Making sense of self-reported socially significant data using computational methods

被引:10
|
作者
Burnap, Peter [1 ]
Avis, Nick J. [1 ]
Rana, Omer F. [1 ]
机构
[1] Cardiff Univ, Cardiff Sch Comp Sci & Informat, Cardiff CF10 3AX, S Glam, Wales
关键词
COSMOS; social media data; computational methods; empirical crisis; CRISIS; GENDER;
D O I
10.1080/13645579.2013.774174
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
The growing number of people using social media to communicate with their peers and document their personal everyday feelings and views is creating a data on an epic scale' that provides the opportunity for social scientists to conduct research such as ethnography, discourse and content analysis of social interactions, providing an additional insight into today's society. However, the tools and methods required to conduct such analysis are often isolated and/or proprietary. The Cardiff Online Social Media Observatory (COSMOS) provides an integrated virtual research environment for supporting the collection, analysis, and visualization of social media data, providing researchers with an innovative facility on which to conduct hypothetical experiments that lead to defensible results. This study presents a methodology for Digital Social Research and explains how the features of COSMOS aim to underpin it.
引用
收藏
页码:215 / 230
页数:16
相关论文
共 50 条
  • [21] Using Timer Data to Conjunct Self-Reported Measures in Quantifying Deception
    Caramancion, Kevin Matthe
    [J]. 2022 IEEE WORLD AI IOT CONGRESS (AIIOT), 2022, : 65 - 70
  • [22] Feasibility of using self-reported patient data in a national diabetes register
    Karianne Fjeld Løvaas
    John G. Cooper
    Sverre Sandberg
    Thomas Røraas
    Geir Thue
    [J]. BMC Health Services Research, 15
  • [23] Predicting cost of care using self-reported health status data
    Christy K. Boscardin
    Ralph Gonzales
    Kent L. Bradley
    Maria C. Raven
    [J]. BMC Health Services Research, 15
  • [24] Regularity in daily mood stabilizer dosage using self-reported data
    Bauer, M.
    Glenn, T.
    Whybrow, P. C.
    [J]. BIPOLAR DISORDERS, 2013, 15 : 95 - 95
  • [25] Measurement Reactivity in a Randomized Clinical Trial Using Self-Reported Data
    Capellan, Jahaira
    Wilde, Mary H.
    Zhang, Feng
    [J]. JOURNAL OF NURSING SCHOLARSHIP, 2017, 49 (01) : 111 - 119
  • [26] Making sense of self-reported practice impacts after online dementia education: the example of Bedtime to Breakfast and Beyond
    Goodenough, Belinda
    Watts, Jacqueline
    Bartlett, Sarah
    [J]. BRAIN IMPAIRMENT, 2020, 21 (03) : 299 - 313
  • [27] Transformer transfer learning emotion detection model: synchronizing socially agreed and self-reported emotions in big data
    Lee, Sanghyub John
    Lim, JongYoon
    Paas, Leo
    Ahn, Ho Seok
    [J]. NEURAL COMPUTING & APPLICATIONS, 2023, 35 (15): : 10945 - 10956
  • [28] INDIVIDUAL CIGARETTE USAGE - SELF-REPORTED DATA AS A FUNCTION OF RESPONDENT REPORTED DATA
    MCMAHAN, CA
    RICHARDS, ML
    STRONG, JP
    [J]. ATHEROSCLEROSIS, 1976, 23 (03) : 477 - 488
  • [29] Improving energy benchmarking with self-reported data
    Hsu, David
    [J]. BUILDING RESEARCH AND INFORMATION, 2014, 42 (05): : 641 - 656
  • [30] Validity of self-reported hyperthyroidism and hypothyroidism:: Comparison of self-reported questionnaire data with medical record review
    Brix, TH
    Kyvik, KO
    Hegedüs, L
    [J]. THYROID, 2001, 11 (08) : 769 - 773