Digital Social Research Tools, Tension Indicators and Safer Communities: a demonstration of the Cardiff Digital Research Platform (CDRP)

数字社会研究工具、紧张指标和更安全的社区:卡迪夫数字研究平台 (CDRP) 的演示

基本信息

  • 批准号:
    ES/J009903/1
  • 负责人:
  • 金额:
    $ 9.94万
  • 依托单位:
  • 依托单位国家:
    英国
  • 项目类别:
    Research Grant
  • 财政年份:
    2011
  • 资助国家:
    英国
  • 起止时间:
    2011 至 无数据
  • 项目状态:
    已结题

项目摘要

This demonstrator project will develop software tools for harvesting data from social media sites (such as facebook) focusing, in particular, on how such data can be automatically aggregated and stored for subsequent analysis. The demonstrator will make use of an existing API to ensure an early prototype is available to start the pilot study (in tension indicators/community cohesion indicators), followed by further refinement of the prototype alongside the Cardiff Digital Research Platform (CDRP -- described in appendix 1, figure 1 in the case for support). The tools implemented within the demonstrator are therefore part of the larger CDRP, intending to provide a software infrastructure for supporting data collection and analysis (covering both data analytics and visualisation) from real time on-line sources, along with traditional Web-based sources and expert curated data collections (provided and managed by government agencies). The tools will be validated in a "community cohesion" pilot study by focusing on particular social media sources. The tools being developed in the project are general in scope and can be utilised in other areas within social sciences and can be adapted to address other research questions. The project is also intended to demonstrate how social media data can be combined with data from official sources (such as the Office of National Statistics) to support social sciences research. The outcome of the pilot study is intended to highlight the benefits and limitations of using social media data and in particular how such an approach can translate to other areas of social sciences research where social media data (often of limited quality and "messy") can augment expert curated data (obtained through "traditional" questionnaire and field-based activities). The deliverables from the project will include software tools for harvesting data from social network sites and an online guide outlining the use of such tools within a pilot study, highlighting particular lessons learned from this process which could translate to other areas within social sciences and beyond (such as methodological, technical, ethical and privacy issues). In particular, the project will demonstrate how the CDRP enables the analysis of information fed through social media in 'real-time' (and, therefore, the rapid and multiple testing of propositions about the frequency, distribution and content of social media communications) whilst also archiving this data in ways that can be stored by the ESRC and subject to secondary data analysis by subsequent users (see also, the 'data management plan' for the project). This demonstrator also provides the basis for undertaking further study on issues of: (i) data quality arising from the use of social media data and (ii) privacy and user engagement in supporting social media data analytics and trending.
这一示范项目将开发从社交媒体网站(如Facebook)收集数据的软件工具,特别侧重于如何自动汇总和存储这些数据以供后续分析。演示者将利用现有的API来确保早期原型可用于启动试点研究(在紧张指标/社区凝聚力指标中),随后与加的夫数字研究平台一起对原型进行进一步改进(CDRP--在案例中的附录1中描述,图1用于支持)。因此,在演示器内实施的工具是更大的CDRP的一部分,目的是提供一个软件基础设施,以支持从实时在线来源以及传统的基于网络的来源和专家管理的数据收集(由政府机构提供和管理)收集和分析数据(包括数据分析和可视化)。这些工具将在“社区凝聚力”试点研究中得到验证,重点放在特定的社交媒体来源上。该项目正在开发的工具范围一般,可用于社会科学的其他领域,也可用于解决其他研究问题。该项目还旨在展示如何将社交媒体数据与官方来源(如国家统计局)的数据结合起来,以支持社会科学研究。试点研究的结果旨在强调使用社交媒体数据的好处和局限性,特别是这种方法如何适用于社会科学研究的其他领域,在这些领域,社交媒体数据(通常质量有限和“杂乱无章”)可以补充专家整理的数据(通过“传统”问卷和实地活动获得)。该项目的成果将包括从社交网站收集数据的软件工具,以及概述在试点研究中使用这类工具的在线指南,强调从这一过程中吸取的具体经验教训,这些经验教训可推广到社会科学内外的其他领域(如方法、技术、伦理和隐私问题)。特别是,该项目将展示CDRP如何能够“实时”地分析通过社交媒体馈送的信息(因此,对有关社交媒体通信的频率、分布和内容的主张进行快速和多次测试),同时还以ESRC可以存储的方式将这些数据存档,并由后续用户进行二次数据分析(另见项目的“数据管理计划”)。该演示还为进一步研究以下问题提供了基础:(I)使用社交媒体数据产生的数据质量,以及(Ii)隐私和用户参与支持社交媒体数据分析和趋势。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Using Social Media with Survey Data
将社交媒体与调查数据结合使用
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Luke Sloan (Author)
  • 通讯作者:
    Luke Sloan (Author)
COSMOS: Towards an integrated and scalable service for analysing social media on demand
Scaling Archived Social Media Data Analysis Using a Hadoop Cloud
Social Media Analysis, Twitter and the London Olympics 2012
社交媒体分析、Twitter 和 2012 年伦敦奥运会
  • DOI:
    10.4135/978144627305013517477
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Burnap P
  • 通讯作者:
    Burnap P
Twitter Analytics
推特分析
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jeffrey Morgan (Author)
  • 通讯作者:
    Jeffrey Morgan (Author)
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Matthew Williams其他文献

First observation of the decays B(0) → D(+)K(-)π(+)π(-) and B(-) → D(0)K(-)π(+)π(-).
首次观察到衰变 B(0) → D(+)K(-)π(+)π(-) 和 B(-) → D(0)K(-)π(+)π(-)。
  • DOI:
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    8.6
  • 作者:
    R. Aaij;C. Beteta;B. Adeva;M. Adinolfi;C. Adrover;A. Affolder;Z. Ajaltouni;J. Albrecht;F. Alessio;M. Alexander;G. Alkhazov;P. Cartelle;A. A. Alves;S. Amato;Y. Amhis;J. Anderson;R. Appleby;O. A. Gutiérrez;F. Archilli;L. Arrabito;A. Artamonov;Marina Artuso;E. Aslanides;G. Auriemma;S. Bachmann;J. Back;D. Bailey;V. Balagura;W. Baldini;R. Barlow;C. Barschel;S. Barsuk;W. Barter;A. Bates;C. Bauer;T. Bauer;A. Bay;I. Bediaga;S. Belogurov;K. Belous;I. Belyaev;E. Ben;M. Benayoun;G. Bencivenni;S. Benson;J. Benton;R. Bernet;M. Bettler;M. Beuzekom;A. Bieñ;S. Bifani;T. Bird;A. Bizzeti;P. ornstad;Thomas Blake;F. Blanc;C. Blanks;J. Blouw;S. Blusk;A. Bobrov;Valerio Bocci;A. Bondar;N. Bondar;W. Bonivento;S. Borghi;A. Borgia;T. Bowcock;C. Bozzi;T. Brambach;J. Brand;J. Bressieux;D. Brett;M. Britsch;T. Britton;N. Brook;H. Brown;A. Büchler;I. Burducea;A. Bursche;J. Buytaert;S. Cadeddu;O. Callot;M. Calvi;M. C. Gomez;Alessandro Camboni;Pierluigi Campana;Angelo Carbone;G. Carboni;R. Cardinale;A. Cardini;L. Carson;K. C. Akiba;G. Casse;Marco Cattaneo;C. Cauet;M. Charles;P. Charpentier;N. Chiapolini;K. Ciba;X. Vidal;G. Ciezarek;P. Clarke;M. Clemencic;H. Cliff;J. Closier;C. Coca;V. Coco;J. Cogan;P. Collins;A. Comerma;F. Constantin;A. Contu;A. Cook;M. Coombes;G. Corti;G. Cowan;R. Currie;Carmelo D'Ambrosio;P. David;P. David;I. Bonis;S. Capua;M. Cian;F. D. Lorenzi;J. M. D. Miranda;L. D. Paula;P. D. Simone;D. Decamp;M. Deckenhoff;H. Degaudenzi;L. D. Buono;C. Deplano;D. Derkach;O. Deschamps;Francesco Dettori;J. Dickens;H. Dijkstra;P. D. Batista;F. Bonal;S. Donleavy;F. Dordei;Á. Suárez;D. Dossett;A. Dovbnya;F. Dupertuis;R. Dzhelyadin;A. Dziurda;S. Easo;U. Egede;V. Egorychev;S. Eidelman;D. Eijk;F. Eisele;S. Eisenhardt;R. Ekelhof;Lars Eklund;C. Elsasser;D. Elsby;D. E. Pereira;L. Estève;A. Falabella;E. Fanchini;C. Färber;G. Fardell;C. Farinelli;S. Farry;V. Fave;V. Albor;M. Ferro;Sergey Filippov;C. Fitzpatrick;M. Fontana;F. Fontanelli;R. Forty;M. Frank;C. Frei;M. Frosini;S. Furcas;A. G. Torreira;Domenico Galli;M. Gandelman;P. Gandini;Y. Gao;J. Garnier;J. Garofoli;J. G. Tico;L. Garrido;D. Gascón;C. Gaspar;N. Gauvin;M. Gersabeck;T. Gershon;P. Ghez;V. Gibson;V. Gligorov;C. Göbel;D. Golubkov;A. Golutvin;A. Gomes;H. Gordon;M. Gándara;R. G. Díaz;L. A. Cardoso;E. Grauges;G. Graziani;A. Grecu;E. Greening;S. Gregson;B. Gui;E. Gushchin;Y. Guz;T. Gys;G. Haefeli;C. Haen;S. Haines;T. Hampson;S. Hansmann;R. Harji;N. Harnew;J. Harrison;P. Harrison;T. Hartmann;J. He;V. Heijne;K. Hennessy;P. Henrard;J. Morata;E. V. Herwijnen;E. Hicks;K. Holubyev;P. Hopchev;W. Hulsbergen;P. Hunt;T. Huse;R. S. Huston;D. Hutchcroft;D. Hynds;V. Iakovenko;P. Ilten;J. Imong;R. Jacobsson;A. Jaeger;Marwa Jahjah;E. Jans;F. Jansen;P. Jaton;B. Jean;F. Jing;M. John;D. Johnson;C. Jones;B. Jost;M. Kaballo;S. Kandybei;M. Karacson;T. M. Karbach;J. Keaveney;I. Kenyon;U. Kerzel;T. Ketel;A. Keune;B. Khanji;Y. M. Kim;M. Knecht;R. Koopman;P. Koppenburg;A. Kozlinskiy;L. Kravchuk;K. Kreplin;M. Kreps;G. Krocker;P. Krokovny;Florian Kruse;K. Kruzelecki;M. Kucharczyk;T. Kvaratskheliya;V. Thi;D. Lacarrere;G. Lafferty;A. Lai;D. Lambert;R. Lambert;E. Lanciotti;G. Lanfranchi;C. Langenbruch;T. Latham;C. Lazzeroni;R. Gac;J. Leerdam;J. Lees;R. Lefèvre;A. Leflat;J. Lefrancois;O. Leroy;T. Lesiak;L. Li;L. L. Gioi;M. Lieng;M. Liles;R. Lindner;C. Linn;B. Liu;G. Liu;J. Loeben;J. Lopes;E. Asamar;N. López;H. Lu;J. Luisier;A. Raighne;F. Machefert;I. Machikhiliyan;F. Maciuc;O. Maev;J. Magnin;S. Malde;R. Mamunur;G. Manca;G. Mancinelli;N. Mangiafave;U. Marconi;R. Märki;J. Marks;G. Martellotti;A. Martens;L. Martin;A. M. Sanchez;D. Santos;A. Massafferri;Z. Máthé;C. Matteuzzi;M. Matveev;E. Maurice;B. Maynard;A. Mazurov;G. McGregor;R. McNulty;M. Meissner;M. Merk;J. Merkel;R. Messi;S. Miglioranzi;D. Milanes;M. Minard;J. M. Rodriguez;S. Monteil;D. Moran;P. Morawski;R. Mountain;I. Mous;F. Muheim;K. Müller;R. Mureşan;B. Muryn;B. Muster;M. Musy;J. Mylroie;P. Naik;T. Nakada;R. Nandakumar;I. Nasteva;M. Nedos;M. Needham;N. Neufeld;C. Nguyen;M. Nicol;V. Niess;N. Nikitin;A. Nomerotski;A. Novoselov;A. Oblakowska;V. Obraztsov;S. Oggero;S. Ogilvy;O. Okhrimenko;R. Oldeman;M. Orlandea;J. Goicochea;P. Owen;K. Pal;J. Palacios;A. Palano;M. Palutan;J. Panman;A. Papanestis;M. Pappagallo;C. Parkes;C. Parkinson;G. Passaleva;G. Patel;M. Patel;S. Paterson;G. Patrick;C. Patrignani;C. Pavel;A. Alvarez;Antonio Pellegrino;G. Penso;M. Altarelli;S. Perazzini;D. Perego;E. Trigo;A. Yzquierdo;P. Perret;M. Perrin;G. Pessina;A. Petrella;A. Petrolini;A. Phan;E. Olloqui;B. P. Valls;B. Pietrzyk;T. Pilař;D. Pinci;R. Plackett;S. Playfer;M. P. Casasus;G. Polok;A. Poluektov;E. Polycarpo;D. Popov;B. Popovici;C. Potterat;A. Powell;J. Prisciandaro;V. Pugatch;A. P. Navarro;W. Qian;J. Rademacker;B. Rakotomiaramanana;M. Rangel;I. Raniuk;G. Raven;S. Redford;M. Reid;A. C. D. Reis;S. Ricciardi;K. Rinnert;D. A. Romero;P. Robbe;E. Rodrigues;F. Rodrigues;P. R. Pérez;G. Rogers;S. Roiser;V. Romanovsky;M. Roselló;J. Rouvinet;T. Ruf;H. Ruiz;G. Sabatino;J. J. S. Silva;N. Sagidova;P. Sail;B. Saitta;C. Salzmann;M. Sannino;R. Santacesaria;C. Rios;R. Santinelli;E. Santovetti;M. Sapunov;A. Sarti;C. Satriano;A. Satta;M. Savrié;D. Savrina;P. Schaack;M. Schiller;S. Schleich;M. Schlupp;M. Schmelling;B. Schmidt;O. Schneider;A. Schopper;M. Schune;R. Schwemmer;B. Sciascia;A. Sciubba;M. Seco;A. Semennikov;K. Senderowska;I. Sepp;N. Serra;J. Serrano;P. Seyfert;M. Shapkin;I. Shapoval;P. Shatalov;Yu . A. Shcheglov;T. Shears;L. Shekhtman;O. Shevchenko;V. Shevchenko;A. Shires;R. S. Coutinho;Tomasz Skwarnicki;A. Smith;N. Smith;E. Smith;K. Sobczak;F. Soler;A. Solomin;F. Soomro;B. S. D. Paula;B. Spaan;A. Sparkes;P. Spradlin;F. Stagni;S. Stahl;O. Steinkamp;S. Stoica;S. Stone;B. Storaci;M. Straticiuc;U. Straumann;V. Subbiah;S. Swientek;M. Szczekowski;P. Szczypka;T. Szumlak;S. T'Jampens;E. Teodorescu;F. Teubert;C. Thomas;E. Thomas;J. V. Tilburg;V. Tisserand;M. Tobin;S. Topp;N. Torr;E. Tournefier;M. Tran;A. Tsaregorodtsev;N. Tuning;M. Garcia;A. Ukleja;P. Urquijo;U. Uwer;V. Vagnoni;G. Valenti;R. V. Gomez;P. V. Regueiro;S. Vecchi;J. Velthuis;M. Veltri;B. Viaud;I. Videau;X. Vilasís;J. Visniakov;A. Vollhardt;D. Volyanskyy;D. Voong;A. Vorobyev;H. Voss;S. Wandernoth;J. C. Wang;D. R. Ward;N. Watson;A. Webber;D. Websdale;M. Whitehead;D. Wiedner;L. Wiggers;G. Wilkinson;Matthew Williams;M. Williams;F. Wilson;J. Wishahi;M. Witek;W. Witzeling;S. Wotton;K. Wyllie;Y. Xie;F. Xing;Z. Xing;Z. Yang;R. Young;O. Yushchenko;M. Zavertyaev;F. Zhang;L. Zhang;W. C. Zhang;Y. Zhang;A. Zhelezov;L. Zhong;E. G. Zverev;A. Zvyagin
  • 通讯作者:
    A. Zvyagin
Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer
用于乳腺癌系统建模和预后的客观贝叶斯网络
Ascent and emplacement dynamics of obsidian lavas inferred from microlite textures
从微晶石纹理推断黑曜石熔岩的上升和就位动态
  • DOI:
    10.1007/s00445-015-0971-6
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    K. Befus;M. Manga;J. Gardner;Matthew Williams
  • 通讯作者:
    Matthew Williams
Innovation in dementia education within undergraduate healthcare programmes: A scoping review.
本科医疗保健项目中痴呆症教育的创新:范围界定审查。
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    3.9
  • 作者:
    Matthew Williams;S. Daley
  • 通讯作者:
    S. Daley
Coming and going: A narrative review exploring the push-pull factors during nurses' careers
来来往往:一项探索护士职业生涯中推拉因素的叙事性综述
  • DOI:
    10.1016/j.ijnurstu.2024.104908
  • 发表时间:
    2024-12-01
  • 期刊:
  • 影响因子:
    7.100
  • 作者:
    Ourega-Zoé Ejebu;Julia Philippou;Joanne Turnbull;Anne Marie Rafferty;William Palmer;Jane Prichard;Iain Atherton;Michelle Jamieson;Lucina Rolewicz;Matthew Williams;Jane Ball
  • 通讯作者:
    Jane Ball

Matthew Williams的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Matthew Williams', 18)}}的其他基金

Stellar Archeology: The Nuclear Fingerprints of Massive Stars.
恒星考古学:大质量恒星的核指纹。
  • 批准号:
    ST/W00321X/1
  • 财政年份:
    2023
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Fellowship
Hate Crime After Brexit: Linking Terrestrial and New Forms of Data to Inform Governance
英国脱欧后的仇恨犯罪:将地面数据和新形式的数据联系起来为治理提供信息
  • 批准号:
    ES/S006168/1
  • 财政年份:
    2019
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Research Grant
Workforce Education: STEM Recruitment, Retention, and Realization
劳动力教育:STEM 招聘、保留和实现
  • 批准号:
    1741982
  • 财政年份:
    2018
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Standard Grant
Centre for Cyberhate Research & Policy: Real-Time Scalable Methods & Infrastructure for Modelling the Spread of Cyberhate on Social Media
网络仇恨研究中心
  • 批准号:
    ES/P010695/1
  • 财政年份:
    2017
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Research Grant
PostDoctoral Research Fellowship
博士后研究奖学金
  • 批准号:
    1204783
  • 财政年份:
    2012
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Fellowship Award

相似国自然基金

小型类人猿合唱节奏的功能假说——宣 示社会关系(Social bond advertising) ——验证研究
  • 批准号:
  • 批准年份:
    2025
  • 资助金额:
    10.0 万元
  • 项目类别:
    省市级项目
Behavioral Insights on Cooperation in Social Dilemmas
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    外国优秀青年学者研究基金项目
多语言环境下Social Tagging的内涵机理与应用框架研究-基于比较的视角
  • 批准号:
    71103203
  • 批准年份:
    2011
  • 资助金额:
    21.0 万元
  • 项目类别:
    青年科学基金项目

相似海外基金

Co-creating digital education about parenting and father-inclusive practice: combining QL impact research and commercialisation for the social good
共同创建有关育儿和父亲包容性实践的数字教育:将 QL 影响研究与商业化相结合,造福社会
  • 批准号:
    MR/Y00356X/1
  • 财政年份:
    2024
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Fellowship
Doctoral Dissertation Research: Global Social Movements and Community-Building in the Digital Age
博士论文研究:数字时代的全球社会运动和社区建设
  • 批准号:
    2342846
  • 财政年份:
    2024
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Social Media Co-Pilot: Enhancing Teens’ Digital Literacy and Cyber Safety Education with AI-based Conversational Intervention
合作研究:社交媒体副驾驶:通过基于人工智能的对话干预提高青少年的数字素养和网络安全教育
  • 批准号:
    2302976
  • 财政年份:
    2023
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Social Media Co-Pilot: Enhancing Teens’ Digital Literacy and Cyber Safety Education with AI-based Conversational Intervention
合作研究:社交媒体副驾驶:通过基于人工智能的对话干预提高青少年的数字素养和网络安全教育
  • 批准号:
    2302975
  • 财政年份:
    2023
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Standard Grant
Collaborative Research: Social Media Co-Pilot: Enhancing Teens’ Digital Literacy and Cyber Safety Education with AI-based Conversational Intervention
合作研究:社交媒体副驾驶:通过基于人工智能的对话干预提高青少年的数字素养和网络安全教育
  • 批准号:
    2302977
  • 财政年份:
    2023
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Standard Grant
Loneliness in the digital world: Co-developing smartphone-based research to examine how online social experiences impact adolescent mental health
数字世界中的孤独:共同开发基于智能手机的研究,以研究在线社交体验如何影响青少年心理健康
  • 批准号:
    MR/X002608/1
  • 财政年份:
    2022
  • 资助金额:
    $ 9.94万
  • 项目类别:
    Research Grant
SCH: INT: Collaborative Research: Passive sensing of social isolation: A digital phenotying approach
SCH:INT:协作研究:社会隔离的被动感知:数字表型方法
  • 批准号:
    9929244
  • 财政年份:
    2019
  • 资助金额:
    $ 9.94万
  • 项目类别:
SCH: INT: Collaborative Research: Passive sensing of social isolation: A digital phenotying approach
SCH:INT:协作研究:社会隔离的被动感知:数字表型方法
  • 批准号:
    10245222
  • 财政年份:
    2019
  • 资助金额:
    $ 9.94万
  • 项目类别:
SCH: INT: Collaborative Research: Passive sensing of social isolation: A digital phenotying approach
SCH:INT:协作研究:社会隔离的被动感知:数字表型方法
  • 批准号:
    10478269
  • 财政年份:
    2019
  • 资助金额:
    $ 9.94万
  • 项目类别:
SCH: INT: Collaborative Research: Passive sensing of social isolation: A digital phenotying approach
SCH:INT:协作研究:社会隔离的被动感知:数字表型方法
  • 批准号:
    10022338
  • 财政年份:
    2019
  • 资助金额:
    $ 9.94万
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了