Journal of Computational Social Science最新文献

As individuals are susceptible to social influences from those to whom they are connected, structures of social networks have been an important research subject in social sciences. However, quantifying these structures in real life has been comparatively more difficult. One reason is data collection methods-how to assess elusive social contacts (e.g., unintended brief contacts in a coffee room); however, recent studies have overcome this difficulty using wearable devices. Another reason relates to the multi-layered nature of social relations-individuals are often embedded in multiple networks that are overlapping and complicatedly interwoven. A novel method to disentangle such complexity is needed. Here, we propose a new method to detect multiple latent subnetworks behind interpersonal contacts. We collected data of proximities among residents in a Japanese farming community for 7 months using wearable devices which detect other devices nearby via Bluetooth communication. We performed non-negative matrix factorization (NMF) on the proximity log sequences and extracted five latent subnetworks. One of the subnetworks represented social relations regarding farming activities, and another subnetwork captured the patterns of social contacts taking place in a community hall, which played the role of a "hub" of diverse residents within the community. We also found that the eigenvector centrality score in the farming-related network was positively associated with self-reported pro-community attitude, while the centrality score regarding the community hall was associated with increased self-reported health.

Supplementary information: The online version contains supplementary material available at 10.1007/s42001-022-00162-y.

With increasing growth of both social media and urbanization, studying urban life through the empirical lens of social media has led to some interesting research opportunities and questions. It is well-recognized that as a 'social animal', most humans are deeply embedded both in their cultural milieu and in broader society that extends well beyond close family, including neighborhoods, communities and workplaces. In this article, we study this embeddedness by leveraging urban dwellers' social media footprint. Specifically, we define and empirically study the issue of spatio-textual affinity by collecting many millions of geotagged tweets collected from two diverse metropolises within the United States: the Boroughs of New York City, and the County of Los Angeles. Spatio-textual affinity is the intuitive hypothesis that tweets coming from similar locations (spatial affinity) will tend to be topically similar (textual affinity). This simple definition of the problem belies the complexity of measuring it, since (re-tweets notwithstanding) two tweets are never truly identical either spatially or textually. Workable definitions of affinity along both dimensions are required, as are appropriate experimental designs, visualizations and measurements. In addition to providing such definitions and a viable framework for conducting spatio-textual affinity experiments on Twitter data, we provide detailed results illustrating how our framework can be used to compare and contrast two important metropolitan areas from multiple perspectives and granularities.

Variation in college application materials related to social stratification is a contentious topic in social science and national discourse in the United States. This line of research has also started to use computational methods to consider qualitative materials, such as personal statements and letters of recommendation. Despite the prominence of this topic, fewer studies have considered a fairly common academic pathway: transferring. Approximately 40% of all college students in the US transfer schools at least once. One quirk of the system is that students from community colleges are applying for the same spots for students already enrolled in four year schools and trying to transfer. How might different aspects the transfer application itself correlate with institutional stratification and make students more or less distinguishable? We use a dataset of 20,532 transfer admissions essays submitted to the University of California system to describe how transfer applicants vary linguistically, culturally, and narratively with respect to academic pathways and essay prompts. Using a variety of methods for computational text analysis and qualitative coding, we find that essays written by community college students tend to be distinct from those written by university students. However, the strength and character of these results changed with the writing prompt provided to applicants. These results show how some forms of stratification, such as the type of school students attend, inform educational processes intended to equalize opportunity and how combining computational and human reading might illuminate these patterns.

Supplementary information: The online version contains supplementary material available at 10.1007/s42001-022-00185-5.

Social bots have become an important component of online social media. Deceptive bots, in particular, can manipulate online discussions of important issues ranging from elections to public health, threatening the constructive exchange of information. Their ubiquity makes them an interesting research subject and requires researchers to properly handle them when conducting studies using social media data. Therefore, it is important for researchers to gain access to bot detection tools that are reliable and easy to use. This paper aims to provide an introductory tutorial of Botometer, a public tool for bot detection on Twitter, for readers who are new to this topic and may not be familiar with programming and machine learning. We introduce how Botometer works, the different ways users can access it, and present a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discuss recommended practice for using Botometer.