近日，我协会成员撰写的论文《TKSP: Long-Term Stance Prediction for Social Media Users by Fusing Time Series Features and Event Dynamic Evolution Knowledge》在CCF-C级会议（KSEM 2023）上发表。第一作者为周子健（2020级网安本科生），合作作者为胡硕渝（2020级网安本科生）、杨凯（2020级网安本科生），指导老师为王海舟副教授。

作者提出了一种融合时间序列特征和事件动态演变知识用于社交媒体用户立场长期预测的模型框架TKSP。作者在该文章中首次构造了一个面向社交媒体用户立场长期预测问题的数据集，数据集包含了3个目标话题，涵盖了949个用户和58310条推文以及每条推文的时间信息。之后，作者利用BERT和TEXT-CNN模型实现用户每条推文的立场预测；同时提出一种反映用户在特定时间窗口内立场倾向程度的立场得分计算机制；除此之外，作者考虑到了事件动态演变对用户立场倾向程度的影响，提出了一种事件动态演变知识的挖掘方法用于提升立场预测效果；最后融合用户每个时间窗口的立场得分以及事件动态演变知识，在多层LSTM网络结构以及Attention机制的帮助下实现用户立场的长期预测。实验表明，TKSP相较于其他的时间序列预测模型有更好的预测效果，并且在减少训练数据和预测数据比例的情况下，TKSP的性能下降程度要明显低于其他预测模型。

会议简介：

KSEM全名The International Conference on Knowledge Science, Engineering and Management，是CCF-C级国际会议；主要研究的主题涉及知识科学、知识工程、知识管理、知识图谱等方面的内容。

官方介绍：The importance of studying knowledge science, knowledge engineering and knowledge management has been recognized widely. We could cite Feigenbaum’s words in his book The fifth generation, Artificial Intelligence and Japan’s Computer challenge to the World, for demonstrating the point: 

• “Knowledge is not the same as information. Knowledge is Information that has been pared, shaped, interpreted, selected, and transformed”
• “Knowledge is power, and the computer is an amplifier of that power. We are now at the dawn of a new computer revolution…the transition from information processing to knowledge processing”
• “…establish a ‘knowledge industry’ in which knowledge itself will be a salable commodity like food and oil. Knowledge itself is to become the wealth of nations”.

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论文链接：https://link.springer.com/chapter/10.1007/978-3-031-40286-9_33