Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr

被引:298
作者
Li, Linna [1 ]
Goodchild, Michael F. [1 ]
Xu, Bo [2 ]
机构
[1] Univ Calif Santa Barbara, Dept Geog, Ctr Spatial Studies, Santa Barbara, CA 93106 USA
[2] Calif State Univ San Bernardino, Dept Geog & Environm Studies, San Bernardino, CA 92407 USA
基金
美国国家科学基金会;
关键词
spatio-temporal footprints; socioeconomic; Flickr; Twitter; georeference; INFORMATION;
D O I
10.1080/15230406.2013.777139
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
Online social networking and information sharing services have generated large volumes of spatio-temporal footprints, which are potentially a valuable source of knowledge about the physical environment and social phenomena. However, it is critical to take into consideration the uneven distribution of the data generated in social media in order to understand the nature of such data and to use them appropriately. The distribution of footprints and the characteristics of contributors indicate the quantity, quality, and type of the data. Using georeferenced tweets and photos collected from Twitter and Flickr, this research presents the spatial and temporal patterns of such crowd-sourced geographic data in the contiguous United States and explores the socioeconomic characteristics of geographic data creators by investigating the relationships between tweet and photo densities and the characteristics of local people using California as a case study. Correlations between dependent and independent variables in partial least squares regression suggest that well-educated people in the occupations of management, business, science, and arts are more likely to be involved in the generation of georeferenced tweets and photos. Further research is required to explain why some people tend to produce and spread information over the Internet using social media from the perspectives of psychology and sociology. This study would be informative to sociologists who study the behaviors of social media users, geographers who are interested in the spatial and temporal distribution of social media users, marketing agencies who intend to understand the influence of social media, and other scientists who use social media data in their research.
引用
收藏
页码:61 / 77
页数:17
相关论文
共 33 条
  • [1] Alampay Erwin, 2006, Electronic Journal on Information Systems in Developing Countries, V27, P1
  • [2] Ames M, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P971
  • [3] [Anonymous], 2009, P 18 INT C WORLD WID
  • [4] [Anonymous], 1995, Interactive spatial data analysis
  • [5] [Anonymous], 2011, P INT AAAI C WEB SOC, DOI DOI 10.1609/ICWSM.V5I1.14109
  • [6] Serglycin-deficient cytotoxic T lymphocytes display defective secretory granule maturation and granzyme B storage
    Grujic, M
    Braga, T
    Lukinius, A
    Eloranta, ML
    Knight, SD
    Pejler, G
    Åbrink, M
    [J]. JOURNAL OF BIOLOGICAL CHEMISTRY, 2005, 280 (39) : 33411 - 33418
  • [7] Antoniou V., 2010, Geomatica, V64, P99, DOI DOI 10.HTTP://DX.D0I.0RG/10.5623/GE0MAT-2010-0009
  • [8] Twitter mood predicts the stock market
    Bollen, Johan
    Mao, Huina
    Zeng, Xiaojun
    [J]. JOURNAL OF COMPUTATIONAL SCIENCE, 2011, 2 (01) : 1 - 8
  • [9] Inferring social ties from geographic coincidences
    Crandall, David J.
    Backstrom, Lars
    Cosley, Dan
    Suri, Siddharth
    Huttenlocher, Daniel
    Kleinberg, Jon
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2010, 107 (52) : 22436 - 22441
  • [10] AN INTERPRETATION OF PARTIAL LEAST-SQUARES
    GARTHWAITE, PH
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1994, 89 (425) : 122 - 127