EPJ Data Science最新文献_第5页

Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study 评估 Twitter 对低可信度内容的算法放大：一项观察研究

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-03-07 DOI: 10.1140/epjds/s13688-024-00456-3

Giulio Corsi

{"title":"Evaluating Twitter’s algorithmic amplification of low-credibility content: an observational study","authors":"Giulio Corsi","doi":"10.1140/epjds/s13688-024-00456-3","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00456-3","url":null,"abstract":"Artificial intelligence (AI)-powered recommender systems play a crucial role in determining the content that users are exposed to on social media platforms. However, the behavioural patterns of these systems are often opaque, complicating the evaluation of their impact on the dissemination and consumption of disinformation and misinformation. To begin addressing this evidence gap, this study presents a measurement approach that uses observed digital traces to infer the status of algorithmic amplification of low-credibility content on Twitter over a 14-day period in January 2023. Using an original dataset of ≈ 2.7 million posts on COVID-19 and climate change published on the platform, this study identifies tweets sharing information from low-credibility domains, and uses a bootstrapping model with two stratifications, a tweet’s engagement level and a user’s followers level, to compare any differences in impressions generated between low-credibility and high-credibility samples. Additional stratification variables of toxicity, political bias, and verified status are also examined. This analysis provides valuable observational evidence on whether the Twitter algorithm favours the visibility of low-credibility content, with results indicating that, on aggregate, tweets containing low-credibility URL domains perform better than tweets that do not across both datasets. However, this effect is largely attributable to a difference in high-engagement, high-followers tweets, which are very impactful in terms of impressions generation, and are more likely receive amplified visibility when containing low-credibility content. Furthermore, high toxicity tweets and those with right-leaning bias see heightened amplification, as do low-credibility tweets from verified accounts. Ultimately, this suggests that Twitter’s recommender system may have facilitated the diffusion of false content by amplifying the visibility of low-credibility content with high-engagement generated by very influential users.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"27 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140054923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The right to audit and power asymmetries in algorithm auditing 审计权与算法审计中的权力不对称

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-03-07 DOI: 10.1140/epjds/s13688-024-00454-5

Aleksandra Urman, Ivan Smirnov, Jana Lasser

引用次数: 0

The simpliciality of higher-order networks 高阶网络的简单性

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-03-07 DOI: 10.1140/epjds/s13688-024-00458-1

Nicholas W. Landry, Jean-Gabriel Young, Nicole Eikmeier

引用次数: 0

Early warning signals for stock market crashes: empirical and analytical insights utilizing nonlinear methods 股市崩盘的预警信号：利用非线性方法的经验和分析见解

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-03-05 DOI: 10.1140/epjds/s13688-024-00457-2

Shijia Song, Handong Li

{"title":"Early warning signals for stock market crashes: empirical and analytical insights utilizing nonlinear methods","authors":"Shijia Song, Handong Li","doi":"10.1140/epjds/s13688-024-00457-2","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00457-2","url":null,"abstract":"This study introduces a comprehensive framework grounded in recurrence analysis, a tool of nonlinear dynamics, to detect potential early warning signals (EWS) for imminent phase transitions in financial systems, with the primary goal of anticipating severe financial crashes. We first conduct a simulation experiment to demonstrate that the indicators based on multiplex recurrence networks (MRNs), namely the average mutual information and the average edge overlap, can indicate state transitions in complex systems. Subsequently, we consider the constituent stocks of the China’s and the U.S. stock markets as empirical subjects, and establish MRNs based on multidimensional returns to monitor the nonlinear dynamics of market through the corresponding the indicators and topological structures. Empirical findings indicate that the primary indicators of MRNs offer valuable insights into significant financial events or periods of extreme instability. Notably, average mutual information demonstrates promise as an effective EWS for forecasting forthcoming financial crashes. An in-depth discussion and elucidation of the theoretical underpinnings for employing indicators of MRNs as EWS, the differences in indicator effectiveness, and the possible reasons for variations in the performance of the EWS across the two markets are provided. This paper contributes to the ongoing discourse on early warning extreme market volatility, emphasizing the applicability of recurrence analysis in predicting financial crashes.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"11 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140034872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Higher-order structures of local collaboration networks are associated with individual scientific productivity 地方合作网络的高阶结构与个人科学生产力相关联

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-02-28 DOI: 10.1140/epjds/s13688-024-00453-6

Wenlong Yang, Yang Wang

{"title":"Higher-order structures of local collaboration networks are associated with individual scientific productivity","authors":"Wenlong Yang, Yang Wang","doi":"10.1140/epjds/s13688-024-00453-6","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00453-6","url":null,"abstract":"The prevalence of teamwork in contemporary science has raised new questions about collaboration networks and the potential impact on research outcomes. Previous studies primarily focused on pairwise interactions between scientists when constructing collaboration networks, potentially overlooking group interactions among scientists. In this study, we introduce a higher-order network representation using algebraic topology to capture multi-agent interactions, i.e., simplicial complexes. Our main objective is to investigate the influence of higher-order structures in local collaboration networks on the productivity of the focal scientist. Leveraging a dataset comprising more than 3.7 million scientists from the Microsoft Academic Graph, we uncover several intriguing findings. Firstly, we observe an inverted U-shaped relationship between the number of disconnected components in the local collaboration network and scientific productivity. Secondly, there is a positive association between the presence of higher-order loops and individual scientific productivity, indicating the intriguing role of higher-order structures in advancing science. Thirdly, these effects hold across various scientific domains and scientists with different impacts, suggesting strong generalizability of our findings. The findings highlight the role of higher-order loops in shaping the development of individual scientists, thus may have implications for nurturing scientific talent and promoting innovative breakthroughs.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"46 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140006988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Critical computational social science 批判性计算社会科学

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-02-26 DOI: 10.1140/epjds/s13688-023-00433-2

Sarah Shugars

引用次数: 0

Thinking spatially in computational social science 计算社会科学中的空间思维

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-02-26 DOI: 10.1140/epjds/s13688-023-00443-0

引用次数: 0

Charting mobility patterns in the scientific knowledge landscape 描绘科学知识领域的流动模式

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-02-20 DOI: 10.1140/epjds/s13688-024-00451-8

引用次数: 0

Cryptocurrency co-investment network: token returns reflect investment patterns 加密货币联合投资网络：代币回报反映投资模式

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-02-20 DOI: 10.1140/epjds/s13688-023-00446-x

Luca Mungo, Silvia Bartolucci, Laura Alessandretti

{"title":"Cryptocurrency co-investment network: token returns reflect investment patterns","authors":"Luca Mungo, Silvia Bartolucci, Laura Alessandretti","doi":"10.1140/epjds/s13688-023-00446-x","DOIUrl":"https://doi.org/10.1140/epjds/s13688-023-00446-x","url":null,"abstract":"Since the introduction of Bitcoin in 2009, the dramatic and unsteady evolution of the cryptocurrency market has also been driven by large investments by traditional and cryptocurrency-focused hedge funds. Notwithstanding their critical role, our understanding of the relationship between institutional investments and the evolution of the cryptocurrency market has remained limited, also due to the lack of comprehensive data describing investments over time. In this study, we present a quantitative study of cryptocurrency institutional investments based on a dataset collected for 1324 currencies in the period between 2014 and 2022 from Crunchbase, one of the largest platforms gathering business information. We show that the evolution of the cryptocurrency market capitalization is highly correlated with the size of institutional investments, thus confirming their important role. Further, we find that the market is dominated by the presence of a group of prominent investors who tend to specialise by focusing on particular technologies. Finally, studying the co-investment network of currencies that share common investors, we show that assets with shared investors tend to be characterized by similar market behaviour. Our work sheds light on the role played by institutional investors and provides a basis for further research on their influence in the cryptocurrency ecosystem.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"23 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139925405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identifying the systemic importance and systemic vulnerability of financial institutions based on portfolio similarity correlation network 基于投资组合相似性相关网络识别金融机构的系统重要性和系统脆弱性

IF 3.6 2区计算机科学

EPJ Data Science Pub Date : 2024-01-31 DOI: 10.1140/epjds/s13688-024-00449-2

Manjin Shao, Hong Fan

{"title":"Identifying the systemic importance and systemic vulnerability of financial institutions based on portfolio similarity correlation network","authors":"Manjin Shao, Hong Fan","doi":"10.1140/epjds/s13688-024-00449-2","DOIUrl":"https://doi.org/10.1140/epjds/s13688-024-00449-2","url":null,"abstract":"The indirect correlation among financial institutions, stemming from similarities in their portfolios, is a primary driver of systemic risk. However, most existing research overlooks the influence of portfolio similarity among various types of financial institutions on this risk. Therefore, we construct the network of portfolio similarity correlations among different types of financial institutions, based on measurements of portfolio similarity. Utilizing the expanded fire sale contagion model, we offer a comprehensive assessment of systemic risk for Chinese financial institutions. Initially, we introduce indicators for systemic risk, systemic importance, and systemic vulnerability. Subsequently, we examine the cross-sectional and time-series characteristics of these institutions’ systemic importance and vulnerability within the context of the portfolio similarity correlation network. Our empirical findings reveal a high degree of portfolio similarity between banks and insurance companies, contrasted with lower similarity between banks and securities firms. Moreover, when considering the portfolio similarity correlation network, both the systemic importance and vulnerability of Chinese banks and insurance companies surpass those of securities firms in both cross-sectional and temporal dimensions. Notably, our analysis further illustrates that a financial institution’s systemic importance and vulnerability are strongly and positively associated with the magnitude of portfolio similarity between that institution and others.","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"2 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139645070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0