Explore the concept of antisocial behavior in online communities through this Stanford University seminar. Delve into the factors that contribute to trolling and negative interactions on the internet. Discover how ordinary people can become trolls due to situational factors like mood, time of day, and discussion context. Learn about data mining and crowdsourcing techniques used to identify principles of online behavior. Examine the impact of downvoting and community bias on user interactions. Gain insights into predicting cascade growth and structure in online discussions. Understand how holistic approaches and multi-method analyses can be used to build healthier social systems and improve online communities.
Overview
Syllabus
Introduction.
Understanding bad behavior helps us build healthier communities.
Antisocial behavior is largely due to ordinary people.
Data Mining + Crowdsourcing.
Identifying principles of online behavior.
CAN ANYONE BECOME A TROLL?.
What is trolling?.
Trolling is behavior that occurs outside community norms..
What if antisocial behavior is situational?.
Challenge: how to show that antisocial behavior is situational?.
Anyone can become a troll.
Initial seed posts in the negative context condition perceived worse.
How did trolling differ across conditions?.
Bad mood and negative discussion context increase trolling.
Can trolling, like mood, vary with the time of day and day of week?.
Mood spills over from prior discussions.
The initial post affects subsequent trolling.
Because trolling is situational, ordinary people can end up trolling.
Downvoting causes negative behavior to worsen.
Challenge: how to compare different users and posts?.
but doesn't change after a positive evaluation.
How does community bias change after an evaluation?.
Cascade growth is predictable.
Cascade structure is predictable.
Holistic approaches for analyzing and building social systems.
Multi-methods analyses identify patterns in data, verify hypotheses, make predictions, and inform the design of better social systems..
Taught by
Stanford Online
