社会计算(英文)最新文献_第9页

Special Issue on Social Dynamics of COVID-19 新冠肺炎社会动态专刊

社会计算(英文) Pub Date : 2021-06-01 DOI: 10.23919/JSC.2021.0012

引用次数: 0

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science 从符号到嵌入：计算社会科学中两种表征的故事

社会计算(英文) Pub Date : 2021-06-01 DOI: 10.23919/JSC.2021.0011

Huimin Chen;Cheng Yang;Xuanming Zhang;Zhiyuan Liu;Maosong Sun;Jianbin Jin

{"title":"From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science","authors":"Huimin Chen;Cheng Yang;Xuanming Zhang;Zhiyuan Liu;Maosong Sun;Jianbin Jin","doi":"10.23919/JSC.2021.0011","DOIUrl":"https://doi.org/10.23919/JSC.2021.0011","url":null,"abstract":"Computational Social Science (CSS), aiming at utilizing computational methods to address social science problems, is a recent emerging and fast-developing field. The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation. However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS? To explore the answer, we give a thorough review of data representations in CSS for both text and network. Specifically, we summarize existing representations into two schemes, namely symbol-based and embedding-based representations, and introduce a series of typical methods for each scheme. Afterwards, we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS. From the statistics of these applications, we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade. Finally, we discuss several key challenges and open issues for future directions. This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 2","pages":"103-156"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/9520766/09520816.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50302385","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}

引用次数: 11

Uncovering the Online Social Structure Surrounding COVID-19 揭示围绕新冠肺炎的网络社会结构

社会计算(英文) Pub Date : 2021-06-01 DOI: 10.23919/JSC.2021.0008

Philip D. Waggoner;Robert Y. Shapiro;Samuel Frederick;Ming Gong

引用次数: 6

Hybrid Predictive Ensembles: Synergies Between Human and Computational Forecasts 混合预测集成:人与计算预测之间的协同作用

社会计算(英文) Pub Date : 2021-06-01 DOI: 10.23919/JSC.2021.0009

Lu Hong;PJ Lamberson;Scott E Page

引用次数: 5

Cover 盖

社会计算(英文) Pub Date : 2021-06-01 DOI: 10.23919/JSCTUP.2021.9520808

引用次数: 0

Message from Editors-in-Chief 主编寄语

社会计算(英文) Pub Date : 2021-06-01 DOI: 10.23919/JSC.2021.0014

引用次数: 0

Literary Destination Familiarity and Inbound Tourism: Evidence from Mainland China 文学目的地熟悉度与入境旅游——来自中国大陆的证据

社会计算(英文) Pub Date : 2021-06-01 DOI: 10.23919/JSC.2021.0013

Guodong Ju;Jiankun Liu;Guangye He;Xinyi Zhang;Fei Yan

{"title":"Literary Destination Familiarity and Inbound Tourism: Evidence from Mainland China","authors":"Guodong Ju;Jiankun Liu;Guangye He;Xinyi Zhang;Fei Yan","doi":"10.23919/JSC.2021.0013","DOIUrl":"https://doi.org/10.23919/JSC.2021.0013","url":null,"abstract":"Destination familiarity is an important non-economic determinant of tourists' destination choice that has not been adequately studied. This study posits a literary dimension to the concept of destination familiarity—that is, the extent to which tourists have gained familiarity with a given destination through literature—and seeks to investigate the impact of this form of familiarity on inbound tourism to Mainland China. Employing the English fiction dataset of the Google Books corpus, the New York Times annotated corpus, and the Time magazine corpus, we construct two types of destination familiarity based on literary texts: affection-based destination familiarity and knowledge-based destination familiarity. The results from dynamic panel estimation (1994–2004) demonstrate that the higher the degree of affection-based destination familiarity with a province in the previous year, the larger the number of inbound tourists the following year. Examining the influence of literature and its consumption on tourism activities sheds light on the dynamics of sustainable tourism development in emerging markets.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 2","pages":"193-206"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/9520766/09520809.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50302388","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}

引用次数: 3

Message from Editors-in-Chief 主编寄语

社会计算(英文) Pub Date : 2021-02-17 DOI: 10.23919/JSC.2021.0006

引用次数: 0

Using Twitter Bios to Measure Changes in Self-Identity: Are Americans Defining Themselves More Politically Over Time? 使用推特生物测量自我认同的变化：随着时间的推移，美国人对自我的定义是否更加政治化？

社会计算(英文) Pub Date : 2021-02-16 DOI: 10.23919/JSC.2021.0002

Nick Rogers;Jason J. Jones

{"title":"Using Twitter Bios to Measure Changes in Self-Identity: Are Americans Defining Themselves More Politically Over Time?","authors":"Nick Rogers;Jason J. Jones","doi":"10.23919/JSC.2021.0002","DOIUrl":"https://doi.org/10.23919/JSC.2021.0002","url":null,"abstract":"Are Americans weaving their political views more tightly into the fabric of their self-identity over time? If so, then we might expect partisan disagreements to continue becoming more emotional, tribal, and intractable. Much recent scholarship has speculated that this politicization of Americans' identity is occurring, but there has been little compelling attempt to quantify the phenomenon, largely because the concept of identity is notoriously difficult to measure. We introduce here a methodology, Longitudinal Online Profile Sampling (LOPS), which affords quantifiable insights into the way individuals amend their identity over time. Using this method, we analyze millions of “bios” on the microblogging site Twitter over a 4-year span, and conclude that the average American user is increasingly integrating politics into their social identity. Americans on the site are adding political words to their bios at a higher rate than any other category of words we measured, and are now more likely to describe themselves by their political affiliation than their religious affiliation. The data suggest that this is due to both cohort and individual-level effects.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2021.0002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Estimating Multiple Socioeconomic Attributes via Home Location—A Case Study in China 基于家庭区位的多元社会经济属性估计——以中国为例

社会计算(英文) Pub Date : 2021-02-16 DOI: 10.23919/JSC.2021.0003

Shichang Ding;Xin Gao;Yufan Dong;Yiwei Tong;Xiaoming Fu

{"title":"Estimating Multiple Socioeconomic Attributes via Home Location—A Case Study in China","authors":"Shichang Ding;Xin Gao;Yufan Dong;Yiwei Tong;Xiaoming Fu","doi":"10.23919/JSC.2021.0003","DOIUrl":"https://doi.org/10.23919/JSC.2021.0003","url":null,"abstract":"Inferring people's Socioeconomic Attributes (SEAs), including income, occupation, and education level, is an important problem for both social sciences and many networked applications like targeted advertising and personalized recommendation. Previous works mainly focus on estimating SEAs from peoples' cyberspace behaviors and relationships, such as the content of tweets or the social networks between online users. Besides cyberspace data, alternative data sources about users' physical behavior, like their home location, may offer new insights. More specifically, in this paper, we study how to predict a person's income level, family income level, occupation type, and education level from his/her home location. As a case study, we collect people's home locations and socioeconomic attributes through a survey involving 9 provinces and 85 cities in China. We further enrich home location with the knowledge from real estate websites, government statistics websites, online map services, etc. To learn a shared representation from input features as well as attribute-specific representations for different SEAs, we propose H2SEA, a factorization machine-based multi-task learning method with attention mechanism. Extensive experiment results show that: (1) Home location can clearly improve the estimation accuracy for all SEA prediction tasks (e.g., 80.2% improvement in terms of F1-score in estimating personal income level); (2) The proposed H2SEA model outperforms alternative models for SEA inference in terms of various evaluation metrics, such as Area Under Curve (AUC), F-measure, and specificity; (3) The performance of specific SEA prediction tasks (e.g., personal income) can be further improved if H2SEA only focuses on cities or villages due to urban-rural gap in China; (4) Compared with online crawled housing price data, the area-level average income and Points Of Interest (POI) are more important features for SEA inferences in China.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 1","pages":"71-88"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2021.0003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50349903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2