Joint Post and Link-level Influence Modeling on Social Media
Joint Post and Link-level Influence Modeling on Social Media
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DOI：
10.1137/1.9781611975673.30
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Liangzhe Chen;B. Prakash
中科院分区：
文献类型：
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作者：
Liangzhe Chen;B. Prakash
微博网站，如Twitter和微博，被数十亿人用来创建和传播信息。这种活动取决于各种因素，如用户之间的友谊联系，他们的主题兴趣和他们之间的社会联系。社交干扰可以被认为是一个潜在的因素，它可能会改变用户的发布和链接行为。理解这些行为对于充分理解和利用这些平台非常重要。在这个领域，大多数先前的工作要么忽略了社会影响的影响，要么只考虑其对链接形成或后代的影响。相比之下，我们提出了PoLIM，利用简单的弱监督，一种新的模型，它联合建模的影响上的链接和后生成。我们还给出了PoLIM-FIT，一个高效的并行推理算法，可扩展到大数据集。在我们的实验中，一个大型的推文语料库，我们发现有意义的主题社区，名人，以及他们之间的影响力的强度模式。此外，我们发现有很大一部分帖子和链接是由信息引起的，当数据集中在特定事件时，这一部分会增加。我们还表明，区分和识别这些受影响的内容也贝内其他具体的定量下游任务，例如预测未来的推文和链接形成，我们在这些方面的表现明显优于最先进的技术。
Microblogging websites, like Twitter and Weibo, are used by billions of people to create and spread information. This activity depends on various factors such as the friendship links between users, their topic interests and social influence between them. Social influence can be thought of as a latent factor, that may alter users posting and linking behaviors. Making sense of these behaviors is very important for fully understanding and utilizing these platforms. Most prior work in this space either ignores the effect of social influence, or considers its effect only on link formation or post generation. In contrast, we propose PoLIM , leveraging simple weak supervision, a novel model which jointly models the effect of influence on both link and post generation. We also give PoLIM-FIT , an efficient parallel inference algorithm which scales to large datasets. In our experiments on a large tweets corpus, we detect meaningful topical communities, celebrities, as well as the influence strengths patterns among them. Further, we find that there are significant portions of posts and links that are caused by influence, and this portion increases when the data focuses on a specific event. We also show that differentiating and identifying these influenced content benefits other specific quantitative downstream tasks as well, like predicting future tweets and link formation, where we significantly outperform state-of-the-art.