Information disclosure during online political activities can place participants under the threat of personal data leakage and misuse, but privacy protection in the context of online political participation has rarely been studied. This study examined how online political participation is related to privacy protection behaviours. Using survey data of internet users from 10 Asian societies, our study suggests two important findings. First, online political participation was found to be positively related to privacy protection behaviours. Second, we examined whether such a positive association can be explained by two mediators: perceived privacy risk and internet efficacy, in countries of different cybersecurity capacity. Our data suggest that internet efficacy mediates the relationship between online political participation and privacy protection behaviours across countries with different levels of cybersecurity capacity, while perceived privacy risk only mediates the effects of online political participation on privacy protection behaviours in countries of low cybersecurity capacity.

Temporal knowledge graphs (TKGs) have become useful resources for numerous Artificial Intelligence applications, but they are far from completeness. Inferring missing events in temporal knowledge graphs is a fundamental and challenging task. However, most existing methods solely focus on entity features or consider the entities and relations in a disjoint manner. They do not integrate the features of entities and relations in their modeling process. In this paper, we propose TPmod, a tendency-guided prediction model, to predict the missing events for TKGs (extrapolation). Differing from existing works, we propose two definitions for TKGs: the Goodness of relations and the Closeness of entity pairs. More importantly, inspired by the attention mechanism, we propose a novel tendency strategy to guide our aggregated process. It integrates the features of entities and relations, and assigns varying weights to different past events. What is more, we select the Gate Recurrent Unit (GRU) as our sequential encoder to model the temporal dependency in TKGs. Besides, the Softmax function is employed to generate the final decreasing group of candidate entities. We evaluate our model on two TKG datasets: GDELT-5 and ICEWS-250. Experimental results show that our method has a significant and consistent improvement compared to state-of-the-art baselines.