Journal of Computational Social Science最新文献

[This corrects the article DOI: 10.1007/s42001-021-00139-3.].

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.

While user-generated online content (UGC) is increasingly available, public opinion studies are yet to fully exploit the abundance and richness of online data. This study contributes to the practical knowledge of user-generated online content and machine learning techniques that can be used for the analysis of UGC. For this purpose, we explore the potential of user-generated content and present an application of natural language pre-processing, text mining and sentiment analysis to the question of public satisfaction with healthcare systems. Concretely, we analyze 634 online comments reflecting attitudes towards healthcare services in different countries. Our analysis identifies the frequency of topics related to healthcare services in textual content of the comments and attempts to classify and rank national healthcare systems based on the respondents' sentiment scores. In this paper, we describe our approach, summarize our main findings, and compare them with the results from cross-national surveys. Finally, we outline the typical limitations inherent in the analysis of user-generated online content and suggest avenues for future research.

Studies on the possibility of predicting critical transitions with statistical methods known as early warning signals (EWS) are often conducted on data generated with equation-based models (EBMs). These models base on difference or differential equations, which aggregate a system's components in a mathematical term and therefore do not allow for a detailed analysis of interactions on micro-level. As an alternative, we suggest a simple, but highly flexible agent-based model (ABM), which, when applying EWS-analysis, gives reason to (a) consider social interaction, in particular negative feedback effects, as an essential trigger of critical transitions, and (b) to differentiate social interactions, for example in network representations, into a core and a periphery of agents and focus attention on the periphery. Results are tested against time series from a networked version of the Ising-model, which is often used as example for generating hysteretic critical transitions.

Credible evidence-based political discourse is a critical pillar of democracy and is at the core of guaranteeing free and fair elections. The study of online chatter is paramount, especially in the wake of important voting events like the recent November 3, 2020 U.S. Presidential election and the inauguration on January 21, 2021. Limited access to social media data is often the primary obstacle that limits our abilities to study and understand online political discourse. To mitigate this impediment and empower the Computational Social Science research community, we are publicly releasing a massive-scale, longitudinal dataset of U.S. politics- and election-related tweets. This multilingual dataset encompasses over 1.2 billion tweets and tracks all salient U.S. political trends, actors, and events from 2019 to the time of this writing. It predates and spans the entire period of the Republican and Democratic primaries, with real-time tracking of all presidential contenders on both sides of the aisle. The dataset also focuses on presidential and vice-presidential candidates, the presidential elections and the transition from the Trump administration to the Biden administration. Our dataset release is curated, documented, and will continue to track relevant events. We hope that the academic community, computational journalists, and research practitioners alike will all take advantage of our dataset to study relevant scientific and social issues, including problems like misinformation, information manipulation, conspiracies, and the distortion of online political discourse that has been prevalent in the context of recent election events in the United States. Our dataset is available at: https://github.com/echen102/us-pres-elections-2020.

Differences in the social and economic environment across countries encourage humans to migrate in search of better living conditions, including job opportunities, higher salaries, security and welfare. Quantifying global migration is, however, challenging because of poor recording, privacy issues and residence status. This is particularly critical for some classes of migrants involved in stigmatised, unregulated or illegal activities. Escorting services or high-end prostitution are well-paid activities that attract workers all around the world. In this paper, we study international migration patterns of sex-workers by using network methods. Using an extensive international online advertisement directory of escorting services and information about individual escorts, we reconstruct a migrant flow network where nodes represent either origin or destination countries. The links represent the direct routes between two countries. The migration network of sex-workers shows different structural patterns than the migration of the general population. The network contains a strong core where mutual migration is often observed between a group of high-income European countries, yet Europe is split into different network communities with specific ties to non-European countries. We find non-reciprocal relations between countries, with some of them mostly offering while others attract workers. The Gross Domestic Product per capita (GDPc) is a good indicator of country attractiveness for incoming workers and service rates but is unrelated to the probability of emigration. The median financial gain of migrating, in comparison to working at the home country, is $15.9\%$ . Only sex-workers coming from $77\%$ of the countries have financial gains with migration and average gains decrease with the GDPc of the country of origin. Our results suggest that high-end sex-worker migration is regulated by economic, geographic and cultural aspects.

Twitter and other social media platforms are important tools for competing groups to push their preferred messaging and respond to opposing views. Special attention has been paid to the role these tools play in times of emergency and important public decision-making events such as during the current COVID-19 pandemic. Here, we analyze the Pro- and Anti-Protest sides of the Twitter discussion surrounding the first few weeks of the anti-lockdown protests in the United States. We find that these opposing groups mirror the partisan divide regarding the protests in their use of specific phrases and in their sharing of external links. We then compare the users in each group and their actions and find that the Pro-Protest side acts more proactively, is more centrally organized, engages with the opposing side less, and appears to rely more on bot-like or troll-like users. In contrast, the Anti-Protest side is more reactive, has a larger presence of verified account activity (both as actors and targets), and appears to have been more successful in spreading its message in terms of both tweet volume and in attracting more regular type users. Our work provides insights into the organization of opposing sides of the Twitter debate and discussions over responses to the COVID-19 emergency and helps set the stage for further work in this area.

This paper deals with peer learning and, in particular, with the phenomena of exclusion; it proposes to model a group of learners where everyone has his own behaviour that expresses his way of following a curriculum. The focus is on individual motivations that avoid disadvantage certain individuals while optimising behaviour at the community level; in this context, the approach is based on the belief that the induced learning dynamics can be clarified by the contribution of agent-based modelling and its entry into the field of peer learning simulation. Flat learning means here that every learner features the same initial skill level, along with the same opportunities to learn both independently and with the help of peers. To address this topic the paper proposes the Flat Peer Learning agent-based computational model inspired by the Vygotsky's social and learning theory. The paper shows that even if strict equity could be guaranteed, educators would still be faced with the dilemma of having to choose between optimising the learning process for the group or preventing exclusion for some.

This paper deals with foreign state-run media outlets that disseminate Persian language news targeted to the Iranian public. More specifically, it focuses on the mobile news app Telegram by undertaking a content analysis of a sample of the top 400 most viewed stories across four channels, i.e., BBC Persian, Voice of America's Persian language service VOA Farsi, Radio Farda, and Iran International television channel. It also offers a topic modelling of all news stories posted. Results show that most of the news coverage centered on politics, particularly with an emphasis on internal Iranian issues, while a few other channels repeatedly urged their followers to submit not only their email addresses and other private information, but also photographs and/or videos of anti-government protests. Conceptually, I consider these channels as portable alternative media, as opposed to state-run news media, since the Iranian public seeks them out as sources of political information that assist them in better understanding world news and, most importantly, news about their own country. The Telegram instant messaging app is related to the meso dimension of alternative media, meaning that it is characterized by the unique production and dissemination means it utilizes. This paper concludes by highlighting the implications of foreign state-run news outlets using news loopholing to disseminate information, while simultaneously collecting private information about their users and/or potentially risking their safety.

Linguistic features of a message necessarily shape its persuasive appeal. However, studies have largely examined the effect of linguistic features on persuasion in isolation and do not incorporate properties of language that are often involved in real-world persuasion. As such, little is known about the key verbal dimensions of persuasion or the relative impact of linguistic features on a message's persuasive appeal in real-world social interactions. We collected large-scale data of online social interactions from a social media website in which users engage in debates in an attempt to change each other's views on any topic. Messages that successfully changed a user's views are explicitly marked by the user themselves. We simultaneously examined linguistic features that have been previously linked with message persuasiveness between persuasive and non-persuasive messages. Linguistic features that drive persuasion fell along three central dimensions: structural complexity, negative emotionality, and positive emotionality. Word count, lexical diversity, reading difficulty, analytical language, and self-references emerged as most essential to a message's persuasive appeal: messages that were longer, more analytic, less anecdotal, more difficult to read, and less lexically varied had significantly greater odds of being persuasive. These results provide a more parsimonious understanding of the social psychological pathways to persuasion as it operates in the real world through verbal behavior. Our results inform theories that address the role of language in persuasion, and provide insight into effective persuasion in digital environments.