Studying Twitter User Accounts: Spotting Suspicious Social Bot Behavior
Date
2020-01
Type:
Thesis
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80 p., appendices
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Authors
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Publisher
Universidad del Desarrollo. Facultad de Ingeniería
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Abstract
Using original tweets published during the first round of the 2017 Chilean
presidential elections, this work aims to study the bot behavior of Twitter users
by specific patterns retrieved from their tweets, such as the user’s metadata,
number of friends, followers, content, network, and time series. Each pattern is
studied both individually and across different subsets of users, such as the
number of tweets per account per day, newly created accounts, and so-called
simple bots. Networking and timing related features proved to be critical in bot
detection. Twitter users considered to “behave” like bots are compared with web
applications (apps) used for bot detection. This work explores the visual analysis
of groups of users with similar characteristics (clusters), suggesting that a bot
behavior can be visually detected using dimensional reduction techniques such
as Uniform Manifold Approximation and Projection (UMAP). The methodology
used in this work can be applied to identify social bot behaviors in any set of
tweets captured in a specific time frame.
Description
Project presented to the Faculty of Engineering of the Universidad del Desarrollo
to opt for the academic degree of Master in data science
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Citation
Keywords
070037S, Data Science, Social Bot, Twitter, Visualization, UMAP