PMID- 33969290
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210513
IS  - 2624-909X (Electronic)
IS  - 2624-909X (Linking)
VI  - 4
DP  - 2021
TI  - Data-Driven Computational Social Network Science: Predictive and Inferential 
      Models for Web-Enabled Scientific Discoveries.
PG  - 591749
LID - 10.3389/fdata.2021.591749 [doi]
LID - 591749
AB  - The ultimate goal of the social sciences is to find a general social theory 
      encompassing all aspects of social and collective phenomena. The traditional 
      approach to this is very stringent by trying to find causal explanations and 
      models. However, this approach has been recently criticized for preventing 
      progress due to neglecting prediction abilities of models that support more 
      problem-oriented approaches. The latter models would be enabled by the surge of 
      big Web-data currently available. Interestingly, this problem cannot be overcome 
      with methods from computational social science (CSS) alone because this field is 
      dominated by simulation-based approaches and descriptive models. In this article, 
      we address this issue and argue that the combination of big social data with 
      social networks is needed for creating prediction models. We will argue that this 
      alliance has the potential for gradually establishing a causal social theory. In 
      order to emphasize the importance of integrating big social data with social 
      networks, we call this approach data-driven computational social network science 
      (DD-CSNS).
CI  - Copyright (c) 2021 Emmert-Streib and Dehmer.
FAU - Emmert-Streib, Frank
AU  - Emmert-Streib F
AD  - Predictive Society and Data Analytics Lab, Faculty of Information Technology and 
      Communication Sciences, Tampere University, Tampere, Finland.
AD  - Institute of Biosciences and Medical Technology, Tampere, Finland.
FAU - Dehmer, Matthias
AU  - Dehmer M
AD  - Department of Computer Science, Swiss Distance University of Applied Sciences, 
      Brig, Switzerland.
AD  - School of Science, Xian Technological University, Xian, China.
AD  - College of Artificial Intelligence, Nankai University, Tianjin, China.
AD  - Department of Biomedical Computer Science and Mechatronics, The Health and Life 
      Science University, UMIT, Hall in Tyrol, Austria.
LA  - eng
PT  - Journal Article
DEP - 20210422
PL  - Switzerland
TA  - Front Big Data
JT  - Frontiers in big data
JID - 101770603
PMC - PMC8100320
OTO - NOTNLM
OT  - causal models
OT  - computational social science
OT  - data science
OT  - network science
OT  - prediction models
OT  - social data
OT  - social sciences
OT  - web experiments
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2021/05/11 06:00
MHDA- 2021/05/11 06:01
PMCR- 2021/04/22
CRDT- 2021/05/10 06:33
PHST- 2020/08/05 00:00 [received]
PHST- 2021/02/18 00:00 [accepted]
PHST- 2021/05/10 06:33 [entrez]
PHST- 2021/05/11 06:00 [pubmed]
PHST- 2021/05/11 06:01 [medline]
PHST- 2021/04/22 00:00 [pmc-release]
AID - 591749 [pii]
AID - 10.3389/fdata.2021.591749 [doi]
PST - epublish
SO  - Front Big Data. 2021 Apr 22;4:591749. doi: 10.3389/fdata.2021.591749. eCollection 
      2021.