Social-Media Activity Forecasting with Exogenous Information Signals

• URL: http://arxiv.org/abs/2109.11024v1
• Date: Wed, 22 Sep 2021 20:24:20 GMT
• Title: Social-Media Activity Forecasting with Exogenous Information Signals
• Authors: Kin Wai Ng, Sameera Horawalavithana, and Adriana Iamnitchi
• Abstract summary: Social media platforms present an ideal environment for studying and understanding social behavior. We propose a modeling technique that forecasts topic-specific daily volume of social media activities.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to their widespread adoption, social media platforms present an ideal environment for studying and understanding social behavior, especially on information spread. Modeling social media activity has numerous practical implications such as supporting efforts to 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 using both exogenous signals, such as news or armed conflicts records, and endogenous data from the social media platform we model. Empirical evaluations with real datasets from two different platforms and two different contexts each composed of multiple interrelated topics demonstrate the effectiveness of our solution.

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