工商管理学院管理研究工坊 No.85

主办系：市场营销与旅游管理系

讲座题目： Predicting Popularity of Viral Content in Social Media through a Temporal-Spatial Cascade Convolutional Learning Framework

讲座时间：11月9日下午15:30

讲座地点：华侨D201会议室

主讲人：徐志轩

徐志轩讲师简介：

　　徐志轩，首都经济贸易大学工商管理学院市场营销与旅游管理系讲师，毕业于中国人民大学信息资源管理学院信息分析专业，曾获得JMS中国营销科学学术年会优秀论文奖，JMS中国营销科学博士生论坛优秀博士生论文奖，全国情报学博士论坛优秀论文奖。研究领域包括社交媒体信息传播、数字营销、零售数据挖掘、县域商业等。研究论文发表在“Mathematics”,《南开管理评论》，《管理评论》、《营销科学学报》等国内外期刊上。

讲座介绍：

　　The viral spread of online content can lead to unexpected consequences such as extreme opinions about a brand or consumers’ enthusiasm for a product. This makes the prediction of viral content’s future popularity an important problem, especially for digital marketers, as well as for managers of social platforms. It is not surprising that conventional methods, which heavily rely on either hand-crafted features or unrealistic assumptions, are insufficient in dealing with this challenging problem. Even state-of-art graph-based approaches are either inefficient to work with large-scale cascades or unable to explain what spread mechanisms are learned by the model.

　　This study presents a temporal-spatial cascade convolutional learning framework called ViralGCN, not only to address the challenges of existing approaches but also to try to provide some insights into actual mechanisms of viral spread from the perspective of artificial intelligence. We conduct experiments on the real-world dataset (i.e., to predict the retweet popularity of micro-blogs on Weibo). Compared to the existing approaches, ViralGCN possesses the following advantages: the flexible size of the input cascade graph, a coherent method for processing both structural and temporal information, and an intuitive and interpretable deep learning architecture. Moreover, the exploration of the learned features also provides valuable clues for managers to understand the elusive mechanisms of viral spread as well as to devise appropriate strategies at early stages. By using the visualization method, our approach finds that both broadcast and structural virality contribute to online content going viral; the cascade with a gradual descent or ascent-then-descent evolving pattern at the early stage is more likely to gain significant eventual popularity, and even the timing of users participating in the cascade has an effect on future popularity growth.