Journal of Computational Social Science最新文献

{"title":"#Election2020: the first public Twitter dataset on the 2020 US Presidential election.","authors":"Emily Chen,&nbsp;Ashok Deb,&nbsp;Emilio Ferrara","doi":"10.1007/s42001-021-00117-9","DOIUrl":"https://doi.org/10.1007/s42001-021-00117-9","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"1-18"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42001-021-00117-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25565772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The global migration network of sex-workers.","authors":"Luis E C Rocha,&nbsp;Petter Holme,&nbsp;Claudio D G Linhares","doi":"10.1007/s42001-021-00156-2","DOIUrl":"https://doi.org/10.1007/s42001-021-00156-2","url":null,"abstract":"<p><p>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 <math><mrow><mn>15.9</mn> <mo>%</mo></mrow> </math> . Only sex-workers coming from <math><mrow><mn>77</mn> <mo>%</mo></mrow> </math> 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.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"969-985"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8755989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39918382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The \"flat peer learning\" agent-based model.","authors":"Philippe Collard","doi":"10.1007/s42001-021-00120-0","DOIUrl":"https://doi.org/10.1007/s42001-021-00120-0","url":null,"abstract":"<p><p>This paper deals with <i>peer learning</i> and, in particular, with the phenomena of <i>exclusion</i>; 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. <i>Flat learning</i> 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 <i>Flat Peer Learning</i> 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.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"161-187"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42001-021-00120-0","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39067169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operation gridlock: opposite sides, opposite strategies.","authors":"Matthew Babcock,&nbsp;Kathleen M Carley","doi":"10.1007/s42001-021-00133-9","DOIUrl":"https://doi.org/10.1007/s42001-021-00133-9","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"477-501"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42001-021-00133-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39221254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How does \"A Bit of Everything American\" state feel about COVID-19? A quantitative Twitter analysis of the pandemic in Ohio.","authors":"Cantay Caliskan","doi":"10.1007/s42001-021-00111-1","DOIUrl":"https://doi.org/10.1007/s42001-021-00111-1","url":null,"abstract":"<p><p>COVID-19 has proven itself to be one of the most important events of the last two centuries. This defining moment in our lives has created wide-ranging discussions in many segments of our societies, both politically and socially. Over time, the pandemic has been associated with many social and political topics, as well as sentiments and emotions. Twitter offers a platform to understand these effects. The primary objective of this study is to capture the awareness and sentiment about COVID-19-related issues and to find how they relate to the number of cases and deaths in a representative region of the United States. The study uses a unique dataset consisting of over 46 million tweets from over 91,000 users in 88 counties of the state of Ohio, a state-of-the-art deep learning model to measure and detect awareness and emotions. The data collected is analyzed using OLS regression and System-GMM dynamic panel. Findings indicate that the pandemic has drastically changed the perception of the Republican party in the society. Individual motivations are strongly influenced by ideological choices and this ultimately affects individual pandemic-related outcomes. The paper contributes to the literature by expanding the knowledge on COVID-19 (i), offering a representative result for the United States by focusing on an \"average\" state like Ohio (ii), and incorporating the sentiment and emotions into the calculation of awareness (iii).</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"19-45"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42001-021-00111-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25598801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An inclusive, real-world investigation of persuasion in language and verbal behavior.","authors":"Vivian P Ta,&nbsp;Ryan L Boyd,&nbsp;Sarah Seraj,&nbsp;Anne Keller,&nbsp;Caroline Griffith,&nbsp;Alexia Loggarakis,&nbsp;Lael Medema","doi":"10.1007/s42001-021-00153-5","DOIUrl":"https://doi.org/10.1007/s42001-021-00153-5","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"883-903"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633087/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39573464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"News loopholing: Telegram news as portable alternative media.","authors":"Ahmed Al-Rawi","doi":"10.1007/s42001-021-00155-3","DOIUrl":"https://doi.org/10.1007/s42001-021-00155-3","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"949-968"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8715841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39783305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linguistic, cultural, and narrative capital: computational and human readings of transfer admissions essays.","authors":"A J Alvero,&nbsp;Jasmine Pal,&nbsp;Katelyn M Moussavian","doi":"10.1007/s42001-022-00185-5","DOIUrl":"https://doi.org/10.1007/s42001-022-00185-5","url":null,"abstract":"<p><p>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42001-022-00185-5.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"5 2","pages":"1709-1734"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33517693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Botometer 101: social bot practicum for computational social scientists.","authors":"Kai-Cheng Yang, Emilio Ferrara, Filippo Menczer","doi":"10.1007/s42001-022-00177-5","DOIUrl":"10.1007/s42001-022-00177-5","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"5 2","pages":"1511-1528"},"PeriodicalIF":2.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9391657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33444586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the roles of bots on Twitter during the COVID-19 infodemic.","authors":"Wentao Xu,&nbsp;Kazutoshi Sasahara","doi":"10.1007/s42001-021-00139-3","DOIUrl":"https://doi.org/10.1007/s42001-021-00139-3","url":null,"abstract":"<p><p>An infodemic is an emerging phenomenon caused by an overabundance of information online. This proliferation of information makes it difficult for the public to distinguish trustworthy news and credible information from untrustworthy sites and non-credible sources. The perils of an infodemic debuted with the outbreak of the COVID-19 pandemic and bots (i.e., automated accounts controlled by a set of algorithms) that are suspected of spreading the infodemic. Although previous research has revealed that bots played a central role in spreading misinformation during major political events, how bots behavior during the infodemic is unclear. In this paper, we examined the roles of bots in the case of the COVID-19 infodemic and the diffusion of non-credible information such as \"5G\" and \"Bill Gates\" conspiracy theories and content related to \"Trump\" and \"WHO\" by analyzing retweet networks and retweeted items. We show the segregated topology of their retweet networks, which indicates that right-wing self-media accounts and conspiracy theorists may lead to this opinion cleavage, while malicious bots might favor amplification of the diffusion of non-credible information. Although the basic influence of information diffusion could be larger in human users than bots, the effects of bots are non-negligible under an infodemic situation.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s42001-021-00139-3.</p>","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":" ","pages":"591-609"},"PeriodicalIF":3.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8403696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39386626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}