News Event Prediction using Causality Approach on South China Sea Conflict

Abstract

South China Sea (SCS) generates huge economic value in fishing and shipping lane as well as a high amount of natural resources. Due to its strategic location and high revenue generated, SCS became a place where several nearby countries competed for its territorial claims. Famous territorial disputes such as Spratly islands, Paracel island, Scarborough Shoal happened due to claim on SCS wealth. Newspapers are the main medium that disseminate the message to the public and update whenever SCS conflict happens. News related to SCS events or conflicts usually contain causal relationships between cause and effect. This causal relationship can be extracted and analyzed to obtain the trends of events and conflicts that have happened. In order to avoid any inevitable conflict among countries in SCS region, event prediction is important as it gives a better insight and foresee future events that might happen. In this paper, phrase similarity is used as important metrics for prediction models. First, it extracts news articles based on causality connectors such as "because", "after", "lead to", etc. into <; cause, effect> tuple. Then, three different embedding techniques, Doc2vec, InferSent and BERT were evaluated based on their best similarity score. The selected embedding technique is used to construct the prediction model and predict South China Sea conflict related events. A crude prediction is done based on similarity of past causes. The result shows that BERT has the highest average accuracy of 50.85% in getting the most similar phrase. By using the causal prediction model, a future possible event can be predicted and this helps to increase the awareness of national security among SCS nearby countries.