes the cultural ecosystem, we compare musical artifacts to their contemporaries to identify novel artifacts, study the relationship between novelty and commercial success, and connect this to the changes in musical content that we can observe over time. Using Music Information Retrieval (MIR) data and lyrics from Billboard Hot 100 songs between 1974-2013, we calculate a novelty score for each song’s aural attributes and lyrics. Comparing both scores to the popularity of the song following its release, we uncover key patterns in the relationship between novelty and audience reception. Additionally, we look at the link between novelty and the likelihood that a song was influential given where its MIR and lyrical features fit within the larger trends we observed.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Scientists collaborate through intricate networks, which impact the quality and scope of their research. At the same time, funding and institutional arrangements, as well as scientific and political cultures, affect the structure of collaboration networks. Since such arrangements and cultures differ across regions in the world in systematic ways, we surmise that collaboration networks and impact should also differ systematically across regions. To test this, we compare the structure of collaboration networks among prominent researchers in North America and Europe. We find that prominent researchers in Europe establish denser collaboration networks, whereas those in North America establish more decentralized networks. We also find that the impact of the publications of prominent researchers in North America is significantly higher than for those in Europe, both when they collaborate with other prominent researchers and when they do not. Although Europeans collaborate with other prominent researchers more often, which increases their impact, we also find that repeated collaboration among prominent researchers decreases the synergistic effect of collaborating.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Literacy is one of the most fundamental skills for people to access and navigate today’s digital environment. This work systematically studies the language literacy skills of online populations for more than 160 countries and regions across the world, including many low-resourced countries where official literacy data are particularly sparse. Leveraging public data on Facebook, we develop a population-level literacy estimate for the online population that is based on aggregated and de-identified public posts written by adult Facebook users globally, significantly improving both the coverage and resolution of existing literacy tracking data. We found that, on Facebook, women collectively show higher language literacy than men in many countries, but substantial gaps remain in Africa and Asia. Further, our analysis reveals a considerable regional gap within a country that is associated with multiple socio-technical inequalities, suggesting an “inequality paradox” – where the online language skill disparity interacts with offline socioeconomic inequalities in complex ways. These findings have implications for global women’s empowerment and socioeconomic inequalities.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The Russian invasion of Ukraine has caused large scale destruction, significant loss of life, and the displacement of millions of people. Besides those fleeing direct conflict in Ukraine, many individuals in Russia are also thought to have moved to third countries. In particular the exodus of skilled human capital, sometimes called brain drain, out of Russia may have a significant effect on the course of the war and the Russian economy in the long run. Yet quantifying brain drain, especially during crisis situations is generally difficult. This hinders our ability to understand its drivers and to anticipate its consequences. To address this gap, I draw on and extend a large scale dataset of the locations of highly active software developers collected in February 2021, one year before the invasion. Revisiting those developers that had been located in Russia in 2021, I confirm an ongoing exodus of developers from Russia in snapshots taken in June and November 2022. By November 11.1% of Russian developers list a new country, compared with 2.8% of developers from comparable countries in the region but not directly involved in the conflict. 13.2% of Russian developers have obscured their location (vs. 2.4% in the comparison set). Developers leaving Russia were significantly more active and central in the collaboration network than those who remain. This suggests that many of the most important developers have already left Russia. In some receiving countries the number of arrivals is significant: I estimate an increase in the number of local software developers of 42% in Armenia, 60% in Cyprus and 94% in Georgia.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers’ behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The aim of this paper is to analyze the Arab peoples reactions and attitudes towards the Russo-Ukraine War through the social media of posted tweets, as a fast means to express opinions. We scrapped over 3 million tweets using some keywords that are related to the war and performed sentiment, emotion, and partiality analyses. For sentiment analysis, we employed a voting technique of several pre-trained Arabic language foundational models. For emotion analysis, we utilized a pre-constructed emotion lexicon. The partiality is analyzed through classifying tweets as being ‘Pro-Russia’, ‘Pro-Ukraine’, or ‘Neither’; and it indicates the bias or empathy towards either of the conflicting parties. This was achieved by constructing a weighted lexicon of n-grams related to either side. We found that the majority of the tweets carried ‘Negative’ sentiment. Emotions were not that obvious with a lot of tweets carrying ‘Mixed Feelings’. The more decisive tweets conveyed either ‘Joy’ or ‘Anger’ emotions. This may be attributed to celebrating victory (‘Joy’) or complaining from destruction (‘Anger’). Finally, for partiality analysis, the amount of tweets classified as being ‘Pro-Ukraine’ was slightly greater than Pro-Russia’ at the beginning of the war (specifically from Feb 2022 till April 2022) then slowly began to decrease until they nearly converged at the start of June 2022 with a shift happening in the empathy towards Russia in August 2022. Our Interpretation for that is with the initial Russian fierce and surprise attack at the beginning and the amount of refugees who escaped to neighboring countries, Ukraine gained much empathy. However, by April 2022, Russian intensity has been decreased and with heavy sanctions the U.S. and West have applied on Russia, Russia has begun to gain such empathy with decrease on the Ukrainian side.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Complex systems often comprise many kinds of components which vary over many orders of magnitude in size: Populations of cities in countries, individual and corporate wealth in economies, species abundance in ecologies, word frequency in natural language, and node degree in complex networks. Here, we introduce ‘allotaxonometry’ along with ‘rank-turbulence divergence’ (RTD), a tunable instrument for comparing any two ranked lists of components. We analytically develop our rank-based divergence in a series of steps, and then establish a rank-based allotaxonograph which pairs a map-like histogram for rank-rank pairs with an ordered list of components according to divergence contribution. We explore the performance of rank-turbulence divergence, which we view as an instrument of ‘type calculus’, for a series of distinct settings including: Language use on Twitter and in books, species abundance, baby name popularity, market capitalization, performance in sports, mortality causes, and job titles. We provide a series of supplementary flipbooks which demonstrate the tunability and storytelling power of rank-based allotaxonometry.</jats:p>

<jats:title>Abstract</jats:title><jats:p>This paper explores how individuals’ language use in gender-specific groups (“mothers” and “fathers”) compares to their interactions when referred to as “parents.” Language adaptation based on the audience is well-documented, yet large-scale studies of naturally-occurring audience effects are rare. To address this, we investigate audience and gender effects in the context of parenting, where gender plays a significant role. We focus on interactions within Reddit, particularly in the parenting Subreddits r/Daddit, r/Mommit, and r/Parenting, which cater to distinct audiences. By analyzing user posts using word embeddings, we measure similarities between user-tokens and word-tokens, also considering differences among high and low self-monitors. Results reveal that in mixed-gender contexts, mothers and fathers exhibit similar behavior in discussing a wide range of topics, while fathers emphasize more on educational and family advice. Single-gender Subreddits see more focused discussions. Mothers in r/Mommit discuss medical care, sleep, potty training, and food, distinguishing themselves. In terms of individual differences, we found that, especially on r/Parenting, high self-monitors tend to conform more to the norms of the Subreddit by discussing more of the topics associated with the Subreddit.</jats:p>