TY - GEN

T1 - The Boundaries of Context

T2 - 5th Networks in the Global World Conference, NetGloW 2020

AU - Bodrunova, Svetlana S.

N1 - Publisher Copyright: © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - Today’s studies of networked discussions may be divided into theory-driven and data-driven, but both lines of research neglect the role of contextual knowledge in assessment of real-world public discourse. As scholars note, without context, data lose meaning and value; however, there is a striking vacuum of scholarly discussion on how to delineate the relevant context for network discussion studies, as well as what procedures of its description in academic publications should be employed. As a mediator between theories and data-driven results, context has a potential of eliminating the opposition between theory- and data-driven research designs. In an attempt to conceptualize context, we suggest to adapt the long-term experience of cognitive linguistics and critical discourse analysis for developing rigorous procedures of selection, assessment, and explicit description of relevant context(s). We bring attention to the paradox that, in online discussion studies, scholars extract sociologically relevant conclusions from the data of non-sociological nature (that is, either text or network structures), and argue it might be fruitful for selection of appropriate contextual background. After meta-reviewing the conceptual papers on online discussion research and using our own experience in such studies of over 7 years, we suggest three types of contexts for network discussions: cognitive, platform-technological, and media/communicative contexts – that need to be taken into account in network discussion studies.

AB - Today’s studies of networked discussions may be divided into theory-driven and data-driven, but both lines of research neglect the role of contextual knowledge in assessment of real-world public discourse. As scholars note, without context, data lose meaning and value; however, there is a striking vacuum of scholarly discussion on how to delineate the relevant context for network discussion studies, as well as what procedures of its description in academic publications should be employed. As a mediator between theories and data-driven results, context has a potential of eliminating the opposition between theory- and data-driven research designs. In an attempt to conceptualize context, we suggest to adapt the long-term experience of cognitive linguistics and critical discourse analysis for developing rigorous procedures of selection, assessment, and explicit description of relevant context(s). We bring attention to the paradox that, in online discussion studies, scholars extract sociologically relevant conclusions from the data of non-sociological nature (that is, either text or network structures), and argue it might be fruitful for selection of appropriate contextual background. After meta-reviewing the conceptual papers on online discussion research and using our own experience in such studies of over 7 years, we suggest three types of contexts for network discussions: cognitive, platform-technological, and media/communicative contexts – that need to be taken into account in network discussion studies.

KW - Cognitive context

KW - Communicative context

KW - Context

KW - Data-driven science

KW - Networked discussions

KW - Research design

KW - Social networks

KW - Technological context

UR - http://www.scopus.com/inward/record.url?scp=85102650431&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/c2a85f07-0608-333d-8360-5432e120865c/

U2 - 10.1007/978-3-030-64877-0_11

DO - 10.1007/978-3-030-64877-0_11

M3 - Conference contribution

AN - SCOPUS:85102650431

SN - 9783030648763

T3 - Lecture Notes in Networks and Systems

SP - 165

EP - 179

BT - Networks in the Global World V - Proceedings of NetGloW 2020

A2 - Antonyuk, Artem

A2 - Basov, Nikita

PB - Springer Nature

Y2 - 7 July 2020 through 9 July 2020

ER -