TY - GEN

T1 - Global Agendas

T2 - 13th International Conference on Social Computing and Social Media, SCSM 2021, held as part of the 23rd International Conference, HCI International 2021

AU - Bodrunova, Svetlana S.

AU - Blekanov, Ivan S.

AU - Tarasov, Nikita

N1 - Publisher Copyright: © 2021, Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - Agendas in online media have become a scholarly focus nearly two decades ago, leading to shifting conceptualizations of what we see as agenda. Thus, agendas and agenda shifts inside online discussions have shown its potential to influence offline deliberation, aggregate support, fuel protest, passing through and/or bypassing traditional media’s gatekeeping. Real-time (or nearly-real-time) learning about quick agenda movement inside globalized public debate might be particularly important for international organizations like UN or EU. However, we today lack both knowledge on how agendas move in such discussions and instruments on such analysis. In particular, we are next-to-unaware of to what extent globally relevant themes get contextualized within language-based discussion segments, as well as to what extent the latter depend on each other and lag behind each other in developing agendas and public opinion on quickly evolving issues or conflicts. In this paper, we propose a method of agenda detection based on neural-network text summarization and compare summaries of tweet packages across three languages within the Twitter hashtag #jesuischarlie. We show that sentiment detection may allow for quality assessment of the text summaries, as compared to aggregated sentiment to the original tweets. We show that, outside France, agendas were more interpretational, abstract, and non-contextualized. The pattern of news changing to ‘issue outburt’ was simultaneous in dense discussion segments and lagged behind in a sparser one. We also show that, globally, main issues of the discussion may be spotted within the first hour.

AB - Agendas in online media have become a scholarly focus nearly two decades ago, leading to shifting conceptualizations of what we see as agenda. Thus, agendas and agenda shifts inside online discussions have shown its potential to influence offline deliberation, aggregate support, fuel protest, passing through and/or bypassing traditional media’s gatekeeping. Real-time (or nearly-real-time) learning about quick agenda movement inside globalized public debate might be particularly important for international organizations like UN or EU. However, we today lack both knowledge on how agendas move in such discussions and instruments on such analysis. In particular, we are next-to-unaware of to what extent globally relevant themes get contextualized within language-based discussion segments, as well as to what extent the latter depend on each other and lag behind each other in developing agendas and public opinion on quickly evolving issues or conflicts. In this paper, we propose a method of agenda detection based on neural-network text summarization and compare summaries of tweet packages across three languages within the Twitter hashtag #jesuischarlie. We show that sentiment detection may allow for quality assessment of the text summaries, as compared to aggregated sentiment to the original tweets. We show that, outside France, agendas were more interpretational, abstract, and non-contextualized. The pattern of news changing to ‘issue outburt’ was simultaneous in dense discussion segments and lagged behind in a sparser one. We also show that, globally, main issues of the discussion may be spotted within the first hour.

KW - Agenda

KW - Charlie hebdo

KW - Cross-national agendas

KW - Longformer

KW - Neural networks

KW - Text summarization

KW - Topic detection

KW - Twitter

UR - http://www.scopus.com/inward/record.url?scp=85112229170&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/9fc02cc1-bfbe-3cf4-a5a5-0c821ddc21a6/

U2 - 10.1007/978-3-030-77626-8_15

DO - 10.1007/978-3-030-77626-8_15

M3 - Conference contribution

AN - SCOPUS:85112229170

SN - 9783030776251

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 221

EP - 239

BT - Social Computing and Social Media

A2 - Meiselwitz, Gabriele

PB - Springer Nature

Y2 - 24 July 2021 through 29 July 2021

ER -