Group items tagged

An analysis shows that information flow between individuals in a social network can be 'gerrymandered' to skew perceptions of how others in the community will vote - which can alter the outcomes of elections.

"YouTube has become the place to learn how to do anything, from assembling an Ikea cabinet to making a Bluetooth connection with your earbuds. It is a font of tutorials, some very good, some meandering, some made by individuals who have become professionals at it and rake in serious sums through advertising. But many are uploaded by people who have solved something that frustrated them and want to share the answer with the world. The native language of the digital world is probably video, not text-a trend missed by the literate classes that dominated the public dialogue in the predigital era. I've noticed that many young people start their Web searches on YouTube. Besides, Google, which owns YouTube, highlights videos in its search results....Someone on YouTube has an answer. But the site has also been targeted by extremists, conspiracy theorists and reactionaries who understand its role as a gateway to information, especially for younger generations. And therein lies the dark side: YouTube makes money by keeping users on the site and showing them targeted ads. To keep them watching, it utilizes a recommendation system powered by top-of-the-line artificial intelligence (it's Google, after all). Indeed, after Google Brain, the company's AI division, took over YouTube's recommendations in 2015, there were laudatory articles on how it had significantly increased "engagement": Silicon Valley-speak for enticing you to stay on the site longer." "YouTube's algorithms will push whatever they deem engaging, and it appears they have figured out that wild claims, as well as hate speech and outrage peddling, can be particularly so."

I'm interested in learning to analyze social networks. This paper explains one approach using a small sample size. "This paper presents an approach for capturing the students' profile according to their participation in the courses. The profile is based on SP concepts and SNA metrics, motivated by the large number of data available in the learning environments. Therefore, tutors may use the profiles as a resource to assist in planning their mentoring activities to facilitate the student learning process. In their turn, the students may use this information to self-analysis (or self-assess) their participation in the group. Another contribution of this work is that in a given sample of students and tutors in a particular scenario, in which the students' profiles based on SP and SNA were presented, there was a 93% reliability rate and it showed the potential to help in tutoring. As a technical contribution it is possible to mention the construction of the prototype. In addition, the proposed architecture can be generalized and applied to other social networks."