Deep Contextualized Word Embedding for Text-based Online User Profiling to Detect Social Bots on Twitter

Abstract

Social media platforms can expose influential trends in many aspects of everyday life. However, the trends they represent can be contaminated by disinformation. Social bots are one of the significant sources of disinformation in social media. Social bots can pose serious cyber threats to society and public opinion. This research aims to develop machine learning models to detect bots based on the extracted user's profile from a Tweet's text. Online user profiles show the user's personal information, such as age, gender, education, and personality. In this work, the user's profile is constructed based on the user's online posts. This work's main contribution is three-fold: First, we aim to improve bot detection through machine learning models based on the user's personal information generated by the user's online comments. The similarity of personal information when comparing two online posts makes it difficult to differentiate a bot from a human user. However, in this research, we leverage personal information similarity among two online posts as an advantage for the new bot detection model. The new proposed model for bot detection creates user profiles based on personal information such as age, personality, gender, education from user's online posts, and introduces a machine learning model to detect social bots with high prediction accuracy based on personal information. Second, we create a new public data set that shows the user's profile for more than 6900 Twitter accounts in the Cresci 2017 [1] data set. All user's profiles are extracted from the online user's posts on Twitter. Third, for the first time, this paper uses a deep contextualized word embedding model, ELMO [2], for a social media bot detection task.