TY - GEN
T1 - Methodology to Compare Twitter Reaction Trends between Disinformation Communities, to COVID related Campaign Events at Different Geospatial Granularities
AU - De, Debraj
AU - Thakur, Gautam
AU - Herrmannova, Drahomira
AU - Christopher, Carter
N1 - Publisher Copyright:
© 2022 Public Domain.
PY - 2022/4/25
Y1 - 2022/4/25
N2 - With still ongoing COVID pandemic, there is an immediate need for a deeper understanding of how Twitter discussions (or chatters) in disinformation spreading communities get triggered. More specifically, the value is in monitoring how such trigger events in Twitter discussion do align with the timelines of relevant influencing events in the society (indicated in this work as campaign events). For campaign events in regards to COVID pandemic, we consider both NPI (Nonpharmaceutical Interventions) campaigns and disinformation spreading campaigns together. In this short paper we have presented a novel methodology to quantify, compare and relate two Twitter disinformation communities, in terms of their reaction patterns to the timelines of major campaign events. We have also analyzed these campaigns at their three geospatial granularity contexts: local county, state, and country/ federal. We have conducted a novel dataset collection on campaigns (NPI + Disinformation) at these different geospatial granularities. Then, with collected dataset on Twitter disinformation communities, we have performed a case study to validate our proposed methodology.
AB - With still ongoing COVID pandemic, there is an immediate need for a deeper understanding of how Twitter discussions (or chatters) in disinformation spreading communities get triggered. More specifically, the value is in monitoring how such trigger events in Twitter discussion do align with the timelines of relevant influencing events in the society (indicated in this work as campaign events). For campaign events in regards to COVID pandemic, we consider both NPI (Nonpharmaceutical Interventions) campaigns and disinformation spreading campaigns together. In this short paper we have presented a novel methodology to quantify, compare and relate two Twitter disinformation communities, in terms of their reaction patterns to the timelines of major campaign events. We have also analyzed these campaigns at their three geospatial granularity contexts: local county, state, and country/ federal. We have conducted a novel dataset collection on campaigns (NPI + Disinformation) at these different geospatial granularities. Then, with collected dataset on Twitter disinformation communities, we have performed a case study to validate our proposed methodology.
KW - COVID-19 pandemic
KW - Disinformation
KW - NPI (Nonpharmaceutical Interventions)
KW - Twitter data
UR - http://www.scopus.com/inward/record.url?scp=85137476213&partnerID=8YFLogxK
U2 - 10.1145/3487553.3524626
DO - 10.1145/3487553.3524626
M3 - Conference contribution
AN - SCOPUS:85137476213
T3 - WWW 2022 - Companion Proceedings of the Web Conference 2022
SP - 458
EP - 463
BT - WWW 2022 - Companion Proceedings of the Web Conference 2022
PB - Association for Computing Machinery, Inc
T2 - 31st ACM Web Conference, WWW 2022
Y2 - 25 April 2022
ER -