  Social media platforms can expose influential trends in many aspects of
everyday life. However, the movements 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 users' profile shows 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. When comparing two online posts, the similarity of personal
information makes it difficult to differentiate a bot from a human user.
However, this research turns personal information similarity among two online
posts into 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 users' online posts and introduces a
machine learning model to detect social bots with high prediction accuracy
based on personal information. Second, create a new public data set that shows
the user's profile for more than 6900 Twitter accounts in the Cresci 2017 data
set.
