Understanding Inorganic Behavior on Twitter: a Machine Learning Approach for Journalistic Applications
The existence of bots on Twitter has been widely the subject of research. Documenting its presence on the platform, understanding the different categories in which they can be studied, and developing detection techniques and algorithms has been the main focus of several studies.
In recent years, the influence bots have on the attention and information dynamics in media ecosystems with heterogeneous agents has also awakened interest in both academia, journalism and civil organizations. Bridging the gap between bot detection on Twitter and the study of their influence in media ecosystems has become more relevant than ever. As many discussions of public interest start on the platform, enabling journalists and civil organizations to do a better job reporting on issues happening there is crucial for the well-being of liberal democracies where social media is becoming the default public forum.
for journalists and civil organizations to detect and better understand malicious activity on Twitter.
In this context, “malicious activity” is considered to be any attempt to mislead public opinion using inorganic behavior and exploiting Twitter’s social-network features.
2023-03-01 12:55
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This is the open repo for the project
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