Text-Based Detection of Unauthorized Users of Social Media Accounts

Although social media platforms can assist organizations’ progress, they also make them vulnerable to unauthorized users gaining access to their account and posting as the organization. This can have negative effects on the company’s public appearance and profit. Once attackers gain access to a social media account, they are able to post any content from that account. In this paper, we propose an author verification task in the realm of blog posts to detect and block unauthorized users based on the textual content of their unauthorized post. We use different methods to represent a document, such as word frequency and word2vec, and we train two different classifiers over these document representations. The experimental results show that regardless of the classifier the word2vec method outperforms other representations.