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1. WeSSRD: A weakly supervised app store spam reviews detection framework | |||
LI Siyi,XU Guosheng,LIN Yan,Guo yanhui,Xu Guoai | |||
Computer Science and Technology 23 February 2022 | |||
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Abstract:With the popularity of smartphones, a large number of apps have emerged in app store for users to download.Most app stores allow users who download an app to post reviews and ratings on this app. These reviews are not only a major factor in determining the ranking of an app, but also a major reference for users in choosing whether to download the app, and an important way for developers to get feedback from users.However, a large number of meaningless reviews (or called spam reviews) have severely damaged the normal ecology of the app store and are one of the urgent problems to be solved in maintaining the regular order of the mobile app market. This paper proposes a weakly supervised spam detection framework called WeSSRD. It can mine app reviews for relevance to the app itself by unsupervised topic modeling methods and then train a weakly supervised detector to detect spam in application stores using a minimal amount of prior knowledge.We tested this framework on a real dataset with 14,052 reviews. The detector trained by our proposed framework can achieve a precision rate of 80.97% and a recall rate of 81.89% on the test set, far exceeding the detection method based on similarity. | |||
TO cite this article:LI Siyi,XU Guosheng,LIN Yan, et al. WeSSRD: A weakly supervised app store spam reviews detection framework[OL].[23 February 2022] http://en.paper.edu.cn/en_releasepaper/content/4756424 |
2. A Topic Detection Algorithm Based on Multiple Strategies for Group Chat | |||
Wu Xu,Chen Chunxu | |||
Computer Science and Technology 22 February 2021 | |||
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Abstract:There are a large number of group chat messages on the Internet, and public opinion analysis requires topic detection to aggregate messages with similar discussions. Group chat topics are easy to be crossing and parallel, and group chat messages also have the characteristics of sparse text features. In order to solve these two problems, this paper proposes a multi-strategy group chat topic detection technology. On the one hand, the topic sequence is constructed to solve the problem of topic crossing and parallel, on the other hand, the user, time, type and other attributes of the message are used to make up for the shortcomings of clustering that rely solely on short text features. The results of experiments conducted on three datasets derived from real group chat logs show that this method has better performance than traditional algorithms. In addition, the types of group chat messages it can process are much more than traditional methods. | |||
TO cite this article:Wu Xu,Chen Chunxu. A Topic Detection Algorithm Based on Multiple Strategies for Group Chat[OL].[22 February 2021] http://en.paper.edu.cn/en_releasepaper/content/4753717 |
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