用于黑市推文检测的多任务学习
Multitask Learning for Blackmarket Tweet Detection
摘要：
在线社交媒体平台使世界比以往任何时候都更加紧密地联系在一起，从而使每个人都可以更容易地向各种各样的受众传播他们的内容。Twitter就是这样一个受欢迎的平台，人们在这里发布推文，将他们的信息传播给每个人。Twitter允许用户转推其他用户的推文，以便将其广播到他们的网络。一条特定的推文得到的转发次数越多，它传播的速度就越快。这就鼓励人们通过使用某些黑市服务来获得对他们内容的无机评价，从而人为地增加他们的推文的覆盖范围。在本文中，我们试图检测已张贴在这些黑市服务，以获得人为提高转推这样的推文。我们使用一个多任务学习框架来利用分类和基于回归的任务之间的软参数共享。这使我们能够有效地检测已发布到这些黑市服务的推文，在将推文分类为黑市或真实推文时，F1得分为0.89。
英文摘要：
Online social media platforms have made the world more connected than ever before, thereby making it easier for everyone to spread their content across a wide variety of audiences. Twitter is one such popular platform where people publish tweets to spread their messages to everyone. Twitter allows users to Retweet other users' tweets in order to broadcast it to their network. The more retweets a particular tweet gets, the faster it spreads. This creates incentives for people to obtain artificial growth in the reach of their tweets by using certain blackmarket services to gain inorganic appraisals for their content. In this paper, we attempt to detect such tweets that have been posted on these blackmarket services in order to gain artificially boosted retweets. We use a multitask learning framework to leverage soft parameter sharing between a classification and a regression based task on separate inputs. This allows us to effectively detect tweets that have been posted to these blackmarket services, achieving an F1-score of 0.89 when classifying tweets as blackmarket or genuine.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
