论文标题
在社交媒体上的模仿：一种识别ingenuine含量的深层神经方法
Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content
论文作者
论文摘要
模仿者在在线社交网络上（尤其是在Instagram上的内容的生产和传播）中发挥了重要作用。这些实体是邪恶的假帐户，打算通过制作类似的概况，然后通过假内容来唤起社交媒体来掩盖合法帐户，这使得很难理解哪些帖子是真正生产的。在这项研究中，我们专注于三个具有合理验证帐户的重要社区。其中，我们确定了2.2k模仿配置文件的集合，其中包含近10k生成的帖子，68K评论和90k喜欢。然后，基于配置文件特征和用户行为，我们将它们聚集到“ bot”和“ fan”的两个集合中。为了将模仿者生成的帖子与真实内容分开，我们提出了一种深层神经网络体系结构，该构建结构可测量“ profiles”和“ posts”特征，以预测内容类型：`bot-nererated''，'bot-enerated'，“粉丝生成”或“真实”内容。我们的研究阐明了这种有趣的现象，并对机器人生成的内容提供了有趣的观察，这些观察可以帮助我们了解模仿者在Instagram上假冒内容中的作用。
Impersonators are playing an important role in the production and propagation of the content on Online Social Networks, notably on Instagram. These entities are nefarious fake accounts that intend to disguise a legitimate account by making similar profiles and then striking social media by fake content, which makes it considerably harder to understand which posts are genuinely produced. In this study, we focus on three important communities with legitimate verified accounts. Among them, we identify a collection of 2.2K impersonator profiles with nearly 10k generated posts, 68K comments, and 90K likes. Then, based on profile characteristics and user behaviours, we cluster them into two collections of `bot' and `fan'. In order to separate the impersonator-generated post from genuine content, we propose a Deep Neural Network architecture that measures `profiles' and `posts' features to predict the content type: `bot-generated', 'fan-generated', or `genuine' content. Our study shed light into this interesting phenomena and provides interesting observation on bot-generated content that can help us to understand the role of impersonators in the production of fake content on Instagram.
