TY - JOUR
T1 - Social media activity forecasting with exogenous and endogenous signals
AU - Ng, K.W.
AU - Horawalavithana, S.
AU - Iamnitchi, A.
N1 - Funding Information:
This work is supported by the DARPA SocialSim Program and the Air Force Research Laboratory under contract FA8650-18-C-7825. The authors would like to thank Leidos for providing data.
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Modeling social media activity has numerous practical implications such as in helping analyze strategic information operations, designing intervention techniques to mitigate disinformation, or delivering critical information during disaster relief operations. In this paper, we propose a modeling technique that forecasts topic-specific daily volume of social media activities by multiplexing different exogenous signals, such as news reports and armed conflicts records, and endogenous data from the social media platform we model. For this, we trained a collection of LSTM models, each leveraging a different exogenous source, and dynamically select one model for each topic. Empirical evaluations with real datasets from two social media platforms and two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.
AB - Modeling social media activity has numerous practical implications such as in helping analyze strategic information operations, designing intervention techniques to mitigate disinformation, or delivering critical information during disaster relief operations. In this paper, we propose a modeling technique that forecasts topic-specific daily volume of social media activities by multiplexing different exogenous signals, such as news reports and armed conflicts records, and endogenous data from the social media platform we model. For this, we trained a collection of LSTM models, each leveraging a different exogenous source, and dynamically select one model for each topic. Empirical evaluations with real datasets from two social media platforms and two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.
KW - Time series forecasting
KW - Social media
KW - Exogenous and endogenous signals
KW - TIME-SERIES
U2 - 10.1007/s13278-022-00927-3
DO - 10.1007/s13278-022-00927-3
M3 - Article
SN - 1869-5450
VL - 12
JO - Social Network Analysis and Mining
JF - Social Network Analysis and Mining
IS - 1
M1 - 102
ER -